THE FUNCTION OF FREE RIDERS: TOWARD A SOLUTION TO THE PROBLEM OF COLLECTIVE ACTION
J. Scott Lewis
A Dissertation
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 2006
Committee:
Donald McQuarie and Rekha Mirchandani, Advisors
Stephen S. Chang Graduate Faculty Representative
Alfred DeMaris
Monica Longmore © 2006
J. Scott Lewis
All Rights Reserved iii
ABSTRACT
Donald McQuarie and Rekha Mirchandani, Advisors
The problem of collective action is the problem of free riders. Current theory argues that free riders are detrimental to group solidarity, and predict that free riders will be punished into compliance with cooperative group norms. Observational evidence from a variety of disciplines does not coincide with those predictions, however. Recent studies show that in many cases, 20%-
40% of individuals will free ride regardless of the frequency and severity of punishment. This treatise seeksto explain the persistence of free riders by arguing that free riders perform latent functions in groups that actually maintain or increase group cohesion in naturally forming, long term groups.
Analyzing theoretical work on the collective action problem from three disciplines-- economics, evolutionary biology, and sociology--I show how drastically different approaches to the collective action problem converge on similar predictions about the nature and causes of free riding. I then show that these theoretical paradigms share a common origin from rational action models. I discuss why the current logic of rational action models are insufficient to offer a viable solution to the free rider problem. I then move beyond the traditional rational action approach by proposing an alternative kind of rationalisty which free riders pursue. Using game theory, I demonstrate the existence and utility of this new approach, and show how this alternative rationality contributes to a solution to the free rider problem by linking it to equilibrium theory.
Equilibrium theory offers a means by which rational action models and functionalist models may be tied together in order to approach a solution to the free rider problem. I argue that iv free riders may perform functions in a group that serve to increase or maintain the solidarity of the group by tending the group toward a state of allostatic equilibrium. I argue that free riders validate or increase the status of productive group members; reduce the probability of incurrence of risk for productive group members; and increase group interdependence by driving down the group's discount parameter. Through these functions, free riders may be seen as an adaptive mechanism by which a group might tend toward an equilibrium state in a dynamic environment. v
This work is dedicated to Charles Darwin, il miglior fabbro. vi
ACKNOWLEDGMENTS
No work is the product of one individual. I am deeply indebted to the following people for their contributions to this work. Thank you to Al DeMaris and Monica Longmore for their insightful comments on the manuscript and logic of the argument. Thank you to Donald
McQuarie and Rekha Mirchandani for agreeing to advise me on this project, and for their help with the manuscript.
A special thank you is extended to Jeff Houser, whose discussions over the course of three years helped develop the arguments made herein. Additionally, a thank you to my wife, children and parents, whose sacrifices while I finished this project were doubtless greater than my own. All have had a profound impact on my life and thought. vii
TABLE OF CONTENTS
Page
CHAPTER ONE. INTRODUCTION...... 1
Introduction...... 2
The Layout...... 6
CHAPTER TWO. THE PROBLEM OF COLLECTIVE ACTION...... 13
Chapter Synopsis ...... 14
Defining the Collective Action Problem...... 14
Sociological Approaches to the Collective Action Problem...... 22
The Biology of Cooperation ...... 29
Economic Theories of Cooperation ...... ……… 32
Examples of Tolerated Free Riding in Man and Animals ...... 34
Chapter Summary ...... 40
CHAPTER THREE. PROSOCIALITY...... 42
Chapter Synopsis ...... 43
The Universal Rule ...... 43
Prosociality in Humans...... 47
Maximization Strategies ...... 54
Autopoiesis: Is the Group Like an Organism?...... 57
Chapter Summary ...... 60
CHAPTER FOUR. GAME THEORY AND EQUILIBRIUM STATES...... 62
Chapter Synopsis ...... 63
An Introduction to Game Theory and Embedded Strategies ...... 63 viii
The Prisoner’s Dilemma ...... 65
The Stag Hunt ...... 69
The Chicken Game ...... 72
Chapter Summary ...... 75
CHAPTER FIVE. A TYPOLOGY OF FREE RIDERS...... 76
Chapter Synopsis ...... 77
Making Distinctions Between Free Riders ...... 77
Passive Free Riders...... 80
Active Free Riders ...... 82
The Use of a Typology of Free Riders...... 84
Chapter Summary ...... 85
CHAPTER SIX. SOCIAL ALLOSTASIS ...... 86
Chapter Synopsis ...... 87
Flexibility and Redundancy ...... 88
Social Allostasis...... 92
The Functions of Free Riders...... 96
Chapter Summary ...... 99
CHAPTER SEVEN. AN EQUILIBRIUM MODEL OF SOCIAL ALLOSTASIS ...... 100
Chapter Synopsis ...... 101
The Framework...... 101
The Re-rise of Group Selection ...... 107
An Allostatic Equilibrium Model ...... 110
Chapter Summary ...... 113 ix
CHAPTER EIGHT. FREE RIDERS AND EMOTIONS...... 114
Chapter Synopsis ...... 115
On Positive and Negative Emotions ...... 115
How Many Emotions Are There?...... 117
Chapter Summary ...... 123
CHAPTER NINE. FREE RIDERS IN THE WELFARE SYSTEM ...... 125
Chapter Synopsis ...... 126
The Misunderstood Welfare System...... 126
The Function of Free Riders in Social Welfare Programs...... 128
Chapter Summary ...... 133
CHAPTER TEN. SOUP KITCHENS, FOOD PANTRIES AND FREE RIDERS...... 134
Chapter Synopsis ...... 135
Why Facilitate Active Free Riding ...... 135
From the Free Riders’ Point of View...... 139
Chapter Summary ...... 140
CHAPTER ELEVEN. FREE RIDERS AND PUBLIC MEDIA...... 141
Chapter Synopsis ...... 142
Group Size and Contribution Size ...... 142
Free Riding in Public Broadcasting ...... 146
Chapter Summary ...... 148
CHAPTER TWELVE. HYPOTHESES, OBJECTIONS AND CONCLUSIONS...... 150
Chapter Synopsis ...... 151
Review of the Theory...... 151 x
Testable Hypotheses ...... 153
Objections to the Theory...... 159
Conclusions, Implications and Future Research...... 164
REFERENCES ...... 168 xi
LIST OF FIGURES
Figure Page
1 Axelrod’s Prisoner’s Dilemma Matrix...... 66
2 The Stag Hunt Payoff Matrix...... 71
3 The Chicken Game Matrix...... 73 1
CHAPTER ONE
INTRODUCTION
“New facts, collected in old ways under the guidance of old theories, rarely leads to any substantial revision of thought. Facts do not ‘speak for themselves’; they are read in the light of theory”
--Stephen Jay Gould 2
Introduction
The theory that is laid out herein is a framework built around a series of reconceptualizations about fundamental social relations. The theory itself is simple, though counterintuitive. The framework is built by a synthesis of a variety of disciplines and perspectives that often are seen as incompatible or competitive. The logical flow of the arguments proceeds at times from one discipline to the other, both to establish links between the disciplines as well as to move the argument forward. I will lay out in a series of steps a new theoretical approach to an old problem.
The core of the conclusion rests on the reconsideration of a number of foundational assumptions that have driven research in the collective action problem in several disciplines. I argue that the core of the collective action problem lies in the way in which current approaches conceptualize the problem, and the inadequacies of their internal logics. Briefly, the collective action problem is how to explain the emergence and maintenance of cooperation given the presumed rational self interest of the individual actors. Much of the empirical work has concentrated on the maintenance of cooperation, specifically the issue of free riders. Free riders are defined as those self interested individuals who receive the benefits of group membership without a corresponding contribution to the group. Current theories converge on the single conclusion that free riders are harmful to the long term solidarity of the group, and must be curbed. Usually, curbing free riders is talked about in terms of how to force free riding individuals to comply with the collective will through the use of sanctions or other forms of coercion. Unfortunately, in every case, the predictions made from such theorizing fall far short of the observed reality of group life. Free riders can be found in nearly every group scenario, and many individuals free ride regardless of the frequency and severity of punishment. 3
In fact, I argue that it is impossible to solve the free rider problem using the existing paradigms. Specifically, in each case, the theories are based on an understanding of social life that forces the theories into the trap of methodological individualism, thereby rendering the problem unsolvable. Within sociology, the paragon of methodological individualism is the variety of rational action theories that have emerged. But sociological rational action models are not the only examples of methodological individualism. Economics, which may largely be credited with giving rise to rational action theories; and the evolutionary biological paradigm, are similarly guilty. Microeconomics centers its theoretical developments on exchange relations between two or more individuals. Rarely do microeconomists view the relations of the exchange itself as a unit of analysis. Rather, it operates under the notion that each individual in action constitutes a unit of analysis. The extent of social understanding is a recognition that exchange requires at least two actors, though the process of exchange is largely ignored. Biological theories rooted in strict Darwinism see the individual unit as the product of evolutionary forces.
Adaptive reproductive fitness is seen as applying only to the individual organism, not a social group. While not every Darwinist adheres to these strict standards (and indeed it is questionable if Darwin himself would have held them), paradigms suggesting alternative units of analysis, such as Dawkins’ selfish genes or Wynne-Edwards’ group selection theory, have remained marginal.
Rational action theories form the cornerstone of collective action theorizing in all three of the disciplines just mentioned. As I shall show, despite their very different approaches to the problem of collective action, all three paradigms, using rational action models as their beginning point, converge on the same essential predictions about free riders. These predictions, despite the currency that they are given, are quite often not substantiated. 4
This gap between the predictions made by these disciplines and the observational and experimental evidence needs to be explained. I argue that such an explanation is impossible within the tradition of rational action theory. In this treatise, I will construct a theory that explains this disparity between the predictions and the observable facts, and offers a new way of approaching the free rider problem.
While not abandoning the rational action paradigm entirely, I argue for a radical reformation of the program that frees it from the constraints of methodological individualism. I propose to transcend the individualistic nature of rational action models, which are forced into a singular vision of logical action, and to demonstrate the utility of a vision of rationality embedded within the social relationships of the individual actors. That is, it is the relationships between actors, rather than the actors themselves, that is of primary importance.
My plan of attack is first to review the collective action problem as it is understood by the dominant paradigm. I argue, however, that the questions that emerge from the current logic would not generate sufficient answers to solve the free rider problem. My revision of the problem contains three major steps. First, I argue that there exists at least one alternative definition of rationality that is equally plausible and equally rational that is complimentary to, rather than contradictory to, the dominant position. This distinction between profit maximization
(the traditional definition of rational action) and probability maximization (the alternative complimentary definition of rational action) allows us to move beyond the constraints of methodological individualism through an understanding of varying social strategies. In other words, while I do not dispute the assertion of rational action theories that individuals act in their own rational self interest, I do disagree with their unitary measurement of rationality. It is this that forms the crux of the problem, for if every individual is acting in their own interest, and their 5 understandings of rational are the same, there can indeed be no solution to the problem of collective action. However, if there exists an alternative social strategy that is equally rational to the traditional conception, it is possible to move toward a solution.
This brings us to the second step in the argument, that of embedded rationality. I argue that while profit maximization and probability maximization strategies are equally self interested and equally rational, they are not competitive strategies. Neither can they be understood as singular units. Rather, both forms of rationality must be considered in light of the other, for the success of each is embedded in the success of the other form. This notion of embedded rationality seems counterintuitive to our understanding of rational self interest, and I therefore spend a considerable portion of the treatise demonstrating its existence and utility for solving the problem at hand.
Finally, I show how this radical revision of rational theory can move us toward a solution to the problem of free riders. Essentially, I argue that free riders form a significant part of the probability maximization strategy, embedded in a larger whole of profit maximization strategies.
One last radical alteration makes the solution complete. I simply revise the questions that frame the collective action problem. Specifically, I change the question of “how do we ensure compliance of free riders?” to “What are the functions that free riders serve in the maintenance of the group, and how do those functions facilitate their tolerance by other group members.”
Admittedly, this is a radical reversal of the question, and it leads us down a path of functional analysis. But I show that this revision is no less warranted and no less logical than the more traditional questions. It is merely a counterintuitive approach to the same fundamental issues. Stating the question in this way opens a new avenue of inquiry into the problem, an inquiry that steps across the boundaries of many disciplines. Rather than see these disciplines as 6 competing for an answer, I view them as complimentary--each contributing an important piece of the puzzle. I will draw on sociology, psychology, evolutionary biology, economics, neurology, anthropology, physiology, behavioral ethology, philosophy, and game theory to make the following case.
I argue that free riders are often tolerated in natural groups and aggregations because they perform functions that actually increase the solidarity of the group. I argue that free riders increase the status of high status group members; reduce the degree of individual risk to high status group members; and drive down the discount parameter, thus making the future more uncertain and increasing group interdependence. Free riders serve to increase the long term solidarity and survival of the group by acting as a mechanism by which the group may adapt to changing environmental pressures. Rather than see free riders are merely deviant individuals acting in self interest, I conceptualize free riding as an important social strategy that aids in group survival.
The Layout
For convenience of discussion, I have divided this treatise into three sections. The first section, the most extensive, establishes the framework of the reconceptualization that forms the keystone of the new theory. Chapter two reviews the collective action problem and shows how research from three very different disciplines—sociology, economics, and evolutionary biology—converge on the same essential predictions about free riders. Chapter two also points out the problem with these methodological individualist approaches as it pertains to the collective action problem: there is an inconsistency between the predictions made and the observable evidence about free riders. An extensive, though not exhaustive, list is offered as examples of tolerated free riders in both the animal and human world. Animal examples are used 7 to emphasize two important points. First, to challenge the notion that free riding must be a conscious strategy. Although commonly implied in the definition of free riding, there is no sound reason why such a criterion must exist; and indeed I show that consciousness is not a necessary component for the social strategy of free riding by examples from animals that I believe most would agree lack a sufficient degree of consciousness to psychically choose such a strategy. Nor can we assert that consciousness is a necessary component of tolerating free riders, largely for the same reasons why free riding itself does not require consciousness. The second point of emphasis is implied by the first—that the social strategy of free riding is a natural, rather than a contrived strategy. This implies not only that consciousness is not a prerequisite, but also that the social strategy of free riding has deep evolutionary roots, and is therefore amenable to Darwinian analysis. Specifically, we can ask such questions as “Is free riding adaptive?”
Chapter three probes the roots of the rational action perspective from its Hobbesian beginnings to its present dilemma. I argue for an abandonment of Hobbesian essentialism to be replaced by an inherently social understanding of rational action that emerges from a Darwinian perspective. In other words, I show that, contrary to rational action models, rationality is not unidimensional behavior, but rather depends upon behavior that is embedded in the social strategies of conspecifics, and the constraints of the immediate environment. Individuals within a group are able to change social strategies toward optimality as endogenous and exogenous constraints change. Similarly, I argue the controversial stance that a group may function as would an organism, and alter its strategy amid a dynamic environment much as an organism would. In this way, group survival is optimized as the group tends toward an equilibrium state according to environmental circumstances. 8
I make a point in this chapter of further stretching traditional rational action paradigms by arguing for two distinct, complimentary forms of rational calculation. I first define rationality as that which maximizes the individual’s gain at the lowest possible cost. This traditional conception of rationality I term profit maximization, since the goal is to maximize the payout that is consequential from the behavior. Profit maximization is then distinguished from probability maximization, which is defined as an equally rational strategy of maximizing the probability that the individual will get any payout, however small. Distinction of these social strategies emphasizes the point that free riding cannot be seen merely as a unitary behavior of self interest. Furthermore, it begins to clarify the reasons why free riders are often tolerated in groups. Resource and tolerance thresholds will vary with, inter alia, social strategy and position within the social hierarchy. This lays the groundwork for a continuing discussion of the notion of embedded rationality and its connection to free riders.
Chapter four continues the discussion of embedded rationality by examining how non- cooperative game theory, which is usually seen as methodologically individualistic in nature
(and therefore contrary to my general approach) actually reinforces the notion of embedded rationality. I argue that the seemingly competitive strategies that are evident in non-cooperative game theory are actually mutually reinforcing strategies that are embedded in each other strategy. I show how particular games illustrate both profit maximization and probability maximization and their embeddedness with one another. Such embeddedness, I argue, tends toward a state of equilibrium in which the strategies of all players are optimized.
Having thus laid the groundwork for a radical reconceptualization of rational action models, chapter five begins to apply this revision to free riders. Specifically, I argue that at least four types of free riders exist. Borrowing from Trivers (1971) classic paper, I make the first 9 distinction based upon the nature of the exchange. Additionally, I further divide free riding to account for differing motives in the social realm. I create a four category typology that shows different kinds of free riders, and that makes predictions about their relative place in the hierarchy of the group. This further clarifies the arguments made earlier about resource and tolerance thresholds as a means by which toleration of free riders may be justified. I furthermore suggest that the typology of free riders offered herein will in part account for much of the discrepant findings that exist in the current research.
Part two of the treatise begins with chapter six. In this section, I define homeostasis and allostasis, and discuss their relevance to free riders. Specifically, I argue that natural groups and aggregations mimic individual tendencies toward a state of allostatic equilibrium. I illustrate allostasis and its relation to the environment by using animal examples, and analogous human examples. An allostatic equilibrium state is achieved by the frequency dependence of free riders in the group, within any given set of constraints, to allow the group qua group to adapt to dynamic environments. This discussion sets the stage for the formal model offered in chapter seven.
Chapter seven ties together the framework discussed in the first six chapters, and proposes a mathematical model to demonstrate that free riders and cooperative group members can exist simultaneously in a state of allostatic equilibrium. Adapted from a model by Giraldeau and Livoreil (1998), the model illustrates the frequency dependent nature of free riders. The discussion continues with a propaedeutic about redundancy and its relation to free riders. I argue that redundant members and free riders in a group provide balance that mimics individual genotypic and phenotypic success that maintains the group at the equilibrium point. In this 10 chapter, I propose that free riders perform at least three possible latent functions in the group that increase solidarity, and which are consequential from a state of equilibrium.
In chapter eight, I introduce the role of emotions in the maintenance of free riders. There is a vast literature on the role of emotions in the creation and maintenance of solidarity, but little hypothesizing about the relation of emotions to free ridership beyond the prediction that free riders should induce negative emotions. I attack this conception first by arguing that the terms negative and positive emotions are misleading and theoretically unjustified. Alternatively, I suggest that emotions be viewed as an adaptive mechanism that provided individuals with a primitive valencing system for organismic senses. I alter the distinction between emotions to hedonic and aversive, rather than positive or negative. I argue that a large part of the functions of emotions in a social context is to tend the group toward an equilibrium state by facilitating proper valencing of environmental dynamics and the initiation of appropriate behavior, both on the individual and group level.
There are generally thirteen emotions linked to cooperation. I review eight of the ones that I believe are most relevant to the construction of the theory. I place the chapter on emotions here because, while it bears little specific relevance to the frequency dependence model constructed in chapter seven, the discussion of emotions provides an illustration of the frequency dependent nature of free riders more generally, and the emotions that are linked to them.
Inclusion of emotions into a model of frequency dependence adds additional understanding to the nature of the relationships between free riders and their cooperative counterparts. Finally, the chapter is placed immediately in front of the final section of the work because it bears relation to the discussions that follow. 11
In chapters nine, ten, and eleven, I apply the theoretical framework I have constructed to three real world scenarios. I show how free riders operate as a balancing mechanism within the social welfare system to both ensure livelihood and maintain the existence of the welfare state as a whole. The American welfare state is a system of tolerated free riders. I show how the facilitation of free riders increases the solidarity of the society through status validation, reduction of risk, and increasing interdependence.
Soup kitchens, shelters and other charitable organizations exist solely for the maintenance of free riders. I show how the maintenance of free riders facilitates the continued existence of the organizations through risk reduction, status validation, and increasing interdependence. Unlike the previous discussion of free riders in the welfare system, I include a section on the existence of soup kitchens from the free riders point of view. This is but one small illustration of the utility of the theory for both macro and micro analysis.
Chapter eleven considers free riders in public radio and television. Once again I point out the three functions of free riders as they pertain to the maintenance and solidarity of the larger social whole. I argue that tolerated free riders are an essential part of the continued existence of public broadcasting.
I argue that the theory outlined here is equally applicable to primary and secondary groups, and to groups of varying degrees of solidarity and normative evolution. Free riders appear in many types of groups, perform the same essential functions, and have the same essential consequences. This shows the broad utility of the theory in explaining a variety of situations in which free riding is tolerated.
The final chapter offers a brief reiteration of the main points of the theory. Specifically, I reaffirm the three functions that tolerated free riders may play in a group. Free riders may 12 increase the status of high status group members, reduce the risk of productive group members, and increase group interdependence by driving down the discount parameter. Similarly, I restate the typology of free riders, and the consequences that are predicted to emerge as a result of the distinctions. After reiterating the basics of the theory, I generate a series of testable hypotheses that emerge from the theory and the rationale that drives it. I make brief suggestions about the ways in which each hypothesis might be tested. The next section retraces numerous objections to the theory. Many of these are scattered throughout the varying chapters, and addressed in those chapters. In this section, however, I concentrate only on the objections and my answers to them.
Doubtless other objections will arise, but will have to be dealt with in a further disquisition. 13
CHAPTER TWO
THE PROBLEM OF COLLECTIVE ACTION
“Be really certain before you ever pronounce something to be the norm, because at that instant, you have now made it supremely difficult to ever again look at an exception to that supposed norm and to see it objectively.”
--Robert Sapolsky 14
Chapter Synopsis
This chapter sets the stage for development of a new theory of free riders by articulating the problem. I will review the collective action problem in general; and the free rider problem more specifically. I offer a very brief review of the trends of major relevant works in each of three distinct disciplines—sociology, biology, and economics. I will show how, despite their very different approaches to the collective action problem, all three of these disciplines converge upon the same essential prediction about free riders, namely that free riders are detrimental to the group and should be punished into compliance with group norms. I then argue that the prediction made by each of these theories is inconsistent with much of the observable evidence. I present a short list of animal and human examples of tolerated free riding, and discuss recent evidence that suggests that a significant proportion of free riding individuals continue to free ride regardless of the severity and frequency of punishment. Thus, the problem is manifest.
Defining the Collective Action Problem
The defining characteristic of a collective action problem is that rational egoists are unlikely to succeed in cooperating to promote common interests (Taylor 1987). Hechter (1987) defines the problem similarly, noting that it is unclear how cooperation can originate and be maintained given the self interested nature of human beings. The problem was most articulated by Hardin (1968) as the tragedy of the commons.
Hardin imagined a pasture that is open to everyone. The herdsmen in the vicinity keep their flocks in the common pasture. Since the herdsmen are in constant competition with one another, they each seek to maximize their own gain. As long as the total number of animals from all herds is lower than the carrying capacity of the pasture, a herdsman can add an animal to his herd without affecting the amount of grazing of the remainder of the animals. However, should 15 the herdsman decide to add to his flock such that the pasture is overgrazed, the tragedy of the commons ensues. The herdsman will receive some gain from adding an animal to his flock. He will also entail some cost. He may gain from the addition of meat or milk, but will lose because the yield of the entire herd is reduced due to overgrazing. Moreover, while the gain is solely to the herdsman who adds an animal, the cost is borne by all of his competitors as well. Therefore, argues Hardin, the gain of adding to the herd is greater than the loss entailed. It is to the herdsman’s advantage, then, to add to his herd. He finds, however, that the other herdsmen have concluded similarly, and have added to their flock as well. The result is a spiraling decline in utility of the pasture for all individuals.
It is in every individual’s interest to act in a selfish manner; but the result of everyone acting selfishly is a state of affairs in which every individual is less well off than they would have been had everyone restrained and operated for the common good (Kormorita and Parks
1995). The collective action problem seeks to discover under what conditions the herdsmen will cooperate to ensure that overgrazing does not occur. More broadly, under what conditions will self-interested individuals cooperate for the common good?
Certainly, not all competitive situations are a collective action problem (Taylor 1987). In order for a collective action problem to exist, the good produced must be non-excludable. Non- excludability exists when it is impossible to prevent individual members of the group from possessing or consuming the good; or when the cost of prevention is prohibitive (Taylor 1987).
Taylor goes on to suggest that a collective action problem requires the good to be divisible. That is, the good can be divided such that when any unit is appropriated by an individual, the unit is subsequently unavailable to other individuals. Indeed, these criteria aptly mimic Hardin’s tragedy of the commons. However, there do appear to exceptions to the criteria of divisibility. 16
Specifically, free riders—individuals who receive advantages from membership in a group, but who contribute little or nothing to the group in return—may free ride from some indivisible goods as well. For example, many people who listen to public radio do not pledge money to the station. They are, by definition, free riding. Yet, the good is indivisible. That is, when a free rider listens to a broadcast, there is no subsequent reduction in the number of people who may listen to the same broadcast. Nor is there a reduction in the quality of the broadcast (Brunner 1998).
Similarly, members of a church congregation who fail to contribute to the church do not reduce the degree of religious experience for the remainder of the congregation. In most cases, failure to contribute to church funds does not reduce the ability of the church to serve other congregation members. Ostensibly, free riding is not a problem in such cases. However, goods that are non- divisible may disappear altogether if the ratio of free riders to contributors gets too high.
Consider again our example of public broadcasting. The stations rely on individual pledges for much of their revenue. When revenue is sufficient to meet operating expenses, free riders pose no problem. However, if everyone were to free ride—that is, listen to broadcasts without contributing—the station would not receive its needed revenue, and would be forced to stop broadcasting, which affects all of the listeners.
The collective action problem is not expressly about inducing or maintaining cooperation.
There are, indeed, many plausible ways to explain cooperation in the human species
(Hammerstein 2003). The collective action problem is, in essence, the problem of free riders.
Individuals who benefit from the welfare of the group without contributing to that welfare pose a problem. In Hardin’s tragedy of the commons, how can the remaining herdsmen prevent the others from adding to their flock, gaining at the expense of the group as a whole? More 17 specifically, the problem of collective action lies in the predictions that current theories make about the degree and nature of free riding.
Free riding is presumed to be the self interested choice when the group produces a common good (Kim and Walker 1984). The benefits of free riding are presumed to outweigh the costs. While empirical research shows a large disparity in the degree of free riding (Hechter
1987; Andreoni 1988), most studies do show at least some free riding behavior (Isaac, Walker and Thomas 1984; Isaac, McCue and Plott 1985; Isaac and Walker 1988; Argyle 1991; Isaac,
Walker and Williams 1994; Brunner 1998). Yet in each case, free riding is less common than cooperation among members of a group. Furthermore, despite consistent predictions that punishment offers an effective deterrent to free riding behavior, free riding persists.
For these reasons, among others, the problem of collective action has posed a problem for social scientists in a variety of disciplines. Most early thinking in psychology and related social sciences has assumed that all behavior originates from internal motivations, and is learned by later reinforcement (Argyle 1991). Drawing on economic models, it was generally supposed that actions are based fundamentally on estimates of costs and benefits (Homans 1961; Argyle 1991).
Yet, as Durkheim (1933) and later Parsons (1951) point out, society cannot be composed merely of individuals pursuing their own self interest. Such a system would not be stable, and human life would reduce to the conditions of Hobbes’ 17th century world: “solitary, poor, nasty, brutish, and short.” (Hobbes 1651: 107). For Durkheim, society was a set of social facts, values, beliefs, and norms that motivated individual adherence to them. In other words, individual conduct was not motivated by rational self interest, but rather by commitment of individuals to the values and norms of the group, which may have non-rational origins (Sanderson 2001). Pareto (1920) similarly rejects the idea that rationality is a significant force in society. Later, Parsons would 18 integrate these ideas into a functionalist framework, arguing that norms that are internalized are embedded within roles performing particular functions for the group. These roles, like the norms and values that are embedded within them, are emergent properties of the group that are not reducible to individual action. This has become the dominant line of thinking in sociology: social structures come first, and individuals are socialized into them (Sanderson 2001).
Drawing on both behavioral psychology and economics, Homans attempted to revive the homo economicus model being replaced by Parsons (Sanderson 2001). Homans objected to
Parsonian explanations as infelicitious. Homans insisted that such models can give only proximate explanations of individual behavior—explanations that are essentially meaningless.
To truly understand the nature of groups, Homans argued that ultimate explanations must be explored. These could be understood only through understanding the nature of exchange relationships between individuals acting in their own best interest.
Building on Homans’ early foundation, later scholars such as Coleman (1990) and
Hechter (1987) constructed a more detailed explanation of rational action. Rooted squarely in the
Hobbesian tradition, rational choice models begin with a series of assumptions that underscore the approach’s atomistic tendencies. First, rational action theories assume that individuals are purposive, and act in accordance with a hierarchy of preferences. Preferences are evaluated both on the reward and cost of achieving that preference. In addition to the simple cost-benefit calculations, preferences are also evaluated in terms of constraints. Constraints may be environmental, such as scarcity of resources. An individual may also face opportunity constraints, foregoing certain desirable goals or actions for the achievement of another. There are also institutional constraints, which weigh as positive or negative sanctions toward any course of action. Rational action models also must assume that individuals are in possession of some 19 amount of information about the benefits and costs of each action. Complete information is not necessary, but the approach assumes some degree of knowledge that is sufficient to make a choice (Friedman and Hechter 1988).
What is rational for one individual may not be rational for another. Each actor operates with a subjective sense of what is in his own self interest. Relatedly, what is determined to be individually rational may not be rational for the group (Sanderson 2001). This has led to misplaced criticisms of reductionism (Homans 1984; Coleman 1990). The methodological reductionism of rational action theory represents, according to Hechter (1983a: 8) a “structural, rather than atomistic kind of individualism.” That is, the structure sets the constraints that limit the way in which individuals may act in pursuit of their desires. However, such constraints are insufficient to determine the behavior of an individual.
Though a marginal perspective for most of its history, rational action models have been quite influential, particularly in examining the problem of collective action. Rather than the top- down analysis characteristic of Durkheimian perspectives, rational action models favor internal analysis of systemic behavior. Coleman (1990) gives several reasons for favoring bottom-up analysis. Among them, Coleman notes that systemic theories begin at the level of the social structures, which are emergent properties from individual social action. From a structural perspective, individual actions, which form the component parts of structures, give rise to structures that are not reducible to the component parts. Coleman notes that these emergent phenomena are often not intended or predicted by the individuals comprising the component parts.
Weissman (2000: 11) adds to this point by noting that “relations are not merely additive or aggregative.” There are, Weissman claims, issues with inferring the effects and consequences 20 of individuals from the effects and consequences of systems. Weissman illustrates this point ably by pointing out that such perspectives suffer from an infinite regress of ontology. Specifically, structural forces are hypothesized to socialize individuals into the available framework. Yet, the structure is itself composed of individuals. In order for the structure to exist, it must socialize individuals within the framework of that structure, which cannot happen unless there is an extant structure. The result is a seemingly unsolvable regression into the origins of both individual and structure. Weissman also notes that while the actions and relationships of component parts are hypothetically infinite, the actions and relationships of structures must be limited to the boundaries set by the emergent properties of the constituents.
The common solution for social scientists has been social constructivism, a version of conceptual idealism that holds the human mind, influenced by social interaction, as the prime determinate of our concepts of reality (Trigg 1980). However, social constructivism is itself plagued by the same problem. Trigg notes that extreme subjectivism—denying the independent reality of anything beyond my judgments (whether held as social constructs or individual constructs)—rules out any ontology. “It may say that only I exist, but there is even here a realist whiff to the claim. Am I saying that I exist independently of and prior to my judgments that I do?” (22-23). Trigg concludes that “the systems each describe the world and respond to its changes. They do not influence the world or determine what happens” (107-108).
Sociologist Stanislav Andreski (1972) solves the problem by concluding that “every empirically observable entity is more than the sum of its parts because to be observable an entity must consist of parts standing in certain relationships to one another” (183). Andreski notes that, while structural influences exist, there is no way in which we can definitively argue that the structure observed influences the relations of the parts whose specific relations constitute the 21 cause of the emerging structure. Andreski correctly points out that it is similarly useless to speak of an individual, since such a thing does not exist. Rather, there are many individuals existing in particular relation to one another, and with the relations constituting emerging structures.
Ontologically, there appears to be no way to advocate for structures giving rise to the relations that create the structures.
Irrespective of their philosophical failings, structural models have remained the dominant paradigm in sociology. However, they have contributed little of value to the free rider problem.
Rational action theories have fared somewhat better, offering panoply of explanations for human cooperation. Yet, despite considerable development of the problem, no satisfactory explanation has surfaced. Rational action models are roughly consistent with economic and biological models in the assumption that individuals act in a self-interested manner (Richerson, Boyd and
Henrich 2003). Nevertheless, rational action models are inadequate to address collective action problems, especially the free rider problem. Indeed, if rational self-interest is assumed, the free rider problem becomes of critical concern. Free riding is a self-interested strategy, wholly egoistic. Yet, it remains considerably less common than cooperation. As early as 1975, Kuhlman and Marshello reported a significant degree of free riding behavior despite the presence of sanctions. More recently, Fischbacher, Gächter and Fehr (2001) and McElreath et al. (2003) report consistently that free riding generally comprises 20%-40% of group membership.
Several interesting solutions have been proposed from rational action models. These may be catalogued into several categories. As noted, some rational action models are congruent with biological and economic models, in that all assume egoism as a founding premise—though for quite different reasons. Sociological theorizing of collective action takes at least two broad forms
(Markovsky and Lawler 1994). Utilitarian theories argue generally that social order is created 22 and maintained because interdependence makes cooperation a valued commodity. Affective theories argue generally that social order is created and maintained by the emotional ties of individuals to groups (Lawler 1992; Lawler 2001). Affective theories have gradually and naturally developed away from collective action problems per se, and toward network and exchange perspectives of solidarity. The like remains strong to economic and biological undercurrents, however, with a continued reliance on the basic assumptions of rational action models.
Utilitarian theories have maintained an even stronger link to biological and economic approaches to the collective action problem. Both the core assumptions and the direction of research overlap quite heavily, often with widely disparate results. The results complicate the picture, since they cannot be readily explained with extant theories.
Sociological Approaches to the Collective Action Problem
In many ways, sociological theorizing about the collective action problem is similar to economic approaches. This is perhaps because both disciplines share a common root. The work of Mancur Olson (1965), an economist, has had far reaching influence. Olson couched his economic theory in emphasis on the group, rather than the individual. While understanding the logic of rational action models, Olson ultimately rejects the idea of group rationality, in favor of an emergent nature of groups.
But it is not in fat true that the idea that groups will act in their self-interest follows logically from the premise of rational and self- interested behavior. It does not follow, because all of the individuals in a group would gain only if they achieved their group objective, that they would act to achieve that objective, even if they were all rational and self-interested. Indeed, unless the number of individuals in a group is quite small, or unless there is coercion or some other special device to make individuals act in their common interest, rational, self interested individuals will not act to achieve their common interest. (2, italics original). 23
In this way, he bridges the gap between Homans and Parsons, and offers a means by which exchange can be seen as a group phenomenon, instead of an individual one.
Olson provides perhaps the most comprehensive discussion of free riders in the literature to date. Olson states his argument in three axioms. The first axiom states that the larger the group, the smaller each individual’s net benefit from the public good. Secondly, the larger the group, the less likelihood there is that any individual members of the group will unilaterally contribute to the group. Finally, the larger the group, the greater the cost of providing the public good there is.
Olson argues that free riders threaten the cohesive nature of the group by increasing the cost of providing a common good, and reaping the benefits of the common good without contribution to it. Acquiring public resources with no obligation or contribution is individually desirable. Yet, if everyone in the group pursues this course of action, how is group life to be organized and maintained? If, as Fessler and Haley (2003) suggest, a barrier to cooperation is the impulse to defect, how can social scientists—and in particular rational actions theorists—account for the prevalence of human prosocial behavior?
According to Olson, individuals must be enticed into the group and held by the force of normative regulation and fear of punishment (Sandler 2001). Coleman (1990) extends this idea by noting that extensive free riding causes low group solidarity. Free riding causes the group, or the individuals that comprise it, to face harmful consequences. Thus, according to Coleman, it is rational for the group, qua group, to act in such a way that reduces free riding. However, unlike
Olson, Coleman argues that positive sanctions can achieve this goal by initiating a cycle of increasing conformity to the group. Hechter (1987) and Willer, Borch and Willer (2003) disagree. They argue that suppressing free riders with payment may be seen as rewarding 24 deviance, and thus increase the degree to which free riding occurs. It is best, they argue, to deal with free riders using negative sanctions.
Taylor (1987) addresses this issue and provides apt criticism to these solutions to the collective action problem. Taylor argues that these analyses are entirely static, assuming that all individual actors will make one choice, and remain with the choice henceforth. In the real world,
Taylor argues, public goods interactions are dynamic; individuals often change their minds about what they will desire and how they will go about achieving that which they desire. He also notes that these theories exclude altruistic motivations, assuming that if the rational action is self interest, then altruism is anomalous. Finally, Taylor argues that sanctions toward free riding individuals do not solve the public goods problem, since such solutions imply second, and subsequent order free riding.
From these roots, sociological theories dealing with the collective action problem have swayed away from the collective action problem per se, and toward discussions of solidarity.
Sociological theories of solidarity generally take on at least two forms (Markovsky and Lawler
1994). Utilitarian theories of solidarity begin with the assumption that social order is created and maintained because interdependence makes cooperation a valued commodity. Affective theories of solidarity begin with the assumption that social order is created and maintained by the emotional ties of individuals to groups.
There is a large and diverse literature on the emotional component of solidarity (e.g.
Parsons 1951; Fireman and Gamson 1979; Scheff 1990; Lawler 1992; Lawler 2001). Emotional theories of solidarity presume that positive emotions act as forces to reciprocally bond individuals into stronger relationships, while negative emotions decrease the likelihood of bonding (Lawler 2001). These theories, like the others, are plagued with several problems. First, 25
Lawler, among others, defines negative emotions as those which decrease the likelihood of future interactions. Positive emotions are similarly defined as those emotions that increase the likelihood of future interactions. These definitions are inadequate, for we know whether an emotion is positive or negative only by how it makes a particular individual feel at a particular time. It is more likely that emotions are neither positive nor negative, but act contextually to aid in evaluations of stimuli as positive or negative (Dolan 2002). For instance, fear, which is typically cited by Lawler as a negative emotion, may serve a positive function if it precipitates avoidance of a dangerous situation. This will be articulated in more detail in the final chapter.
McElreath et al. (2003) identify thirteen separate emotions that are correlated with cooperative behavior. These emotions are tied to feelings of trust and reputation. For example, members of a group need to know that they can trust their conspecifics. Similarly, an individual desires to be trusted by other group members. Thus, emotions serve to enhance the trustworthiness of individuals in a group, and to gauge trust in others. There is, however, no determination of causality made. In other words, it cannot be deduced from current methodologies whether emotions are a cause or consequence of solidarity.
Utilitarian theories of solidarity may be broken down into several subcategories.
Utilitarian theories may take the form of resource theories, such as the one proposed by Hecther
(1987). Coleman (1990) proposes a normative theory of solidarity. Another distinct theoretical category is structural in nature, of the kind proposed by Markovsky and Lawler (1994) and
Markovsky (1999). Hechter (1987) defines solidarity as the product of two main factors: the extensiveness of individuals’ group obligations, and the degree to which individual members comply with those obligations. These obligations are the function of each individual’s dependence on the group. Members comply with group obligations to the degree that the group 26 has the ability to sanction non-compliance. Solidarity, according to Hechter, increases the amount of public goods that individual members have access to, thus facilitating adherence to group obligations as a condition of receipt of public goods. For Hechter, higher production and quality of public goods is associated with higher solidarity.
Although Hechter’s theory does contain elements of normative regulation, Coleman
(1990) argues that norms are central to the creation and maintenance of solidarity. For Coleman, extensive free riding decreases group solidarity. Free riding causes individuals within the group to be exposed to negative externalities. Because of these harmful consequences, it is rational for the group (qua group) to decrease free riding. In order to reduce free riding, groups create and enforce norms that induce cooperation and compliance. Unlike Hechter, who relies primarily on negative sanctions to enforce group obligations, Coleman argues that positive sanctions may also be used to increase conformity to the normative structure.
In two papers, Markovsky and Lawler (1994) and Markovsky (1999) develop a structural theory of solidarity. In this theory, social cohesion forms the central core of solidarity. Social cohesion is defined as the strength and directness of relations among members of the group.
Even with high levels of cohesion, however, groups must also exhibit unity of structure, which is the absence of actors with higher cohesion among one another than with other subsets of the group. Thus, according to Markovsky and Lawler, groups exhibit solidarity when they have a set of actors with high cohesion and high utility of structure. A decrease in solidarity is attributable to decreasing cohesion between members. Markovsky (1999) introduces a means for determining a network’s cohesion and homogeneity. Markovsky hypothesizes that greater homogeneity is correlated with higher solidarity, while lower homogeneity (greater heterogeneity) is associated with lower solidarity. 27
While each of these perspectives make important contributions to the solidarity literature, none are individually adequate. Resource theories, as Hechter seems to acknowledge, are inadequate to deal with the free rider problem, and in particular, second order free riding.
Normative theories presume that which they set out to prove. Taylor (1987: 30) notes that “it remains to be explained how the system of sanctions itself came into being and is maintained. .
.the maintenance of a system of sanctions itself constitutes or presupposes the solution of another collective action problem.” This is, in effect, no different from the criticisms made earlier to
Durkheimian normativism. At best, normative theories can offer a means by which solidarity is maintained through normative structures; but they are inadequate to explain the origins of cooperation, or to explain free riders.
There is doubt, also, about the degree to which norm enforcement actually plays a part in the maintenance of cooperation. For example, Marques (2005: 25) notes:
Reciprocity power is derived from its ability to act as a form of dissuasion for cheating in contracts and free riding in collective action agreements. This dissuasion power results from the internal dynamics of each relation and not from external sanctioning mechanisms
That is, cooperation is a consequence of monitoring within the immediate exchange group; but is not strongly influenced by larger group norms or sanctions.
Similarly, structural theories fail to explain the origins of cohesion and solidarity within the group. Structural theories suggest that high homogeneity is associated with high solidarity.
While this may be true in some cases, it is by no means completely generalizable. Despite suggestions that the emergence of cooperative behavior among humans can be explained entirely by these mechanisms, the claim, according to Bowles and Gintis (2003), is entirely false. They note that much of the experimental evidence about cooperative behavior in humans comes from 28 nonrepeated interactions, or at the end of repeated interaction. In such cases, structures cannot be implicated in the cooperative behaviors.
An attempt to synthesize these theoretical perspectives within a larger framework has been made by Willer, Borch and Willer (2002). They demonstrate that there exists a lower boundary beyond which cooperation and solidarity is zero. Specific points above the lower boundary are equilibrium points where cooperation is possible. At the points of equilibrium, group cohesion is evident. Members of the group prefer to stay in the group, rather than leave to join another group, since the benefits of remaining in the group—and maintaining the same social strategy—is Pareto optimal (Willer, Borch and Willer 2002).
The authors also offer one possible solution to the problem of second order free riding without adding higher order problems. They claim that status acts as a cultural structure that prevents second order free riding. Specifically, they argue that individuals who sanction free riders, because they further cooperation in the group, will gain higher status than those who do not punish. They claim to offer several ethnographic examples of this. However, the examples are less than satisfying. As an example, the authors quote Malinowski (1959: 36).
The positive aspect of compliance to primitive custom, the fact that obedience to rules is baited with premiums, that it is rewarded with counterservices, is as important, in my opinion, as the study of positive sanctions.
There is, in this selection, little to indicate the value of status beyond the compliance itself.
Rather than demonstrate that status is increased via punishing defectors, the quote merely indicates the desirability of cooperation as a good in and of itself.
The authors go on to note that there is a consistent relationship between high status and sanctioning norm violators. In fact, one of the examples cited by the authors seems to indicate that status occurs prior to sanctioning, which is opposite the causal direction predicted by the 29 authors! They assert that individuals will sanction first order free riders only when the cost of sanctioning is exceeded by the value of gaining status for doing so. They furthermore argue that status can operate to resolve second order free riding problems only when the status hierarchies are fluid (Willer, Borch and Willer 2002). We must recall, however, the criticisms made by
Taylor (1987) noted earlier, for they are no less relevant here.
The Biology of Cooperation
In 1971, biologist Robert Trivers published his theory of reciprocal altruism. The paper offered a possible explanation for the emergence and maintenance of cooperation in natural, non- kin groups. Building on Hamilton’s (1964) classic paper on cooperation via genetic relatedness,
Trivers expanded the theoretical scope of the argument by suggesting that non-kin cooperation is possible in a scenario where altruism is reciprocated between non-related conspecifics.
Trivers suggests that each individual possesses some degree of cooperative and cheating behavior, and chooses between them in order to maximize genetic fitness with the extant environment. Selection will discriminate against cheating if the cost of cheating outweighs the benefits. Conversely, cooperative behavior will be selected for when the long term benefits to cooperation exceed the cost of cooperating. Thus, the cheater will be selected against relative to the cooperator. In this way, propensities toward cooperative behavior may spread through a population. Individuals who cheat receive no positive reciprocation. The cost of cheating is increased with each subsequent cooperator who withholds reciprocation.
Trivers does argue, as shall I, that cheating and cooperating behavior depends largely on the social environment in which the organisms are embedded. Since environments tend to change over time, selection has favored developmental plasticity of those traits that regulate cooperating and cheating behavior (Wright 1994). 30
Trivers’ theory is enticing and mathematically consistent with available literature in population genetics. However, the theory makes several erroneous assumptions and assertions.
For example, Trivers makes assertions about the evolution of reciprocal altruism genes that in fact apply only when the gene for altruism is exactly 50% (Trivers 2002). Yet, one of the issues the paper tries to address is how to take a gene that occurs at a low frequency and show its growth to a gene of high frequency. What happens when genes are at 50% is largely irrelevant to the paper (Trivers 2002). Trivers’ theory also suggests that cheaters will be selected against such that their numbers will dwindle to a nonsignificant proportion of the population. Using data from a variety of primates, including humans, Boyd and Richerson (2003) conclude that reciprocal altruism, while possible, is unlikely to evolve in moderate or large sized groups. Indeed, supporting data has been modest (Argyle 1991; Hammerstein 2003). This is caused in part by the fact that reciprocal altruism rests on the assumption of a high percentage of other reciprocal altruists. In groups where this is not the case, reciprocal altruism rarely emerges as a dominant social strategy (Nemeth and Tákács 2005, unpublished). Furthermore, if reciprocal altruism were to gain a foodhold in a group, we would expect, ceteris paribus, to see a gradual reduction in the degree of free riding behavior. Contrary to his assertion that the cost of cheating increases with increasing population size, several sources demonstrate that the number of cheaters increases with increasing population size, although the proportion of cheaters and cooperation often stays the same (Olson 1965; Fischbaher, Gächter and Fehr 2001; McElreath et al. 2003).
Of the data that does exist in favor of Trivers, most has come from the primate world.
While the generalizability of this data has been repeatedly challenged (Smith 2003; Bowles and
Gintis 2003), it is likely that some degree of carryover to human behavior is warranted by fact of a common evolutionary history. Just as similar phylogenetic traits in species are presumed to 31 have a similar evolutionary origin, it is assumed that similar behavioral traits likewise have similar origins (Darwin 1859; Darwin 1872). Gould (1980: 32) agrees, noting that “behavior can be as ancient and as heritable has form.” Indeed, similarities in inherited behavior unite members of taxonomic phyla in exactly the same way in which bodily forms do (Lorenz 1965).
Axelrod (1984) hypothesizes similar origins to cooperation. Using computer simulations of simultaneous iterated Prisoner’s Dilemma games, Axelrod demonstrates that a reciprocal cooperative strategy can win out over other strategies under robust conditions. Axelrod pitted a group of strategies against each other, and allowed the points to accumulate for each strategy throughout the tournament. The winning strategy, known as Tit-for-Tat, cooperates on the first iteration, and subsequently copies the previous move of its opponent. Although Tit-for-Tat did poorly in each individual simulation, it had the highest cumulative score. Axelrod later expanded the scope of the simulation by introducing multiple player Prisoner’s Dilemma games, and adding background noise modeled as errors in reading the opponent’s previous move (Axelrod
1997). Tit-for-Tat did similarly well under such conditions. This is often cited as evidence for the strength of reciprocal altruism (Trivers 2002).
These studies suggest that cooperation can be an emergent property of interaction. Under conditions in which it would be in the individual’s best interest to engage in cooperative behavior, and in which there is at least one other individual willing to cooperate, it seems reasonable to assume that cooperation can develop. The scope of these results is limited, however. There has been little follow-up work done to determine the specific conditions under which cooperation can emerge in this way. Also, although it is certainly true that some human social interaction can be equated with a Prisoner’s Dilemma game (Taylor 1987; Fehr and
Gächter 2002; Fehr and Henrich 2003), many more human interactions can not be modeled in 32 this way (Argyle 1991). Clearly, a more flexible model is necessary to broaden the scope of these findings. Furthermore, studies such as these say little or nothing about the specifics of free riding behavior, beyond the mere admission of their existence.
Economic Theories of Cooperation
Fehr and Gächter (1998a; 1998b; 2000), Gintis (2000) and Henrich and Boyd (2001) have proposed an alternative to reciprocal altruism known as strong reciprocity. In strong reciprocity, individuals will willing repay gifts and punish defectors even in anonymous one-shot encounters, and even at substantial cost to themselves (Fehr and Henrich 2003). Strong reciprocators begin any interaction with the assumption that the other individual will cooperate.
In the event of a defection, however, strong reciprocators will punish the defectors, even at a considerable cost to themselves. In this way, interaction is shifted from a self-serving zero-sum scenario to a nonzero-sum game in which both individuals will either gain or lose.
Fehr and Gächter (2002) conclude that reciprocal fairness substantially contributes to the enforcement of cooperative agreements in bilateral exchanges. Reciprocal fairness provides incentives for potential free riders to behave competitively. Nevertheless, they note that there is a non-negligible fraction of subjects who always defect. Fehr and Henrich (2003) went on to test the frequency and strength of strong reciprocity in curbing free riders. Results indicate that cooperation is much higher in multiple iteration scenario than in a single iteration scenario. This suggests that strong reciprocity, which itself implies multiple iterations, may be a key part of maintaining cooperation over time. However, Fishcbacher, Gächter and Fehr (2001) found once again that roughly 20%-30% of subjects behave in a fully selfish manner and always defect, irrespective of the frequency or severity of punishment. Fehr, Fischbacher and Gächter (2002), in a similar setting, report that roughly 30% of the subjects of the study behaved in a completely 33 selfish manner, regardless of the actions of the other members of the group. Fehr and Henrich
(2003) conclude that there exist two fundamentally opposed evolutionary forces-strong reciprocators and purely selfish individuals—that exist in equilibrium, though the specifics of this equilibrium are not specified or elaborated.
The idea of strong reciprocators remains problematic. Detailed analysis of strong reciprocity has repeatedly shown that, while it can maintain short term cooperation in small groups, it is insufficient to account for long term cooperation or cooperation in larger groups
(McElreath et al. 2003; Fehr and Henrich 2003). It should also be noted that strong reciprocity, in situations where it is likely to emerge and effectively enforce cooperation, provides a proximate explanation for cooperation only. Ultimate causes cannot be explained by strong reciprocity (McElreath et al. 2003). Panchanathan and Boyd (2004) argue that it is unclear why individuals should engage in costly punishment, given the certain cost and uncertain outcome.
Additionally, Fehr and Gächter (2003) fail to include resource thresholds in their models.
In other words, their experiments fail to account for the effects of status and access to resources that are implicit in every group. It cannot be assumed that every individual within the group will have tolerance for free riding, or will react in the same way; nor can it be assumed that individuals will have the same amount of resources to devote to a strong reciprocity strategy.
Whereas high status individuals will likely have greater access to resources, lower status individuals will be less likely to afford resource expenditures toward punishing free riders, and therefore will be more likely to be tolerant of free riding. Fehr and Gächter (2003) also fail to account for possible differences in the types of goods being created or exchanged. It is well documented, such as in the Ache of Paraguay, that the degree to which individuals will tolerate noncooperation varies with the type of resource in question (Kaplan and Hill 1985). 34
Relatedly, Fehr and Gächter (2003) cannot account for second order free riding. Second order free riding occurs when individuals who would ordinarily be expected to punish renege on the expectation (Taylor 1987; Willer, Borch and Willer 2002). Noting that defection from the group’s decision to punish a free riding individual would gain roughly the same benefit without the associated cost, any individual punisher is likely himself to free ride. Second order free riding would be particularly attractive if the individual has few resources to devote to the punishment, and where that fact is coupled with the knowledge that other group members have considerable resources that may be dedicated to punishing. Taylor (1987: 30) notes that:
. . .the maintenance of a system of such sanctions itself presupposes the solution of another collective action problem. Punishing someone who does not conform to the norm—punishing someone for being a free rider on the efforts of others to provide a public good, for example—is itself a public good for the group in question, and everyone would prefer others to do this unpleasant job.
Thus, as Taylor demonstrates, the collective action problem is more about free riders than cooperation. While free riding may appear to be the rational strategy, it is far less common than cooperation, and needs to be explained within this framework.
Examples of Tolerated Free Riding in Man and Animals
All of the theories discussed herein share not only foundational assumptions about the nature of cooperation and cooperative organisms, but also share a common prediction about free riding. All of these theories suggest that free riding is detrimental to solidarity, and that free riders, at minimum, should be sanctioned into compliance (Coleman 1990; Fehr and Gächter
1998a; 2000). These predictions ought to apply to all social groups that exhibit free riding.
While numerous studies document the free rider problem (Sweeney 1973; Scherr and
Babb 1975; Alfano and Marwell 1980; Marwell and Ames 1979; Smith 1980; Kim and Walker 35
1984), few studies have been done to determine what, if any, consequences free riding has on the group as a whole. Economist Ernst Fehr, among others, has studied punishment, and free rider reaction to punishment, but has not provided data on the solidarity of the subjects as a group.
Interestingly, despite the commonality of prediction, observational research and empirical studies do not conform to the predictions. In fact, there are numerous documented cases of tolerance for, and even defense of, free riding individuals. Many of these examples come from the animal world, where, as we have already seen, the propensity for selfish behavior is both homologous and analogous to human behavior. In fact, Barash (2003: 138) notes that “biologists have yet to document cases in which real animals punish free riders.”
Primatologist Franz DeWaal (1996) documents several instances of tolerated free riders.
Among the more popular examples is Mozu, a Japanese macaque (Macaca fuscata) who was born without hands or feet, and with a demeanor that reflects a life of long suffering. Mozu is unable to contribute to social foraging, and yet maintains a connection to the social group that includes successful mating and child rearing. According to DeWaal, Mozu is well accepted by most of her group mates. He sees this as evidence that even in groups that are based on social contracts, there is room for individuals of little value. He notes that, in the case of Mozu, the cost to other group members for her inclusion is negligible. The benefits to Mozu, however, are great.
DeWaal (1996) also notes that archaeological evidence suggests that early hominids supported inidivduals who could contribute little or nothing to the community. Dettwyler (1991) concurs, noting that for groups in resource rich environments, sharing resources with less productive group members posed no problems to group survival. Such altruism would likely not be seen in environments where resources were poor. 36
Another example can be seen in dragonflies (Plathemis Lydia). In this species, strong males defend optimal breeding territories. Up to four satellite males linger on the outskirts of the territory, waiting for females to arrive at the site. Since optimal sites often attract more females than the defender can copulate with efficiently, the satellite males receive mating opportunities with minimal investment. While it is certainly in the self-interest of the dominant, defending male to drive off competitors, they are rarely seen to do so (Trivers 1985). Similar free riding has been observed in the digger bee (Centris pallida), and various species of crickets and frogs
(Trivers 1985).
Patterns of free rider tolerance are also evident in lions. Lionesses live in closely knit prides, with males attaching themselves to the pride for the purpose of reproduction and access to food. Male lions neither hunt nor defend territory. Yet, there presence is tolerated even when there are no estrus females in the pride. Even more astonishing, however, are the results of an experiment conducted by Heinsohn and Packer (1995). Lionesses studied (Panthera leo senegalensis) advertise their territory through vocalization. It is therefore easy to trick lions into believing their territory is being invaded by a rival pride simply by playing taped recordings of vocalizations. Heinsohn and Packer noticed that lionesses walked toward the sound to investigate, some more aggressively than others. Lionesses who lead the investigation incurred greater risk, while those that hung back incurred little if any risk. Heinsohn and Packer write:
We suggest that female lions may be classified according to four discrete categories. ‘unconditional cooperators’ who always lead the response, ‘unconditional laggards’ who always lag behind, ‘conditional cooperators’ who lag least when they are most needed, and ‘conditional laggards’ who lag farthest when they are most needed. (1995: 1260). 37
More importantly, there was absolutely no sign of punishment for the laggards (Heinsohn and
Packer 1995; Ridley 1996).
Van Lawick-Goodall (1971) documents predation in chimpanzees (Pan troglodytes).
Male chimps cooperatively hunt red colobus monkeys, juvenile baboons, and bushpigs, among other small animals. Hunting is engaged in my males, and the meat is often shared with unrelated conspecifics who did not engage in hunting, including females, juveniles, and other males.
Although there does appear to sharing preferences, they do not appear to be rigidly followed.
Oldfield-Box (1967) discovered that under certain conditions, rats trained in an operant situation often free ride off of other rats. For example, two rats were placed in an apparatus with one lever and one food tray. One rat did most of the work in pressing the lever, and the other rat consumed the resulting food. No repercussions were noted when the rats were caged together after the trial (Dimond 1970).
Evidence of similar phenomena is similarly replete in the human species. Brief reflection will probably recall numerous examples from everyday life. Many of the examples come from aggregations rather than groups. This is not surprising, since much of modern interaction is aggregational rather than group oriented. Some examples of non-punished free riding in aggregations include welfare recipients and non-contributing viewers of public television. The existence of soup kitchens and the preponderance of charity point to the fact that free riders are not often punished. There are also numerous examples of non-punished free riding in human groups. Of the examples that are documented, most come from gatherer-hunter societies; or societies making the transition to swidden agriculture (Kaplan and Hill 1985), in which reciprocity and sharing is more commonly studied. As opposed to the western society composed 38 of many aggregations, gatherer-hunter societies are usually tightly knit groups. I will mention only a few.
One notable example of tolerated free ridership in humans comes from the Hazda, a
Tanzanian tribe of gatherer-hunters. Hazda men set out to kill large game, from antelopes to giraffe. Most animals killed have far more meat than can be consumed by one family. Successful hunters give the remaining meat to other group members who have been unsuccessful in the hunt. Anthropologist Kristen Hawkes (1992; 1993) notes that some Hazda men are content not to hunt, but to receive gifts of meat from successful hunters. She notes that these free riders are not punished, even though they are consistently idle or incompetent hunters.
Gurven, Hill, Kaplan, Hurtado and Lyles (2000) document tolerated free riders among the Hiwi of Venezuela. After a kill, kin are called into a circle to receive a share. The authors document a number of instances where unrelated tribe members who had not participated in the hunt begged for, and were given, pieces of meat. Often, the favors were left unreturned.
Blurton Jones (1987) records what he calls tolerated theft. This occurs when resources exhibit a diminishing returns curve such that each additional unit of the same resource is seen as less valuable to the consumer than the preceding one; and when the marginal value of a resource determines the outcome of any competition over the resource. Rather than assume the cost associated with the competition over the resource, the consumer voluntarily cedes some of the resources to conspecifics who have a higher marginal value for that resource. Tolerated theft is most common among resources that are of a highly variable nature. Generally, the larger, rarer, and more widely distributed the resource is, the more likely it will be shared through tolerated theft. Waguespack (2002) documents the existence of tolerated free ridership in the form of 39 tolerated theft at the Palangana archaeological site in Alaska. Bliege Bird and Bird (2005) have also documented cases of tolerated theft in the Meriam of the Torres Strait.
Interestingly, Bell (1995) disputes the tolerated theft model on the grounds that there is a
“socially supported expectation” (828) that the resource will be shared, and that such an expectation belies the diminishing utility assumption that is necessary to validate tolerated theft.
However, such an assumption is not necessary in the more broadly conceived context of tolerated free ridership, in that tolerated theft is one kind of free riding behavior, and that the assumption of diminishing utility is an additional necessary condition only for that particular kind of free riding behavior we call tolerated theft. Bell suggests rather that successful hunting confers high status, and that some individuals within the tribe or band prefer to seek high status, while others, presumably those who free ride, are less concerned with status achievement. In contrast to the path taken here, Bell is more concerned with those individuals who give away the meat, rather than those who take it. However, as we shall see, Bell’s conclusion is roughly and simplistically consistent to the theory outlined here.
Linton (1955) notes that in subarctic Eurasian gatherer-hunter societies, surplus women were adopted by men as plural wives. These women, who may be widowed or ill, are unable to fend for themselves. They are often women who are not easy to take care of. Often, they require considerable resources, and offer no reciprocation and no return on the investment.
It is clear that human free riders are often tolerated. One reason for this might be the cost of punishment. As already noted, the cost of punishing free riders is dependent upon the amount of resources that punishing individuals have, and how willing they are to expend those resources on punishing a free rider. This is especially relevant when we recall that 20%-40% of individuals in a group free ride regardless of the frequency or severity of the punishment. 40
In short, the predictions about free riders made by the dominant theories in all three of these disciplines are inconsistent with much of the observed data. Again, this is not to deny that punishment of free riders does not exist. It is certainly true that free riders are not always tolerated, yet there are enough counterexamples to warrant concern about the explanatory and predictive powers of the dominant theories. Additionally, the dominant theoretical perspectives are unable to generate the same hypotheses for groups and aggregations. Specifically, theories that argue for the use of reciprocation or punishment are satisfactory as a possible explanation for cooperation in groups, but not in aggregations.
Chapter Summary
In this chapter, I have laid out the specifics of the collective action problem. I have briefly summarized the main approaches to the collective action problem in three distinct disciplines—sociology, evolutionary biology, and economics—and shown how each discipline, while beginning from a common assumption and following their individual paths, converge on similar predictions about the nature of free riding in the collective action problem. All three disciplines predict that free riders weaken group solidarity, and that free riders will be sanctioned into compliance with cooperative group norms. I have also demonstrated that the predictions made by each of these disciplines falls short of the observed data. There are numerous examples of tolerated free ridership in animals and humans that cannot be explained with the current theoretical approaches.
The purpose of the remainder of this work is to move toward a solution to the problem. I attempt to bridge the gap between the predictions and observational evidence by proposing an equilibrium model of interaction that offer a means by which the presence of free riders may be explained in both groups and aggregations. I will argue that free riders perform several functions 41 within groups and aggregations that facilitate duration, in the case of aggregations, and solidarity in the case of groups by moving the group toward a state of allostatic1 equilibrium. The next chapter moves us toward that goal by reviewing the origins of the extant predictions, and a reconceptualization of the premises of the origins. 42
CHAPTER THREE
PROSOCIALITY
“Networking has been a key to evolution since this universe first flared into existence. . . .The instant of creation marked the dawn of sociality.”
--Howard Bloom 43
Chapter Synopsis
The purpose of this lengthy chapter is to review the premises that underlie the predictions of the theories we have thus far reviewed. First, I take a very general approach to sociality as a state of nature. I argue, however, that sociality in humans has been approached in a way very different from other biological organisms. Specifically, I argue that all of these disciplines, for different reasons, begin with a Hobbesian conception of man. It is this ontological orientation that promotes the problem that was the subject of the previous chapter. I debunk the essentialist tendencies of Hobbes and Locke, and argue that we need to move beyond the simplistic understanding of rational action that emerges from such essentialism.
I propose a radical revision of rational action models. While traditional rational action theories have defined rational as that which makes the greatest gains at the lowest possible costs,
I boldly propose an equally rational alternative. I compare and contrast these two conceptions of rationality—termed profit maximization and probability maximization, respectively. This new approach opens the door for further exploration of rational action and its stability as a social strategy.
I spend some pages arguing that the group can function as would an organism, in an attempt to make a distinction between maximization and optimization. The distinction made is subtle, but important, as it sets the stage for the remainder of the work.
The Universal Rule
Howard Bloom (2000) opens the first chapter of his book by reminding us that sociality is inherent in the fabric of the universe. It is a lesson that we should be reminded of often, primarily because it is so pervasive. Our thoughts, ideas, behaviors, and communication are rooted in sociality. And yet, the fact is often overlooked. Sociality is so pervasive as to be often 44 taken for granted. It is not only inherent in the rules of the universe; sociality is the rule of the universe (Capra 1996; Bloom 2000)!
Sociality in the living and nonliving universe allows not only for the creation of a complex and dynamic environment, but also a means by which to exist within that environment.
The various bifurcations that become available both as constraint and response to constraint are virtually limitless. However, it is equally clear, given the order that is apparent at many levels of existence, that some bifurcations are superior to others. Bifurcations that are functional—that is, give rise to an emergent property that successfully responds to constraints—will be preserved over those bifurcations that cannot respond in an appropriate manner. However, there is something fundamentally different between the sociality of the nonliving and living universe.
Although both undoubtedly represent systems, they represent two very different kinds of systems. A galaxy or quasar represents a closed system, while a living organism represents an open system. Von Bertalanffy (1968: 121) writes:
The organism is not a static system closed to the outside and always containing the identical components; it is an open system in a (quasi-) steady state. . .in which material continually enters from, and leaves into, the outside environment.
This statement implies that open systems are self-regulating (Capra 1996). Open systems are far more adaptable than closed systems simply because open systems permit the influx and outflow of components. Closed systems are constrained by their boundaries. They can neither incorporate new material, nor expel erroneous material. All that may change in a closed system is the relationship between parts. Open systems have greater flexibility precisely because they can change the relationships between parts, but also change parts. Nevertheless, in many respects, it is the relationship between these parts that is of importance to understanding the successes and 45 failures of living organisms as they respond to their environments. Open systems, though less limited than their closed counterparts, still seem to be constrained by a relatively small number of successful strategies.
Three general functional explanations are commonly given to explain prosociality in living organisms (Wilson 1975). First, prosociality may be brought about by territorialism.
Territorial behavior is a likely outcome when food supplies are dispersed, but predictably stable
(Wilson 1975; Trivers 1985). Defense of a territory ensures adequate access to food and other resources, but also consumes a considerable amount of energy. Social defense of the territory minimizes the cost of defense for any one individual, since it is easier for the group to adequately patrol or defend its boundaries than any one individual in the group. Although such a strategy implies a marginal decrease in food for any one individual in the group, the benefits of such a strategy for the individual and the group greatly outweigh the costs. A group of stout defenders can not only better protect the territory than any one individual, but also can command a larger territory than any single individual (Wilson 1975). Territorial defense also ensures greater access to potential mates, as well as limiting the access of rival groups.
A second push toward prosocial behavior also involves defensive strategies. A concentration of members of the same species in any one location makes it more difficult for a predator to approach any one member without detection (Trivers 1971; Wilson 1975). The efficiency of the group at detecting predators is superior to that of any individual, and has no theoretic upper boundary. Relatedly, individuals that feel safer are able to decrease their expenditure on risk detection, and increase their efficiency in other activities, such as mating or food consumption. 46
Another principle strategy for risk avoidance is to utilize marginal members as a shield
(Wilson 1975). This strategy ensures both that the resources of a group are not being drained by free riding members—since they are more likely to become prey while at the margins of the group—and ensures that more productive individuals remain in the group. Wilson describes the behavioral phenomenon of individuals pressing toward the center as centripetal collapse. One example of this strategy comes from starlings (Sturnidae sturinini), who when pursued by predator hawks, engage in a complicated series of sharp turns, shifts of direction, changes in speed, and changes in altitude (Eshel 1978; Zahavi and Zahavi 1997). Such maneuvers require substantial energy, and the less fit members of the group quickly fall behind the stronger group members. The weaker group members, unable to maintain a place in the center of the flock, quickly fall behind and become easy prey for the hawks.
Congregational groups are conferred the advantage of proportionally diminishing the incurrence of individual risk, as well as multiplying that effect by increasing the average distance between groups (Wilson 1975; Bloom 2000). Such effects make it harder for predators to find prey. However, if the predator is transient, the advantage is mediated, since the group can move only as fast as its slowest member.
In either of these cases, marginal (i.e. less reproductively fit) members of the group are preyed upon much more frequently than stronger group members (Wilson 1975; Trivers 1985;
Zahavi and Zahavi 1997). Centripetal strategies, like the others, are advantageous only to the extent that the marginal members of the group can be replaced. If replacement cannot occur, the probability of risk reduction to productive group members must necessarily decrease. Should productive group members be preyed upon, the group loses not only a member, but also the contributions of that member. To combat these shortcomings, many social groups combine 47 strategies as a means of maximizing survival potential. Groups, of course, may utilize one or any combination of the strategies to maximize benefit.
These strategies are not without drawbacks, however. Territorial strategies are dependent upon the environment to maintain available food. Natural disasters, as well as swelling local populations, strain the environment and reduce the condition of territories. As extant resources diminish relative to group size, Malthusian controls inevitably result. Again, however, it is the less fit members who will feel the brunt of these controls.
Prosociality in Humans
Prosocial tendencies in humans have been approached in a fundamentally different way.
The Hobbesian view, whether tacitly or openly, underscores all previous research on group cohesion, and presupposes the need for a structural authority that provides adherence to the group (Hechter 1987; Wrong 1994; Bayertz 1999). Hobbes believed that a man has a natural right to pursue self interested ends, of which the most basic was self preservation (Frost 1962).
This is seen as lying in direct contrast to the interests of the group, which requires that each member sacrifice some individual interest for the collective good. Unlike on an individual level, where the ends of self preservation are best served by usurping the rights of others, the group level requires that certain rules of conduct are abided. It remains to be seen, from Hobbes’ conception of human nature, why any individual should choose to belong to a group that constrains individual action. Hobbes (1651) maintained, however, that in the state of nature, no individual could ensure his survival for very long. Therefore, a social group is formed as a protective measure, with the voluntary sacrifice of individual liberties.
Hobbes hoped to construct a natural law, standing independent from assumptions of human deification. Hobbes sought a positive law of hierarchy, in preference to common law 48
(Frost 1962). More generally, Hobbes hoped to provide a theory of absolute authority as a substitute for the divine right of kings. Finally, Hobbes also sought the first comprehensive articulation of bourgeois ethics. In contrast to the scholastics, who believed in studying human motivation as passions or affects, Hobbes believed that human nature could be described only in terms of active forces or motions, in terms of their consequences. This is the foundation of the later development of modern pragmatism and the accompanying essentialism and linear causal thinking about human behavior.
Hobbes argued that the motivation of each individual is to pursue his natural right of power and self governance. The consequences of this power, Hobbes wrote, was universal competition—a war where “every man is enemy to every man.” (107). The consequence of this competition is that human, in their state of nature, would have a life that is “solitary, poor, nasty, brutish, and short.” (107).
To escape this state of nature, Hobbes states, humans are driven by three forces—fear, hope and reason—that lead them to create articles of peace, or natural laws of governance.
Hobbes sees “Leviathan” as the artificial construction (as opposed to an emergent property) of a collective to escape the state of nature. In this way, Hobbes also escaped the traditional assumption that authority is given from above.
The natural laws are contrived through mutual contract among men to seek peace and security. The contract assumes that the desires of the individual for self governance and power are at fundamental odds with the needs of the group for peace and security; and requires that the desires of the individual be suspended for the good of the group. The group then becomes, as
Hobbes puts it, “an artificial man, though of greater stature and strength than the natural, for whose protection and defense it was intended.” (23). Similar to the natural man, the artificial 49
Leviathan of Hobbes has its own nature and laws that are at once dependent upon it, yet independent of the individuals that comprise the parts. The whole, then, is greater than the mere sum of the component parts.
Hobbes presumed that the state of nature determines the necessity of a constructed order.
In order for the individual to escape the warring state of nature, they must construct a social order with other individuals; an order that, according to Hobbes, will temper the interests of the individual in favor of a normative group structure that ensures peace and security for the group as a whole, and those within it.
John Locke’s (1690) later hypothesis that the human mind is tabula rasa, a blank slate, that can be written upon at will by individual or social forces historically further entrenched the
Hobbesian conceptions of the nature of man in the minds of social scientists. This is particularly noteworthy in that Locke seems to have disagreed with Hobbes on a number of points that are relevant to the discussion at hand.
While Locke refers directly to Hobbes only once in his writings, there are passages that clearly indicate his familiarity with Hobbes’ work. Locke’s writings were actually written as a response to the more contemporary Sir Robert Filmer, who shared absolutist tendencies with
Hobbes (Laslett 1965). More specifically, both Hobbes and Filmer shared absolutist tendencies that will is the source of law and the form of all authority. Where Hobbes and Filmer agreed that submission to the law and to the absolute authority that it represented is required, Locke argued essentially that authority is granted by the governed as opposed to imposed by powerful group members.
Locke argued, in contrast to Hobbes, that the state of nature of man is essentially good.
Locke correctly noted that cooperation, rather than overt competition, is the more common 50 human endeavor. Social order is formed, according to Locke, by a social contract, mutually agreed upon by all members of the group, for the purpose of equal justice. Locke rejected preternatural authority, and laid the groundwork for modern notions of democracy.
Locke’s assertion of the infinite malleability of the individual mind is a foundation for his assertions of the infinite malleability of society. Locke argued that the mind is a blank slate, and that human experience impresses all knowledge. Just as the social order is a negotiated contract between individuals, Locke sees the mind as a negotiated contract between the individual and his experience of the world.
Of course, both Hobbes and Locke predate Darwin’s (1859) theory of natural selection by nearly 200 years. Had either man been exposed to Darwin’s ideas, it is questionable whether they would have continued to hold their views. While Hobbes, Locke and Darwin would all agree that there exists a state of nature of man, and that society is both a product and process of that nature,
Darwin’s theory, however, suggests that sociality is a means by which individuals within the group may increase their reproductive fitness. Indeed, with a few notable exceptions, organisms throughout evolutionary history that have lived in groups have been more reproductively fit than individuals who are fundamentally asocial4. To put it another way, Darwin would have argued that sociality is not an order that is negotiated by mutual consent. It is rather an emergent property of the forces of natural selection. Darwin’s theory similarly denies the tabula rasa mind, and suggests rather that the mind is a mechanism of limited behavioral plasticity that maximizes reproductive success for that organism (Trivers 2002).
Subsequent testing of these various ideas have not been supportive of either Hobbes or
Locke. In fact, there exists no evidence for either position (Pinker 2002). This may be due, in part, to the inherent methodological difficulties of studying the brain and natural social groups; 51 yet, even as methods refine, no evidence for these perspectives has been forthcoming.
Falsification has been most harsh on Locke. With the development of contemporary behavioral psychology and psychobiology, considerable evidence has accumulated that the mind is, in fact, born with certain predispositions that facilitate isolation of some stimuli over others (Donald
1991; Joseph 1993; Mithen 1998; Panksepp 1998; Gopnik, Meltzoff and Kuhl 1999; Pinker
2002).
The writings of Thomas Hobbes form the cornerstone of many theories of human behavior in the social sciences. Yet, there exists no evidence that man is by nature solitary or brutish. Similarly, there exists no evidence that the interests of the individual and the interests of the group must necessarily be in conflict, or be mutually exclusive. In fact, the whole notion of essentialism is being challenged by the available evidence. Anthropological studies suggest that human social orders are, like individual minds, evolved from patterns of lower forms (Wilson
1975; Fiske 1990). Sociality is not a mere epiphenomenon, but rather an ancient and rather simplistic mechanism of ensuring survival of individual genes (Trivers 2002). Humans evolved as a social species, and must be considered in that light.
Various philosophers have suggested alternative models. For example, Henry Hazlitt
(1964) suggested that the interests of the individual and the interest of the group are often congruent. Individual needs cannot, in Hazlitt’s view, be acquired without deference to the group. Lest this should too much like Hobbes, Hazlitt noted that Hobbes prescribed a preternatural determinant. Hazlitt argued that none is needed. We need only to understand that needs are natural, and often beyond the grasp of any single individual. Therefore, it may be completely self interested to be accommodating to the interests of the group. 52
More recently, David Weissman (2000) has proposed a systems approach, arguing that human social systems are mirrors of universal, interconnected systems that cannot be reduced to mere parts. The unique structures that occur in a population are emergent properties with characteristics that are separate and unique from those of any of the component parts. While evidence is sparse, what evidence is available seems to indicate that these models are superior in many ways to the models of Locke and Hobbes.
We should not, therefore, assume that there is necessarily one human nature. The very concept of human nature is essentialist, suggesting that humans have fixed and unchangeable natures6
(Bradie 1994). Just as we should not assume that there is necessarily one theory that adequately describes any particular social behavior, so we should not assume that there exists only one conception of human nature that applies to everyone at all times (Ehrlich 2000). Human natures may change as technology changes, as new properties emerge from newly created complexities in interaction.
It should be noted, however, that this is not the same as saying that there is no human nature. If that were true, then no continuity could exist at all between interactants, and therefore no social life would be possible. It is to say that there exist templates of behavioral strategies upon which can be built more complex systems. Natural selection often builds on existing traits, utilizing them for new or expanded functions (Trivers 1985; Gould 1977). Similarly, humans have the ability to build on basic templates of behavior to expand their social repertoire. To put it another way, rather than a single human nature, there exist a panoply of human natures within bounded limits. While no one set of behaviors may be properly labeled as human nature, There are evolutionary and cultural limits to the successful behavioral plasticity that humans may exhibit. The pervasiveness of essentialism is evident in the literature on collective action. 53
Coleman (1990) suggests that the interests of the individual are orthogonal in most cases. Yet, even in those circumstances in which the interests are congruent, they are seen as minimally correlated (Willer, Borch, and Willer 2002). Various scholars have proposed that group sanctions temper the interests of individual against the interests of the group (Williamson 1975; Hechter
1987; Coleman 1990). Willer, Borch and Willer (2002) expands on this conception by arguing that the group tempers individual interests through distribution of group resources that minimize the importance of individual interests. This assumption, however, presumes what it sets out to prove. Accept that group sanctions are presumed to mediate the interests of the individual.
Accept that those sanctions are themselves dependent upon the existence of a solid group for their strength against the individual. A mutual contradiction is apparent. The assumptions work only if we believe that norms precede the existence of the solid group—a Durkheimian notion which, as I have already argued, is theoretically and philosophically untenable.
That being said, it remains somewhat of a mystery why contemporary social science still builds its foundations upon the Hobbesian assumption. I believe that much of the clinging to
Hobbes and Locke stems from a fundamentally misguided understanding of biological paradigms. That is, biological paradigms are often (incorrectly) seen by social scientists as deterministic, rather than as probabilistic8. Relatedly, this misinterpretation has led the push to maintain a social desirability factor among social scientists that leans toward notions of a malleable society. In other words, the world views of Hobbes and Locke appeal to the utopist social agendas of social scientists, and are therefore preserved, even in the face of mounting counterevidence. What is still unclear, however, is why social science—which was founded on the premise that groups should be the primary unit of analysis for human behavior—clings tenaciously to what are largely individualistic and essentialist models of social ontology. 54
In contrast to the Hobbesian assumption that self interest is the most rational and natural state of man, I propose, in concurrence with Darwin, that man evolved as a social species. Given the pervasiveness of cooperative behavior across the evolutionary continuum, it seems reasonable to assume that cooperation is closer to the natural state of organisms than is individualism. It is also perfectly reasonable to see cooperation as a strategy that is equally rational to self interest, provided that cooperation confers the greatest individual benefit for the actor. As an example, consider that it may be in my best interest to make concessions to my critics in order to achieve the individual success of completing this project in a timely manner.
Additionally, if cooperation is the state of nature, then it is free riding behavior that must be explained—but this can be done only in the context of its relation to cooperation.
Maximization Strategies
Free riders are found in virtually every extant social species. This implies that it, too, is an adaptive response to evolutionary pressures, rather than a deliberate, constructed behavior. It also implies that the evolutionary origins of the behavior go back quite far in the evolutionary continuum. There is, however, a more important implication. If indeed free riding is an adaptive response, it implies that there exists a selective advantage to free riding.
Even if the discussion of free riders is limited to humans, it is still useful to explore the nature of the adaptive significance of free riders. Dettwyler (1991) argues that free riding was commonplace in early human society. Indeed, given that the best evidence for the structure of early human social interaction is taken from gatherer-hunter societies, there is ample evidence, as already shown, that free ridership is a natural and often tolerated behavior. 55
Free ridership is more than a mere behavior, however. It is more than a single action, or even a series of actions. Free ridership is a response to a specific set of social circumstances. It is, in short, a social strategy—a template of behavior within a social group that optimizes benefits and minimizes costs.
Rational action models and evolutionary models converge on the conclusion that self interest is a rational strategy to pursue. This is of interest, in that such a statement is essentialist unless the statement describes the average, or normative, state of being, rather than all possible variation. If we eliminate essentialism from our interpretation of rational self interest, then we may properly deduce rational behavior in a way that does not leave free ridership as an unexplainable phenomenon.
Traditionally, self interest as a rational social strategy has assumed a definition of receiving the greatest possible payoff at the lowest possible cost (Argyle 1991; Dugatkin 1998;
Barash 2003). The object of self interest is the maximization of profit. However, I wish to suggest that there exists an equally rational social strategy—that of probability maximization.
Probability maximization is defined simply as a social strategy that seeks to maximize the probability that the individual using the strategy will end up with anything better than he started with. Thus, the goal is not necessarily to maximize profit, but rather to act such that the odds of any gain are maximized. Hypothesizing such a social strategy is reasonable, and has both theoretical and empirical support (Argyle 1991; Giraldeau and Livoriel 1996). One example of probability maximization can be found in the mating game.
It is well documented that across species, high status is associated with preferential mating rights. However, it is also well documented that lower status individuals mate successfully (van Lawick-Goodall 1971; Trivers 1985; Sapolsky 2001). Low status individuals, 56 however, tend to mate only with low status individuals, while high status individuals tend to mate with both high and low status individuals, with preferences for high status individuals. Low status individuals have no real hope of maximizing fitness through mating with higher status conspecifics, and therefore must be content to maximize fitness by mating with anyone. This is a probability maximization strategy—mate with anyone to ensure that you will have any mating at all, regardless of the status of the mate. This lies in contrast to the profit maximization strategy of high status individuals, who universally have a preference for high status mates. The goal in this case is to seek the highest possible profit—the mate with the individual of highest status.
Another way to understand the distinction is by exploring a hypothetical lottery. In this thought experiment, there are two possible choices. You may play the profit maximization lottery, which offers a 1/1000 chance of winning the prize of $1000. Alternatively, you may choose to play the probability maximization lottery, which offers a 1/10 chance of receiving a
$10 prize. Assuming that the cost of playing each lottery is equal—say $1—and that you may play as many times as you like, it is equally rational to choose one as to choose the other.
A real world example of probability maximization comes from Wal-Mart. The strategy of the conventional retailers is to maximize the profit on each individual item by charging the highest price that will not deter consumers from purchasing the item. Wal-Mart, on the other hand, charges a much lower price than most retailers. Often, the price per item sold gives only a marginal profit; Wal-Mart makes their money in volume. The strategy of the world’s most successful retailer is to maximize the probability that a consumer will purchase the item at Wal-
Mart, even if it means a lower profit per item (Walton 1993; Soderquist 2005).
Social strategies are best understood as embedded in one another, as parts to a greater whole. For instance, it is difficult to fathom the caste system of hymenoptera9 through analysis of 57 one division of labor only. It is only through understanding the role that each division plays in the greater whole that the social nature of the caste system can be properly understood (Wilson
1975). Similarly, while both profit and probability maximization strategies can exist independently of one another, it is their relation to one another that is of interest. Like the moves of chess men, each strategy responds to the other. As I hope to show, the result is an allostatic equilibrium state in which both strategies maintain optimality, and facilitate the existence of free riders among cooperators. Social strategies, I will argue, also play a role in the functioning of the group (qua group). More specifically, I will argue that social strategies operate in a state of equilibrium to maintain the group in a dynamic environment.
First articulated by Maturana (1970), autopoiesis is defined as a network pattern in which the function of each component is to participate in the facilitation or transformations of other components (Maturana 1970; Capra 1996). Each relation between components operates to facilitate itself, and to transform the relation such that the relations of other components are affected.
Autopoiesis: Is the Group Like an Organism?
Three criteria are generally used to define autopoiesis (Fleischaker 1990; Capra 1996). First, the system must be self bounded this occurs when the extent of the system is determined by a boundary that is composed by the relations between components. An autopoietic system must also be self-generating, which means that all components, including the boundary, are produced by relational processes within the network. Finally, autopoietic systems must be self- perpetuating. Self-perpetuation occurs when the relational processes continue over time so that all components are eventually replaced by the system’s relational processes. 58
The theory of autopoiesis states that a living system interacts with its environment through a process known as structural coupling (Capra 1996). Structural coupling occurs when a living system interacts repeatedly with its environment, such that there are structural changes in the system. In other words, the system adapts to dynamic environmental stimuli; the group will tend toward an equilibrium state. It is important to note that the environment merely triggers the structural change; it does not direct the change nor specify the nature of the change. This is, incidentally, very similar to the process of natural selection, which responds to environmental change without direction or purpose (Gould 1992).
Maturana (1970) argues that through structural coupling, the behavior of autopoietic systems is more or less determined. However, rather than being determined by outside structures, as Durkheim suggests, behavior is determined by the system’s own structure. Changes in the structure of the system usually results in changes in future behavior. Thus, the impact of environmental constraints on behavior of the system is relevant, but indirect. The relations of components to one another bears directly on the behavior of the system. The behaviors are a product of the structural history of the system as it adapts to a dynamic environment. It is not necessary that all of the relations between components change; it is enough that some of them change in nature, frequency, or strength. Structural changes, which are often non-linear in nature
(Capra 1996) give rise to new emergent properties, which tend the system toward an equilibrium state. Michod (2003: 291) writes:
An organism is more than a group of cooperating cells related by common descent and requires adaptations that regulate conflict within itself. Otherwise, its individuality and continued evolvability is frustrated by the creation of within-organism variation and conflict between levels of selection. Conflict leads to greater individuality and harmony for the organism through the evolution of adaptations that reduce it. 59
The question of whether social systems may be considered autopoietic has been the subject of considerable debate (Capra 1996). Autopoiesis was originally defined only for physical systems and computer simulations defined in mathematical space. The central point of debate is that while physical systems must obey natural laws, conceptual systems like those created by human social groups need not necessarily abide by the rules that emerge from the system.
Niklas Luhmann (1990) argues that an autopoietic social network is feasible if the description of human social systems remains entirely within the boundaries of that social system.
For Luhmann, these social processes are processes of communication. He writes:
Social systems use communication as their particular mode of autopoietic reproduction. Their elements are communications that are. . . produced and reproduced by a network of communications that cannot exist outside of such a network (104).
Aggregations of individuals without emergent properties will usually fail to be autopoietic.
Aggregations will often have one or two of the characteristics of an autopoietic system, however, they will fall short in that there are no emergent properties in aggregations; there is no structural coupling. Groups, however, are characterized by emergent properties that arise from the relations between individuals. These emergent properties allow for self-generation, self-perpetuation, and a clearly demarked group boundary. This is not to argue that groups must achieve a state of autopoiesis; rather, I am suggesting only that groups may achieve such a state. Autopoiesis is an equilibrium state, in which all components function in such a way as to perpetuate the group through specific relational qualities. Both frequency of relations and the strength of the relations will be implicated in the emergence of autopoietic characteristics. An autopoietic state is one in 60 which no group member can obtain more optimum circumstances by unilaterally changing his social strategy, which is the definition of an equilibrium state.
Optimization is distinct from maximization, however. To the extent that maximizing behaviors are rational, maximizing behaviors are not necessarily optimal. Assume you need to get from point A to point B. A train traveling directly between the two points is determined to be the route which maximizes your time (i.e. gets you to your destination sooner). Once underway, the brakes on the train fail. While the train is still the rational means to get to your destination, it is no longer optimal, in that there is now a great probability that you will die as the train crashes into the station. You may jump from the train and survive, but this not optimal either, in that while you have maximized your probability of survival, your time is no longer maximized. In an autopoietic state, optimization replaces maximization as the dominant goal. In this sense, social groups are autopoietic. Groups react and adapt to the dynamic environment, changing structure as necessary, changing the relations between members, tending toward an equilibrium state. In other words, groups optimize.
In the following chapter, I will elaborate on the notion of optimal social strategies by using game theory. Game theory is a technique for exploring strategic choices in relation to other players. Using game theory, I hope to show how different social strategies can be fundamentally compatible, even when they appear to be competing with one another.
Chapter Summary
In this chapter, I have reviewed sociality as a state of nature, arguing that sociality is the natural state of organisms. With few notable exceptions, all extant species are social. I then argue that current understanding of man as a social being is rooted primarily in the philosophy of
Thomas Hobbes and John Locke, both of whom conceive of humans as asocial, rectus civitas 61 res. I have argued that the essentialist notions of Hobbes and Locke should be replaced by a paradigm that sees humans as fundamentally social creatures, and argue that rational action theories can maintain compatibility with this change by incorporating the notion of embedded rationality into its lexicon.
I have argued that there exists at least one equally rational alternative to the traditional profit maximization of rational action theories. Termed probability maximization, I argue that this concept of rationality seeks to maximize the probability that the individual will get anything better than he started with, however small the net gain. I argue that this strategy can be understood only as it is embedded within the larger framework of the traditional profit maximization strategy.
I end the chapter by discussing the concept of autopoiesis. I argue that groups may be seen as functioning like an organism in many ways. This sets the stage for the following chapter, which discusses embedded rationality in terms of competitive game theory. 62
CHAPTER FOUR
GAME THEORY AND EQUILIBRIUM STATES
“We think that the procedure of the mathematical theory of games of strategy gains definitely in plausibility by the correspondence which exists between its concepts and those of social organizations”
--John Von Neumann and Oskar Morgenstern 63
Chapter Synopsis
In this chapter, I illustrate the nature of optimality—or embedded rationality—using competitive game theory. Game theory is typically seen as a means by which varying competitive strategies are assessed. This is usually accomplished through an analysis of individual equilibrium states. However, what is less often discussed is embedded rationality. In many competitive games equilibrium states are elusive and generally not robust. However, stable optimal states can emerge from seemingly competitive strategies in cases where the individual equilibrium states converge.
I illustrate this phenomenon through several examples of noted game theoretic matrices. I further illustrate the nature of exogenous and endogenous constraints on the principle of optimality. I argue that optimality is robust against embedded rationality, but not against individual rationality.
An Introduction to Game Theory and Embedded Strategies
It should not be assumed, as it often is, that cooperation is necessarily a socially desirable, or even morally correct, phenomenon. The same should be said for competitive or free riding strategies. The morality of a specific social strategy is certainly important to consider, in that like social strategies themselves, the morality of any strategy is relevant only as it pertains to another strategy. Discussions of cooperation are often argued in terms of individual desirability, and are considered as existing in a vacuum. However, cooperation implies sociality, and must be considered as a social behavior. One simple way to examine the emergence and maintenance of cooperation as a social strategy is game theory.
First devised as a mathematical approach to decision making by John Von Neumann and
Oskar Morgenstern in 1944, game theory models the available strategic choices an individual has 64 in a particular scenario relative to the other players. Von Neumann, who laid the ground work for such theorizing in 1928 (Pounstone 1992), assumed that the interests of all players are strictly opposed. He then demonstrated that every finite, zero-sum, two person game has optimal mixed strategies. Each player will attempt to maximize his gain and minimize his opponents’ gain in a zero-sum scenario. The goal of game theory was to devise a method to systematically deduce the optimal course of action among a set of strategies that will lead to the most beneficial outcome for any individual player or group of players relative to the other players in the game.
When a situation occurs in the game such that no player can improve his relative position, the game is said to be in a state of equilibrium. Von Neumann realized that players in multiple person games will often form coalitions with the joint goal of gaining from another player. Each individual in the coalition sacrifices, in that the cost to benefit ratio in a coalition is higher than remaining individualistic (Von Neumann and Morgenstern 1944). Coalition formation quite obviously relaxes the zero-sum condition of the theory, although it does not eliminate it entirely. For that reason, Von Neumann concentrated on two person strategy games.
John Nash, a young mathematician at Princeton, realized both the utility and the limitations of a game theoretic approach. Nash devised equations for games of more than two people, where forming a coalition was not possible (Poundstone 1992). Nash argued that in such games, players will come to a point where their benefit is maximized relative to their cost, such that no player will be in a position to desire to unilaterally change his strategy. Subsequently dubbed the Nash equilibrium, these calculations expanded the use of game theory as a means by which decision making might be modeled. 65
The Prisoner’s Dilemma
To test Nash’s hypotheses, Merrill Flood and Melvin Dresher devised a game in which the equilibrium state is thought to be non-desirable to each of the players. Now known as the
Prisoner’s Dilemma (PD) game, Flood and Dresher’s game has become the most popular and most studied in game theory. The Prisoner’s Dilemma, along with similar games, has emerged as an important way to study human interaction (Argyle 1991; Barash 2003). In the PD game, two or more players independently decide whether to cooperate with the other player, or defect.
There are four possible outcomes for each round. Player 1 and player 2 may both cooperate
(CC); player 1 cooperates and player 2 defects (CD); player 1 defects and player 2 cooperates
(DC); or both players defect (DD). The matrix is organized so that DC>CC>DD>CD, and
CC>(CD+DC/2). In the payoff matrix, mutual cooperation earns each player 3 points. If one player defects while the other cooperates, the defector earns 5 points, while the cooperator earns nothing (the sucker’s payoff). If both player’s defect, they mutually receive 1 point.
An equilibrium point is reached when every player has maximized his minimum payoff, and minimized the other players’ maximum payoff. In a single iteration, or one shot PD game, the most rational course of action is to defect.
This is because no matter what the other player does, you will collect some value that is greater than the starting value. In other words, you will maximize the probability that you will get anything better than you started with. It also offers the best probability that you will get anything better than you started with. It also offers the best chance for high profit, since cooperation by the other player will ensure them the sucker’s payoff. Thus defection in a single iteration PD game offers both probability and profit maximization. As we shall see, however, this is not necessarily so in an iterated PD scenario. 66
Political scientist Robert Axelrod (1984) utilized a version of the Prisoner’s Dilemma game in a now famous simulation experiment. Axelrod sponsored a contest in which each participant was to submit a computer program to play a multiple iteration PD game. Each program was to play against every other program, and a random program. Total scores were accumulated at the end of each game, and a winner declared based upon the total score of each program after all simulations had been run. The winning strategy was submitted by Anatol
Rapapot, a Canadian political scientist. Known as Tit-for-Tat (TFT), the program was the simplest strategy submitted. Simply put, TFT cooperated on the first iteration, and then did whatever the opponent had done on the previous round.
Player 1 Column Player
cooperate defect
c o P1=3, P2=3 P1=5, P2=0 o p Reward for mutual Sucker's payoff, and e cooperation Temptation to defect r Player 2 a t Row Player e
d P1=0, P2=5 P1=1, P2=1 e f Temptation to defect Punishment for mutual e and sucker's payoff defection c t
Figure 1: Axelrod’s Prisoner’s Dilemma Matrix 67
Although TFT won very few individual games, it had the highest overall score. Axelrod attributes TFT’s success to three characteristics of the program. First, TFT is a “nice” program.
In other words, it cooperates on the first round of play and seeks cooperation throughout the game. Second, Axlerod noted that TFT is quick to punish defectors by defecting itself on the following round. Finally, TFT is equally quick to forgive. The program returns to a state of cooperation as soon as its opponent does. In short, TFT was successful because in a behaviorally interdependent scenario, TFT induced cooperative behavior from the other players (Coleman
1990; Barash 2003).
In later works, Wu and Axelrod (1997) added programming to their simulations that introduced random errors to implementing choice. This noise, as they refer to it, was initially disruptive to the iterative game. However, methods did evolve for dealing with the errors. First, adding generosity to a reciprocating strategy was found to be an adequate way to deal with random noise. Wu and Axelrod found that a generous version of TFT, known as Two Tit-for-Tat, was a highly successful strategy when errors were likely, but only if the other players in the iteration had not already adapted to it. Second, in the event that the other players have adapted to the noise at the time of the targeted iterations, adding contrition to a reciprocating strategy effectively dealt with the noise. This was illustrated by a contrite Tit-for-Tat. In this version, TFT has three states: contrite, content, and provoked. The program begins in the content state, and remains there unless there is a unilateral defection from another player. It then becomes provoked, and remains there until the defector cooperates, in which TFT returns to the content state. If TFT was the defector while content, it becomes contrite and begins to cooperate.
A final strategy for dealing with random noise is known as Pavlov. Simply put, Pavlov measures the ratio of benefits to cost for various choices. If the payoff for a particular iteration is 68 sufficiently high, Pavlov will continue to utilize that choice until a more profitable option emerges. If Pavlov receives a low payoff, it will unilaterally change its choice on the following iteration. This strategy, which Wu and Axelrod reject as not robust, is effective only against strategies that did not remember past choices. It is a model profit maximization strategy (Axelrod
1987).
Axelrod’s simulations raise several important points. First, it seems possible, at least theoretically, for cooperation to emerge out of competitive interaction. Second, it is no longer clear that defection is the best strategy, at least in the long term. Even though a strategy of always defecting gains more points when paired one on one with cooperative strategies, always defecting is only marginally successful against other strategies (Axelrod 1984; Poundstone
1992). In fact, neither always defecting nor always cooperating turn out to be stable strategies
(Lorberbaum 1994; Giraldeau and Livoriel 1998). Loberbaum (1994) goes on to prove mathematically that no strategy is in fact stable in the PD game if considered independently.
However, a stable equilibrium state can form when cooperative strategies are paired with defection strategies, and seen as frequency dependent upon one another.
If it is assumed that each member of the group will encounter each other member at some point in time, a strategy of defection when you meet a cooperator ensures success only if two conditions are met. Defectors (free riders) can exist in a group only if there are cooperators.
Moreover, the number of defectors that can exist is dependent upon the number of cooperative conspecifics (Giraldeau and Livoriel 1998). Payoff for defection is greater when the proportion of cooperators is high. Put another way, the higher the proportion of cooperators to defectors, the higher the average fitness of the defectors will be (Giraldeau and Livoriel 1998). 69
It seems paradoxical at first glance, but in the PD game cooperating is a profit maximizing strategy, while defecting is a probability maximization strategy. For example, defection offers the greatest long term probability of getting anything better than you started with. Cooperation, on the other hand, has a lower probability of profit (since if you cooperate while the other person defects, you receive the sucker’s payoff). However, the long term profits of cooperators are generally greater than those received from a pure defection strategy. However, when the proportion of cooperators is high, their high profits and cooperative nature make them easy targets for exploitation by defection strategies.
Caraco, Blankenhorn, Gregory, Newman, Recer and Zwicker (1990) found that by reducing the supply of resources in actual scenarios, the ratio of cooperators to defectors changes in favor of the cooperators. This finding suggests that in times of resource deprivation, free ridership declines. In other words, an abundance of public goods—though not necessarily a surplus—is required for a free riding strategy to be successful (Davies and Houston 1981).
Barash (2003: 139) notes that “one of the important findings that emerge from Robert Axelrod’s computer tournament was the importance of being nested in a network of cooperators.”
The Stag Hunt
So far, I have suggested that free riders are dependent upon a larger cooperative framework. Free ridership as a social strategy is embedded in the larger cooperative gestalt.
Alone, free ridership is not a stable strategy; the same may be said for cooperation, since it is highly susceptible to invasion by rival strategies. It is only when the two strategies operate in a frequency dependent manner that the group achieves a stable equilibrium.
The analysis up to this point has been simplistic. Yet, we cannot assume that any situation in the real world necessarily corresponds to a PD game (Argyle 1991). Fehr and 70
Gächter (2003) point out, however, that there were likely numerous PD type scenarios throughout our evolutionary history. Indeed, Barash (2003) identifies several contemporary situations which may be classified as Prisoner’s Dilemma scenarios. What happens, though, if the payoff matrix is changed? Consider the example from philosopher Jean-Jacques Rousseau called the stag hunt.
A group of hunters are pursuing a stag with their bows and arrows. Such large game offers plenty of food for all of the hunters, but is relatively hard to find. The success of the hunters if a stag is found requires that everyone cooperate and use their collective energies to bring down the stag. While engaged in the hunt, one of the hunters spies a rabbit. Although smaller than the stag—smaller even than the portion of the stag the hunter is likely to get—the rabbit is easy to catch: a sure thing. Since it is possible that a stag will not be located, the temptation of the hunter is to go after the rabbit and leave the other hunters without any food.
Even if a stag is found, other hunters might have been similarly tempted to chase their own rabbit, leaving too few people to bring down the stag. The payoff matrix for the stag hunt game is shown below.
In the stag hunt, the strategy with the biggest payoff is cooperating, provided of course, that the other hunters do the same. There is also a chance that no stag will be found, and everyone will be worse off than if they had acted alone. The consequence of everyone chasing rabbits produces a higher success rate, but a lower individual return.
Once again, it is clear that neither strategy alone is stable or sufficient. However, when the two strategies are combined in a frequency dependent manner, they become mutually reinforcing. For example, if there are too many stag hunters, the law of diminishing utility states that each additional hunter beyond what is needed to kill the stag will be less beneficial than the 71 one before. Coupled with the fact that the party is to divide the stag evenly among all stag hunters, regardless of their contribution to the hunt, it is clear that such a strategy cannot be stable. Stag hunters above the critical mass that is needed to kill the stag decreases the size of the share for everybody involved. With smaller and smaller shares of the stag, it is beneficial at some point to begin to hunt rabbits, since the payoff of hunting rabbits is equal to or greater than the payoff in hunting the stag. However, everyone hunting rabbits is also problematic, in that if everyone hunts rabbits it will likely cause a downturn in the population of available rabbits, in which case everyone will be forced to hunt stags, at least for a little while.
When the two strategies are pursued side by side, with some preferring group stag hunting, and some preferring solitary rabbit hunting, the system is in balance.
other hunters
stag rabbit
s t a g you=4, others=4 you=1, others=3
you r a b b i t you=3, others=1 you=2, others=2
Figure 2: The Stag Hunt Payoff Matrix 72
Of course, the best possible individual choice is to ally yourself with the stag hunters, guaranteeing a share of what they catch, without actually participating in the hunt—a free riding strategy. However, if everyone thinks that way, then everyone is better off hunting rabbits. No strategy is stable by itself. However, when strategies are combined, the result is an equilibrium in which every social strategy operates at its optimum.
Game theory analysis often assumes that the players in a given game are equals. This is, however, absurd. Individuals enter a social dilemma with various talents or strengths that may or may not be of use to them. Players often differ in the amount of resources they have, or that are willing to invest. As I have already pointed out, punishment of free riders could depend greatly on the tolerance and resources of high status individuals. Furthermore, resource thresholds will have a lot to do with the strategies that are utilized by the various players.
In the Prisoner’s Dilemma and Stag Hunt games, individual players with more resources can afford to pursue riskier, high profit strategies. Low resource players are confined to situations where their probability of success will be high. To demonstrate this, let us look at one final game.
The Chicken Game
Two cars are hurtling toward each other at high speeds. Each driver can make one of two choices: swerve, or go straight. To go straight is to win; to swerve is to become the chicken. Of course, there is the possibility that both drivers would swerve, in which case they are both chickens, but neither suffers in reputation relative to the other (Barash 2003). Both drivers can go straight, each attempting to make the other swerve. Both are deeply committed to winning. The result is that both drivers suffer a common disaster. 73
In this game, to go straight is to defect and to swerve is to cooperate. The driver who defects wins, but only if the other driver swerves. The punishment for mutual defection is disastrous. Like the Prisoner’s Dilemma, the highest payoff occurs when one player cooperates and the other defects. In this game, it is even clearer that the correct strategy for one player is embedded in the strategy of the other player. The game has two Nash equilibrium points, which are different pure strategies for each player: defect when the other player cooperates; and cooperate when the other player defects (Barash 2003). No free riding is possible in this game since the nature of the game allows differential success only at the expense of the other player. The probability maximization strategy and the profit maximizing strategy are the same— swerve. In short, the Chicken game is not a social dilemma. While the choices each person are likely to make are embedded in the decisions of the other, there is no contribution to the public good that allows free riding to occur. To put it another way, while the specific
driver one
stay swerve
s t a D1=0, D2=0 y D1=0, D2=5 mutual destruction driver two s w e r D1=0, D2=0 v D1=5, D2=0 e mutual embarrassment
Figure 3: The Chicken Game Matrix 74 strategy followed in the Prisoner’s Dilemma and Stag Hunt games are dependent upon both external and internal constraints, the specific strategy followed in the Chicken Game are independent of both external and internal constraints.
External, or exogenous constraints are constraints that impact the group from the surrounding environment. These may broadly include, but are not limited to, selection pressures, population pressures, and rivalry. Internal, or endogenous constraints operate from within the group. Endogenous constraints are more limited and more specific than exogenous constraints, and are often affected by exogenous constraints. Endogenous constraints include information, status, power, individual resources, or limits on the kind of relations permitted between group members.
Constraints are vital to an aggregation or group because they serve to define the limits of relations between group members. For example, individuals with more resources are less constrained than individuals with fewer resources. As already pointed out, this may be one explanation for tolerated free riders. Greater access to information will often confer an advantage to one group member over another.
How do we know that game theory approaches offer us accurate insight into the world of free riders? Gomulkiewicz (1998) states that “game-theoretic approaches are appropriate when fitness is frequency-dependent (i.e. when individual fitness depends on the distribution of phenotypes)” (288). Indeed, this is exactly what I shall argue. Groups must constantly adapt to both exogenous and endogenous changes. Like an organism, a group that cannot adapt to the dynamics of a changing world is selected against, and will fail to reproduce as effectively. Those emergent properties that cannot answer to the changes around them will vanish as suddenly as they arose. Similar to an organism, a group that is plastic enough to respond appropriately to 75 dynamic changes in environments—both internal and external—will endure, and many of its emergent properties will be preserved for posterity.
How do groups change and adapt to changes in the environment? That is the subject of the next three chapters. In chapter five, I argue that at least four different kinds of free riders exist, with different implications for the group. In chapter six, I discuss in more detail the notion of frequency dependence, equilibrium, and how disequilibrium affects the group and precipitates change. In chapter seven, I lay out a specific model of equilibrium that demonstrates how two competitive strategies can exist simultaneously in a state of equilibrium.
Chapter Summary
In this chapter, I have argued that competitive game theory offers an adequate illustration of the nature of embedded rationality. I have shown how optimum strategies for several game theoretic models are not stable when considered individually; but converge on a stable equilibrium strategy when considered as embedded within another strategy. Additionally, I have added to the argument the notions of endogenous and exogenous constraints. These are seen as limitations to the repertoire of actions that are available to the individual actors, and that such constraints affect the optimality of any strategy. I have argued that optimality is robust against embedded rationality, but not against individual rationality.
Having now laid down the framework for the theory, I offer a typology of free riders to further clarify their role as it is embedded in the larger cooperative framework. 76
CHAPTER FIVE
A TYPOLOGY OF FREE RIDERS
“Commonsense is nothing more than a deposit of prejudices laid down by the mind before you reach eighteen.”
--Albert Einstein 77
Chapter Synopsis
In this chapter, I argue that at least four different kinds of free exist. I argue that we may distinguish free riders on the basis of motivation and degree of behavior. The utility of this approach is to gain further understanding of the diversity of free riding behavior, and the consequences that each has as it affects toleration of free riders. More exactly, the distinction allows us to differentiate between the consequences that occur from the various types of free riding. I also suggest that this typology may partly explain the disparity in the nature and degree of free riding in many empirical studies.
Making Distinctions Between Free Riders
While much has been written about free riders in a variety of disciplines, the findings within and across disciplines has quite disparate (Hechter 1987). For example, Bohm (1972);
Sweeney (1973); and Marwell and Ames (1979; 1980) found considerably less free riding than predicted. Kim and Walker (1984); Brunner (1998); Isaac and Walker (1988); and Gaube (2001) demonstrated free riding at or above predicted levels. Some observational research, such as that done by Fessler and Haley (2003) shows moderate degrees of free riding and infrequent and inconsistent punishment for free riding individuals.
Hechter (1987) attributes the disparity at least in part to the different methodologies and experimental settings. This explanation is almost certainly true. However, it is probably not the entire explanation. Marwell and Ames (1980) subjected previous experimental protocol to a variety of changes in parameters, with little effect in the degree of free riding. Another possible explanation for these disparities is that the experiments conducted identified and tested different types of free riders. Subtleties in the various experimental settings may have unknowingly led to 78 the emergence of different categories of free riders, with characteristics that are distinctive to that group.
Biologist Robert Trivers (1971) identified two distinct types of free riders. Subtle cheaters are free riders who reciprocate in a cooperative setting, but contribute less to the creation of the public good than they receive. Gross cheaters are free riders who completely fail to reciprocate in a cooperative setting. They are akin to the defectors in the Prisoner’s Dilemma game discussed earlier. Economic theories of free riders seem to have picked up on this distinction as well. Smith, Kehoe and Cremer (1995), and Brunner (1998) note the difference between individuals who contribute nothing at all to the public good, and those who contribute less than they benefit from the public good. Brunner terms them free riders and easy riders, respectively. Though the nomenclature differs, the definitions are essentially the same. Trivers notes that evolutionary theory and social forces will likely select against gross cheaters to a greater degree than subtle cheaters. Thus, the relative frequency of gross and subtle cheaters will likely differ. In addition, their outcomes as relational members of a group or aggregation will also likely differ.
I will make yet another distinction between types of free riders, not in conflict to Trivers’ typology, but rather as complimentary to it. Trivers bases his typology on preferences founded on a rational action model. In other words, the goal that is in pursuit of some preferred outcome is more rational than pursuit of some less preferred outcome. However, preference alone is not sufficient, as Coleman (1990) points out. Action and motivation are also necessary components of preference. If I prefer pizza to spinach, I will be more motivated to seek out and eat a pizza than a can of spinach. It is the motivation which drives the action. The action thus corresponds to the motivation. That is what is meant by preference. 79
Trivers’ distinction deals merely with the action of free riding. It is the action of partially reciprocating or not reciprocating at all that Trivers bases his distinction on. Yet, we may also make a distinction based upon motivation. For example, some individuals may be more motivated to contribute to the public good than others, but are unable to do so due to age or infirmity. Indeed, the example given earlier by DeWaal (1996) illustrates this distinction admirably. The Japanese macaque (Macaca fuscata) Mozu was born with no hands and no feet.
It is clear from DeWaal’s report that Mozu was motivated to be part of the troop, and to contribute to its success. However, her role in this capacity was seriously compromised due to her disability. Children and the elderly may be willing to help with certain tasks, but are unable to do so because of their age. All are free riders by definition—they all take advantage of public goods without replacing to the degree they consume. Still, the motivation to contribute is present.
It is their ability to act on that motivation that is lacking. These I shall call passive free riders.
Juxtaposed to passive free riders, there are also active free riders. These are, I believe, what Coleman and others are generally referring to when they discuss the free rider problem.
Active free riders may be distinguished by their lack of motivation to contribute. Active free riders may be perfectly able to contribute to the public good. They simply choose not to.
As noted, the distinction between active and passive free ridership are not incompatible with
Trivers’ distinction. In fact, the two distinctions are quite complimentary, as are motivation and action. We may then have active subtle free riders; active gross free riders; passive subtle free riders; and passive gross free riders. I shall take each of these in turn, discussing the nature of the distinction, and the predictions that come from those distinctions. First, however, it is important to note that these distinctions may not be discrete in all cases. Some individuals, for example, may be unable or unwilling to contribute in one social context, but not in another. Motivations, 80 and the actions that accompany them, may change as constraints change. There are a number of studies that document such changes, including Barnard and Silby (1981); Parker (1984);
Giraldeau and Livoriel (1998); and Johnstone (1996). The point here is that free riding is a social strategy to be used selectively. Free riding individuals—especially active free riders—can change their social strategy as constraints change. Despite a motivation to free rider, in some cases free riding may not be a wise choice of strategy. This should be kept clearly in mind while performing and interpreting any empirical study of the collective action problem. Kurtzman and
Houser (2001) make a distinction between strong free riders, which composed about 28% of their sample; conditional cooperators of reciprocators, which composed roughly 29% of their sample; and strong cooperators, which composed nearly 25% of their sample. The remaining individuals in the sample could not be classified into any of these three categories.
Passive Free Riders
We have defined passive free riders as individuals unable to contribute to the public resources that they benefit from. Passive free riders include individuals who are too young or too old to contribute, or who have some other mitigating factor that prevents them from contributing to the public good even when they desire to. Passive free riders are quite common in all societies, and are universally tolerated. Passive free riders often rest on their accumulation of social capital from past contributions; or they are tolerated due to contributions that they are expected to have sometime in the future. This is noteworthy, in that the cost of raising children in real terms is often greater than the input they will supply in real terms. Yet, we tolerate them nonetheless.
This is because children are considered a good in and of themselves. Children contribute richly to our lives and cultures, but they are a commodity in the mere sense. Passive free riders do not necessarily have to have made, or will have to make, some contribution to the public good. In 81 animal societies, such individuals would die quickly, and would not place any considerable burden on the public good. Individuals who have a disability that prevents them from contributing at any level are often tolerated in human societies, however. There is in such cases no expectation of proximate contribution, but there exists an assumption that had the person the ability to contribute, they would have done so.
Passive free riders may be gross or subtle. Examples of a passive subtle free rider include individuals who have retired from an occupation, yet remain part time employed. Students who work part time jobs while in school are similarly passive subtle free riders. Again, though subtle free riding is more socially desirable from an economic point of view, passive gross free riders are often tolerated, and in some cases revered. As noted before, the lines between gross and subtle cheating need not (and perhaps are usually not) discrete. Passive free riders are present in every society. They are often great in number—certainly more common than their active counterparts. It is easy to see why they would be generally more tolerated than their active counterparts. I predict that they will also have higher status than active free riders.
Passive free riders are also much more visible than active counterparts. In fact, they are so common that they are usually overlooked as free. And yet, like their active counterparts, passive free riders perform functions that keep groups and aggregations tending toward a state of allostatic equilibrium.
Some individuals have objected to this distinction on the basis that forethought and a conscious will to cheat are necessary in order to be defined as a free rider. I vehemently oppose this argument. First, there is no reason that consciousness needs to play any part in the discussion. I have offered a number of examples of animals that exhibit the behavior that is defined as free riding. Many or most of these animals are arguably without enough 82 consciousness to choose a free riding strategy. There is no less of a reason to suppose that free riding is an adaptive social strategy that is built in to the repertoire of many social species. For my part, until we fully understand what consciousness is and what is necessary for it to emerge, we have no reason to include it in the discussion. It is the behavior of free riding that is of concern here, and that undoubtedly warrants the distinctions made herein.
Active Free Riders
Active free riders are individuals who are unwilling to contribute to the group or aggregation. These are individuals that pursue a social strategy that is seemingly at odds with the dominant cooperative strategies. However, as I have already shown, such strategies exist only when embedded in the framework of a larger cooperative gestalt. Free riding strategies are both competitive with, and complimentary to, cooperative strategies.
Current theorizing about active free riders suggests that they are deleterious to the solidarity of the group or aggregation. This is based upon erroneous assumptions about human nature, and the belief that there exists no complimentarity between free riding and cooperation. I will, however, argue, that free ridership—and especially active free ridership—forms a cornerstone of group solidarity. In other words, free riders perform functions that increase the solidarity of the group.
Active free riders include such individuals as welfare recipients, who make little or no ostensible contribution to the society in which they live. Occasionally, of course, active gross free riders will slide into subtle free riding, especially if they have been sanctioned by their conspecifics, or if constraints within or between groups has changed. Such changes disrupt the equilibrium of the group and force adjustment toward a new equilibrium point. This will be taken up in more detail in the following two chapters. The shift from gross to subtle active free riding 83 does not represent a shift in social strategy. Indeed, the motivation remains the same. It does, however, represent a shift in the action that is used to pursue such a strategy over time. Indeed, from an evolutionary point of view, it seems likely that the ability to shift from gross to subtle and back would serve the active free rider well, and would be selected for.
Active free riders are predicted to be tolerated much less in a group or aggregation than passive free riders. However, as we have seen, whether they are actually punished for their noncooperation depends upon the resources and individual tolerance levels of the individuals in the group or aggregation in which the free rider resides. Active free riders will have lower relative status in the group than both cooperative group members and passive free riders, whether they are punished or not. Of course, in either passive or active free riding, subtle free riders will have higher status than gross free riders.
Active free riders are also fewer in number than passive counterparts, and that number will be frequency dependent upon the number of cooperators in the group, the availability of resources, and other exogenous and endogenous constraints. Generally, the larger the social group or aggregation, the greater the number of active free riders. This is consistent with Olson’s
(1965) prediction that free ridership increases with increasing group size.
There is a fine line between active and passive free riders in some cases. For instance, at what point do we truly say that an individual is unable to contribute. It is not unknown for an individual who is receiving disability payments to claim the inability to work, and yet takes his children to the park and pushes them on the swings. Is the inability to work genuine; or is it merely the fact that the individual is unable to contribute as he has in the past? Is there, perhaps, another way for the individual to contribute? These are stormy ethical questions, but important ones. They amply demonstrate the nondiscrete nature of the categorizations outlined here. It 84 demonstrates as well the contextual nature of definitional terms such as ‘contribution,’
‘inability,’ and ‘unwillingness.’ A former brick layer who injures his back and collects disability payments, claiming his inability to work, very likely means one thing—the inability to work at his former occupation. The term as used by assessors of the disability likely means something else entirely—the inability to work at any job in any capacity. This confusion is certainly part of the reason why so many lawsuits are filed by individuals against the government when those individuals are refused disability benefits. The government sees many of them as active free riders, while the individuals themselves claim to be passive free riders.
The Use of a Typology of Free Riders
Clearly, different kinds of free riders exist. I have identified at least four. I believe that such a typology is not only theoretically useful, but empirically useful as well. Previous studies have treated all free riders the same. For instance, even in Hardin’s (1986) tragedy of the commons, the story can very different endings based upon the type of free rider that we assume.
It is likely that a passive free riders who adds an animal to the herd because he faces imminent starvation will be seen and dealt with very differently than an individual who adds to the herd for no reason other than greed. Similarly, a group member who actively free rides on a task is likely to be thought of very differently than a member who passively free rides due to illness.
Adding an understanding of the different types of free riders—both in action and motivation—will help to specify future research and hopefully eliminate many of the disparities present in the current literature. A typology such as this gives us the tools and opportunity to standardize and more clearly define research in this area. It also offers us a means by which we might more clearly understand the complexity of free ridership, and how it functions in various groups and aggregations to maintain or increase solidarity. 85
Chapter Summary
In this chapter, I have distinguished between four different types of free riders. I have argued that we may distinguish free riders on the basis of motivation and degree of behavior. I have argued that different kinds of free riders will be perceived differently within the group; and therefore tolerance levels will differ with the differing kinds of free riders. I have argued that the utility of this approach is both a clarification of the nature and degree of free riding as well as a clarification of many of the disparities between empirical research projects. 86
CHAPTER SIX
SOCIAL ALLOSTASIS
“Every great advance in science has issued from a new audacity of imagination.”
--John Dewey 87
Chapter Synopsis
In this chapter, I discuss the concept of social allostasis. I begin with a general discussion of the notions of flexibility and redundancy, and their relations to the success of living systems.
This builds on the discussion of embedded rationality and equilibrium in that flexibility and redundancy are two general mechanisms by which organisms achieve equilibrium in a dynamic environment. I offer several examples of the way in which these mechanisms work to achieve a state of equilibrium. This segues into the primary discussion of allostasis, which suggests that there are stable set points of equilibrium that can be adjusted to adapt to a changing environment.
Normally seen as a genotypic trait, I link this discussion with what has been said about autopoiesis and argue that groups also maintain an allostatic equilibrium. This is accomplished largely through the general mechanisms of flexibility and redundancy. More specifically, the tendency toward an equilibrium state is accomplished by an adjustment in the frequency of social strategies relative to one another.
I then apply this foundational argument—that groups, like individual organisms, must adapt to a changing environment, and do so through the use of mechanisms that allow the equilibrium point to shift with a changing environment—to free riders. I argue that free riders operate as a specific mechanism for allostatic adjustment. Free riders perform this task by performing any or all of three latent functions. Free riders may protect high status group members from individual risk; or they may increase the status and power of high status individuals; or finally increase group interdependence by increasing the group’s (qua group) uncertainty of the future. I conclude the chapter by offering several animal and human examples of this phenomenon. 88
Flexibility and Redundancy
Experiments with symbiogenesis, such as those conducted by Barricelli (1962), demonstrate the emergence of organized properties from complimentary, but interrelated parts.
Barricelli (1962: 100) writes:
This tendency to act on any thing which can have importance for survival is the key to understanding of the formation of complex instruments and organisms and the ultimate development of a whole body of somatic or non-genetic structures.
Dyson (1997) argues that translation of these ideas from genotype to phenotype is warranted. He argues that such a translation offers an explanation of how living systems provide the flexibility and redundancy necessary to survive in a dynamic, and often hostile, environment.
In fact, it is precisely flexibility and redundancy that are the goals of phenotypic success.
It should be emphasized, as Tinbergen (1960) reminds us, that natural selection, whether operative on phenotypes or genotypes, results in a balance between numerous mechanisms that serve to aid the species in survival. No mechanism can be developed to an extreme (Gould
1977). It must be tempered by an alternative, though not necessarily functionally opposite, drive that is in turn tempered by the first. It is this balance of forces that creates behavioral plasticity.
Tinbergen notes that many of the bifurcations of behavior are caused by competing motivations, and the mechanisms that serve them.
Indeed, flexibility of behavior, like flexibility of form, is a hallmark of evolution.
However, this flexibility occurs within specified limits. Flexibility allows the organism to offer different behavioral responses to similar environmental stimuli. In either case, the advantages are evident. It is certain that some bifurcations of behavior will be quickly and permanently extinguished; for they will be responses that are inappropriate to the environmental context. 89
However, the point is merely to illustrate how behavioral plasticity creates net advantages within a dynamic environment. Just as genotypically, it is clear that mechanisms must balance, so phenotypically must populations find balance in their dynamic environment (Gould 1980). This is the lesson of Malthus (1798), who emphatically argued that “in no state that we have yet known has the power of population been left to exert itself with perfect freedom” (21). For
Malthus, this balance is left primarily to the countervailing forces of population and food supplies; yet his statement might be applied to any number of situations where competing forces operate simultaneously.
One example of this that is particularly relevant is the evolution of altruism, and its presumed antagonist—free riding. William Calvin (2002) argues that even if a single hunter was able to kill a big game animal, it would be too much for the single hunter and his family to eat.
The best way to get rid of the meat is to give it to others, with the hope that they will reciprocate in the future. There is, however, a countervailing force that carries substantial risk. There is no guarantee that anyone to whom you give the meat will share with you at a later date. As already pointed out, this phenomenon has been documented in societies such as the Hadza and Hiwi.
Indeed, the propensity to free rider is at least as strong as instinct as the propensity to share (Calvin 2002; McElreath et al. 2003). These two countervailing forces operate in tandem.
It is not, I think, appropriate to view them as opposites. Rather, they should be considered complimentary. Consider that the contrary to altruistic sharing in the absence of sharing. There is no implication necessary, a priori, to attach the absence of sharing with the behavior of free riding; nor can we necessarily attach an absence of free riding to the behavior of contributing to the public good, exchanging, cooperating, or any other behavior. Rather, the two forces—the propensity to participate in the creation of a common good, and the propensity to free ride—are 90 forces that compliment one another. They are frequency dependent traits, mutually reinforcing strategies for survival in a harsh social world. As Brown (1983) implies, frequency dependent traits can be evolutionary stable strategies.
Tinbergen (1960) offers several examples of these competing but complimentary forces.
One noteworthy example comes from several species of terns (Sterna fuscata; Sterna albifrons;
Sterna hirundo). In these terns, observations have been made of competing but complimentary drives between egg retrieval and incubation. If an egg is removed from the nest and placed within view of the tern, the bird will stare at the lone egg for some time. Eventually, it will walk over from the nest until it is nearly directly above the lone egg. Then, it will begin to move the egg a little closer to the nest. However, as soon as the tern again spies the nest, it will abandon the lone egg and return to incubating. Then, it will once again see the lone egg, and reluctantly leave the nest, move the egg a bit closer to the nest, and then frantically return to the nest. These competing drives—one for the safety of the lone egg, and one for the safety of the eggs in the nest—tend toward an equilibrium state as the tern ensures that both sets of eggs are safe and remain warm. These two behaviors are competing, yet complimentary, and ensure the optimal non-zero outcome. These behaviors are examples of both redundancy (in the case of the incubation behavior) and flexibility (in the retrieval behavior).
Flexibility and redundancy operate as competing but complimentary behaviors that tend toward equilibrium within an organism or population. Flexibility, in the social sense, refers to the ability of a group member to change his strategy in the group such that his own interest is optimized. That is, flexibility is assumed for individuals whose social ties allow them some degree of choice as to the way in which a particular interaction will take place. Thus flexibility is scaled by degrees within a specific social context. 91
Redundancy refers to individuals who maintain a nonoptimal social strategy. Redundant individuals can include, but are not limited to, individuals who copy the roles and functions of others within the group. If there were an opportunity to change, such individuals would be better off to do so—to occupy some unfilled niche—rather than merely be cast in the shadows of others. Redundant members who copy a filled role I shall call copy-cats. Redundancy may also take the form of an individual who pursue a social strategy in common with someone else.
Both redundancy and flexibility are unstable social patterns if taken individually. Too much redundancy inhibits and sometimes prohibits growth and novelty within the group, which prevents the group from efficient adjustment to constraints. Similarly, too much flexibility threatens the group in that there can exist no normative rules or social patterns by which to maintain solidarity within the group. Additionally, too much flexibility leaves no room for the impact of experience. If every situation is taken as novel, there is no adequate or efficient means by which the appropriate responses might be discovered.
These two strategies, however, compliment each other such that they become mutually reinforcing and robust against constraints. Some flexibility is indeed essential, in that emergent constraints can have no precedent. Past behaviors may not be adequate to respond appropriately to the constraint. Some redundancy is needed as well, since constraints often put pressure on groups to expand or contract. Contraction implies that some positions will be eliminated.
Redundancy compensates by having a member who can fulfill the same role as the eliminated member. Expansion implies the addition of positions or roles. Redundancy is useful in such cases in that redundant individuals offer the possibility of assuming the new role or position.
Evidence for the complementariness of these forces can be extrapolated from Von
Neumann’s expanding economic model. Von Neumann showed that “goods are produced not 92 only from ‘natural factors of production,’ but . . .from each other.” (1) In other words, equilibrium is dependent upon growth (Dyson 1997). Growth offers both flexibility and redundancy, and is in turn stimulated further by those same forces. These forces are necessary to maintain an equilibrium state. Indeed, the lesson that natural selection teaches us is that if a trait serves a useful function, it survives (Gould 1980; Dyson 1997). Both copy-catting and free riding represent social strategies that will tend to compliment one another to maintain group stability.
I am not suggesting that these are the only two forces that operate within groups or aggregations. Nor am I suggesting that all groups will have free riders or copy-cats. For example, it would be difficult to find either one in a dyad. What I am suggesting, is that as the group or aggregation increases in numbers and size, maintaining an equilibrium state gets progressively more difficult. Thus, these forces, which may be sufficient but not necessary to move the group toward equilibrium, emerge as a means by which the group can maintain enough flexibility to respond to constraints, and enough rigidity to guide the group toward optimization. As in the case with the nesting birds struggling to balance incubation with egg retrieval, the forces at work tend to find a point of optimization. The consequence of these two evolutionary forces is a movement toward equilibrium in which cooperators and free riders coexist (Fehr and Henrich
2003).
Social Allostasis
Walter Cannon’s (1932) treatise on homeostatic systems was an important discussion of the way in which living organisms maintain equilibrium in a dynamic environment. Cannon argued that necessary bodily functions such as water retention and blood pressure were regulated internally by the body by responding to the deviation of that system from a set point. As the deviation from the set point increases, motivational systems are activated to induce the organism 93 to respond appropriately with the goal of returning the system to the set point. The greater the deviation from the set point, the stronger the motivation to return to the set point.
A commonly used metaphor to illustrate homeostasis is a common household thermostat.
The thermostat is adjusted to a set point—the desired temperature. As environmental change occurs—a snow storm, for example—that disrupts the set point, the thermostat responds by heating the room to the set temperature. The furnace provides a wave of warm air that returns the room to the prearranged set point.
Cannon argued that these motivational systems were an important survival tool that provided internal stability to the organism amid dynamic environmental circumstances.
Homeostatic mechanisms provide balance in a changing world. This balance ensures that the organism remains at optimal efficiency.
Shulkin (2002) expanded on Cannon’s original hypothesis by noting that the set point of various mechanisms may change over time, or with fluctuating environmental circumstances.
For example, Shulkin notes that a thermostat, once set, does not have to remain at the set temperature forever. It changes as necessity warrants. Shulkin also notes that blood pressure, rather than having an unchangeable set point, has variable optimums at different times of the day. In the morning, as the organism first awakens, lower blood pressure rises with the rising metabolic rate. The set point optimum is readjusted accordingly. Shulkin named this phenomenon allostasis, and argues that allostasis is a more accurate model of equilibrium for most systems.
Allostatic mechanisms provide a means by which optimality is utilized to maintain the organism’s viability. Although neither Cannon nor Shulkin hypothesized the extension of these concepts beyond the organismic level, it is not a stretch, given what has been said about 94 autopoiesis, to argue that the same fundamental forces that apply to individual organisms in a social universe apply as well to aggregations of organisms. Indeed, complex organisms are merely social groups of individual cells working in balance with one another. Insofar as a group or aggregation functions within the social universe as distinguishable entity, it may be considered an organism within the greater social phylogeny. Just as an organism is comprised of a series of interrelated unit-parts, which in turn are comprised of smaller interrelated unit-parts (and so on, ad infinitum), so an aggregation is made up of smaller, interrelated unit-parts known as individuals. We can infer that homeostatic and allostatic mechanisms are mirrored in groups.
Wynne-Edwards (1962) hypothesized just such an extension when he wrote that animal species
“control their own population densities and keep them as near as possible to the optimum level for each habitat that they occupy” (9). Set points exist in groups that serve to optimize the group within the given constraints. This set point, as Shulkin would point out, will change with changes in constraints. Both social homeostasis and social allostasis are hypothesized to be key elements in the maintenance of group solidarity over time; and key as well for the development of solidarity in aggregations.
As I have already argued, no social strategy is stable by itself. Each strategy depends on another to create an equilibrium state that prevents either strategy from overrunning the group, or from being eliminated entirely. One piece of evidence for this is the nature of the flexibility of social strategies. People are not born free riders any more than they are born cooperators. It is certainly true that there exist some innate tendencies toward one strategy over another, but social context and constraints will be a greater determinant of social strategy at any particular moment; and will predict when those strategies will change. 95
Ridley (1996) relates the story of one woman who suddenly changed strategies, and the resulting effect. On August 8, 1993, a woman in Ireland won the national lottery. Her husband died within a month, and there were no heirs to the fortune. Known as a generous and dedicated woman, she was expected to share her good fortune with the other 450 people who lived in her community. However, the woman refused to share her winnings with the community, and moved away.
In this case, the environmental change was winning the lottery. Suddenly, the environment became richer—at least for one person. This precipitated a change in strategy on the part of the woman. But the environmental change affected not only her, but the rest of the village as well. Suddenly, the equilibrium had been disturbed. For the lottery winner, the strategy now turned to one of selfishness. Group interdependence was no longer necessary, and her participation in the needs of the village no longer evident. Equilibrium was restored only after the woman left her village.
The change in constraints for the lottery winner precipitated a strategy change not just for her, but for the rest of her group as well. While she became selfish, they became potential free riders. Of course, had the woman stayed and shared her new found fortune, equilibrium may still have been disturbed. As it was, the sudden change in constraints moved the system out of equilibrium; new constraints (i.e. the woman leaving the group, and thus severing her interactions with the village) restored equilibrium with a new set point.
More examples of a change in equilibrium is reported by Sih (1980; 1982; 1984; 1998).
In one notable example, Sih (1998) studied predator-prey responses in a three trophic system. Sih demonstrates that the system tends toward an equilibrium state in that prey congregate and 96 forage in areas where the supply of food is balanced by the population density of predators.
Predators tend to congregate in areas with more prey;
The Functions of Free Riders
Imagine a group with several free riders. Suddenly, the prey avoids areas with higher predation risks. The result is an equilibrium point where “prey can adaptively balance the conflicting demands of feeding and avoiding predators” (Sih 1998: 234). As constraints change, the response of the aggregation or group must change as well to accommodate the new constraints. In situations where food supply or other resources is limited, free riders serve to tend the group toward a new equilibrium point by being differentially selected against. It is well known that predators overwhelmingly choose prey that are only marginally fit
(Zahavi and Zahavi 1997). Individuals who exist on the periphery of the aggregation, such as those in our example of swallows fleeing from a hawk, are differentially selected against. Free riders fit into this category, irrespective of their typology. Free riders are maintained in a group by subsisting on the collective good of cooperative group members. When collective resources become scarce, free riders are forced to change their strategy or face extinction. In this way, free riders act as a buffer between productive group members and death. It is certainly true that having a productive, high status member of a group or aggregation die is more damaging to the welfare of the group (qua group) than if a lower status individual—such as a free rider—dies.
Free riders indeed bear the brunt of change when the group is not at a state of equilibrium.
These changes occur when the equilibrium state of the aggregation is disrupted by exogenous or endogenous changes in constraints. Free riders serve the latent function of tending the group toward a new equilibrium state by changing to a more supportable strategy, or by differential selection or predation. Free riders also become more numerous as collective goods 97 increase. In this case, free riders tend the aggregation to an equilibrium state by increasing the uncertainty of the future. As free riders consume resources from the aggregation, future interdependence is assured, since resources previously existing have been compromised by the existence of the free rider. As resources increase, individuals within the group—and particularly individuals with greater access to abundant resources—tend to discount future interactions
(Axelrod 1984; 1997). Termed the discount parameter, the greater the discount, the less perceived need for interaction with aggregational conspecifics. Free riders serve the purpose of driving down the discount parameter, and thus increasing future uncertainty and increasing aggregational interdependence.
Evidence of these trends can easily be seen in social insects. Ants, along with
Hymenoptera, including bees, move toward an equilibrium state when environmental circumstances change. Colonies of bees in a food rich environment contain a measure of free ridership—individuals who tend not to look very hard for new food sources and rarely travel very far from the nest (Bloom 2000). This strategy quickly changes, however, when food becomes scarce. As food sources dwindle due to overexploitation or competition, free ridership in the hive becomes less and less tolerable. Formerly free riding bees suddenly become quite active foragers (Bloom 2000). When a colony runs out of resources, it splits and attempts to find new locations rich in food and heavily protected, to build their new nests. These behavioral changes represent a fundamental change in equilibrium.
Bloom (2000) also notes that large ant colonies, with high populations, are able to divide labor, thus increasing the efficiency of the colony. In such cases, free riders are common, as are copy cats, since individual ants are expendable. Small colonies are unable to specialize. Each member is interdependent upon the others. Everyone performs multiple tasks. There is no room 98 for free ridership to emerge, since the whole colony might perish. There is no degree of expendibility in a small colony.
Yet another example comes from studies of the Pied wagtail (Motacilla alba). This species of bird defends winter riverside territories, foraging on insects that surround the water.
Intruders—wagtails without territories—often land in claimed territories. Often times, these intruders are driven off by the defending owner. However, there are instances when the intruders are tolerated in the territory:
On days of high food abundance, when intruder pressure is greatest, owners tolerate satellites. . .owners increase their feeding rate by this association, because the benefits gained through help with defense outweigh the costs incurred through sharing the food with another bird. On days of low food abundance, when an owner would have a higher feeding rate by being alone, it evicts the satellite from the territory. (Davies and Houston 1981: 157).
Intruders have no foraging territory of their own, and must find and attach themselves to a resource rich territory. The owner of the territory will tolerate the free rider only as long as resources permit, or until tolerating the free rider is no longer to his advantage.
Experimental studies on humans also provide evidence for this point. Fishbacher, Gächter and Fehr (2001) demonstrate that the breakdown of cooperation in iterated public goods experiments can be neatly explained by the dynamics of the interaction between strongly reciprocal strategies and selfish (i.e. free riding) strategies. In other words, opposing forces of reciprocity based cooperation and free riding converge toward balance in aggregations. Here, applying the typology of free riders would be particularly illuminating. For example, strong reciprocity would perhaps be less influential, or even nonexistent, toward passive free riders than active free riders. Strong reciprocity, which acts as a moral compass in the sphere of collective action, would certainly account for the varying motivations, if they were able to be ascertained. 99
Free riding is a common phenomenon in many social species. It is not surprising that reciprocity strategies, and related behaviors, have evolved right along with free riding strategies.
It is, in fact, that the two categories of behavior, however manifest, evolved side by side, in a struggle for equilibrium. The two behavioral strategies balance each other, providing flexibility and redundancy to the aggregation. Even as simple a creature as a polycheate worm is able to assess whether a conspecifics is cheating, and adjust its behavior accordingly (Sella 1985; 1988;
1991).
In the following chapter, I will demonstrate one possible model of frequency dependent equilibrium between cooperators and free riders, and show how this model allows for the flexibility and redundancy necessary to maintain an allostatic equilibrium state.
Chapter Summary
I began this chapter with a discussion of redundancy and flexibility, two concepts that operate as mechanisms to facilitate group survival. Specifically, I argued that flexibility and redundancy are two means by which a group (qua group) adapts to a dynamic environment by tending the group toward an equilibrium state. I subsequently argue that group equilibrium is allostatic in nature, and that the tendency toward an equilibrium state is accomplished by an adjustment in the frequency of social strategies relative to one another.
This is accomplished in part through the latent functions that free riders perform in the group. I have argued that free riders may perform any one or combination of three distinct functions that serve to increase group solidarity. First, free riders increase or validate the status of high status group members. Second, free riders protect productive group members from the incurrence of individual risk. Finally, free riders drive down the discount parameter, thus increasing the uncertainty of the future and increasing group interdependence. 100
CHAPTER SEVEN
AN EQUILIBRIUM MODEL OF SOCIAL ALLOSTASIS
“A scientific hypothesis is elegant and exciting insofar as it contradicts common sense.”
--Sir Charles Lyell 101
Chapter Synopsis
Having thus laid the groundwork for a radical shift from methodological individualism to a functionalist approach, in this chapter I begin by offering various examples of the three functions of free riders I have articulated in previous chapters. I discuss such examples in terms of group selection theory, a marginal force of Darwinian selection. I assert that many groups— and in particular human groups—fulfill the criteria for arguing group selection as a strong evolutionary force.
I then offer one possible frequency dependency model of allostatic equilibrium. Adapting a model from Giraldeau and Livoreil (1998), the model demonstrates the logic of the embedded nature of the two equally rational social strategies discussed in previous chapters. I show how two seemingly competitive strategies (profit maximization and probability maximization) can exist simultaneously in an equilibrium state.
The Framework
Within a framework of a larger cooperative group, free riding is a probability maximization strategy. Conversely, cooperation is beneficial only to the extent that the benefit of cooperating is greater than the cost entailed in the cooperative venture. We would suspect, as
Olson (1965) predicts, that highly cooperative groups are particularly vulnerable to free riders.
This cannot be said for mixed strategy groups, in which interactions between free riders results in a non-optimal state (Taylor 1987); and where interactions between cooperators and other cooperators, or between cooperators and free riders, become optimal (Taylor 1987).
There are situations in which cooperation is either individually unstable or individually inaccessible, Elster (1976) notes. Similarly, the Prisoner’s Dilemma game illustrates the possibility that rational action can lead to a non-optimal outcome (Taylor 1987). However, a 102 combination of cooperative and free riding strategies provides for a reduction in non-optimal outcomes by balancing profit maximization and probability maximization strategies, which are embedded in each other.
The groundwork I have laid in the first six chapters has attempted to provide not only new assumptions upon which we might base a theory of free riders that is consistent with the available evidence, but also to provide conditions under which the strategies that facilitate free ridership are embedded within larger cooperative strategies to tend the group toward an allostatic equilibrium state. The embedded nature of these competitive, but complimentary, social strategies merge toward a developmentally stable strategy that is mutually beneficial for all individuals, regardless of strategy.
While the cooperative members of an aggregation provide the conditions under which free riding might flourish, so the free rider facilitates cooperation between cooperative aggregation members. Indeed, the success and stability of each strategy is dependent upon the success and the stability of the other. Free riders facilitate the success of the group in three main ways. These correspond to the three categories of interaction that may be found in a group: individual to individual relations; individual to group relations; and group to group relations.
Again, it should not be assumed that any free rider will necessarily perform all of these functions. I assert simply that tolerated free riders are tolerated because of the effect they have on group relations. Also, it is important to note that it is the relationship between the free rider and conspecifics that matters, rather than the individual free rider. To put it another way, it is the role and action of free riding that is of concern, not the characteristics of any individual free rider.
The functions that free riders play in the group are certainly not manifest. They are latent, and 103 require no forethought or consciousness. It is necessary only that the social organism has the behavioral plasticity to adopt such a strategy.
Free riders protect the group from the incurrence of disproportionate risk. A benefit of sociality is that for every member that is added to the aggregation, the probability of incurrence of risk of any individual member of the group is reduced. Free riders weight this probability reduction so as to make it more likely that the free rider will incur risk, and less likely that more productive and cooperative group members will incur the risk. As I have already shown, it is well documented that predators overwhelmingly prefer to hunt marginal members of an aggregation (Zahavi and Zahavi 1997). Free riders are marginal members to the degree that the remaining members see them as less valuable to the maintenance of the group than other conspecifics; and to the degree that their status is tied to their manifest contribution to the group.
The loss of several marginal members of a group will usually not be as debilitating to the group as the loss of one highly cooperative (core) group member. Wright (2000) argues that diffusion of risk is a key element to non-zero sumness—a state of equilibrium.
One example of diffusion of risk can be found in Turnbull’s (1962) classic on the
BaMbuti of Africa. Turnbull relates the story of Cephu, a known free rider. Cephu was seen to always build his hut on the edges of the camp, close enough to be seen as part of the group, yet certainly not a core member. Cephu was tolerated, and allowed to sit by the fire with other men—afterall, he was still a part of the group. However, it is certainly clear from Turnbull’s insights that the loss of Cephu would not have been a devastating one to the group. The loss of a more skilled or more aggressive hunter would certainly have caused greater distress than the loss of a marginal hunter like Cephu. 104
Relatedly, free riders also provide status validation for core group members of high status. In the competition for status within a group, an individual who can advertise his status in the group benefits by an ostentatious display. One way in which this is accomplished is through the maintenance and care of marginal conspecifics. But the benefit extends beyond mere advertising of one’s status. A high status individual who is able to expend resources to maintain the livelihood of others has ready allies when his status position is challenged. A loss of status— and a loss of the corresponding resources that accompany a loss of status—constitute a threat to those individuals whose livelihoods depend upon the other.
Yet, why should a free rider come to the aid of a higher status individual? Would it not be preferable to usurp the status of your benefactor? Certainly, and this often happens. However, such coups are usually short term, as the marginal member cannot maintain such power for long.
Sapolsky (2001) documents such a case, and the unpleasant result. A troop of baboons had lost their alpha male, and the competition for high status created highly unstable and chaotic situations for the troop. Six males simultaneously vied for control, including one formerly marginal member. Sapolsky notes that many of the lower status individuals vying for high status formed coalitions, most of which broke apart within minutes when one coalition partner betrayed the other. The hierarchy shifted rapidly in the troop for some time, but eventually, the marginal member found himself in the alpha male position. This lasted only a short time, however, as the marginal member was promptly overthrown by a rival.
More often, it behooves the marginal member to support the status quo—that is, wanting to maintain the hierarchy in the present stable state so as to ensure the greatest probability that the current benefactor will remain. There is, after all, no guarantee that any other individual will 105 become a benefactor. Similarly, the benefactor finds it advantageous to maintain free riders if possible as a demonstration both of his power and status, as well as his generosity.
Kompter (2005) echoes a point made much earlier by Bailey (1971) that gifts are often given as a means to advertise a superior status or moral position. Such gift giving is even more impressive when there is no expectation of reciprocation of a physical gift from the receiver.
Note that this is not the same as altruism, in which there is no expectation of reciprocation in a situation in which the costs to the altruist exceeds the benefits derived, if any.
Maintenance of free riders is not just advantageous for an individual. Groups that are able to maintain free riders can regularly advertise the fitness of their group to competing groups. The number, health, and status of free riders offer a good indicator of group fitness, since they essentially subsist on surplus resources.
Potlatches offer insight into such matters. Practiced variously in New Guinea (Ridley
1996) and in the Pacific Northwest (Ridley 1996; Wright 2000; Winston 2002; Kompter 2005), the potlatch stands as the ultimate advertisement of status, wealth and power for an individual and group. For example, among the Kwakiutl of western Canada, individuals bought and advertised status by giving away or destroying everything they had. The more that was destroyed or given away, the higher the rise in status. Such ostentatious displays are clear indicators of the power of the individual to accumulate wealth and power. Potlatch ceremonies were also held between tribes, to advertise the strength and wealth of the tribe as a whole.
A more concrete example pertaining to free riders is polygyny. Polygyny—the practice of having multiple wives—is the most sanctioned martial practice cross culturally. However, even a cursory examination of societies reveals that polygyny is not often practiced. The high cost of maintain multiple wives is relegated to a few very powerful or wealthy individuals. While 106 polygyny may be accepted within a society, it is by far less frequently seen than monogamy.
Very few individuals can afford the added expense of maintaining a family with multiple wives.
This is because, while the expenses incurred are the same for each wife, the benefits of each wife are subject to diminishing utility.
Finally, free riders facilitate group interdependence by increasing the uncertainty of the future. Hechter (1987) argues that the production and maintenance of public goods is necessary to maintain group solidarity. Public goods are surplus resources that are available to members of the group. For Hechter, the creation and use of public goods is seen as the basis for emergence or construction of group norms. What is often missed, however, is that public goods may also serve to reduce group solidarity. The uncertainty of the future declines as surplus public goods accumulate. As Axelrod (1984) notes, uncertainty about the future encourages dependence and cooperation among conspecifics. Free riders increase the uncertainty of the future by their unilateral use of public goods without subsequent contribution. This reduction in surplus, whether real or perceived, makes future cooperation with conspecifics necessary.
For an illustration, we shall again turn to Turnbull’s observations of the BaMbuti.
Turnbull (1962) writes: “Co-operation is the key to Pygmy society; you can expect it and you can demand it, and you have to give it.” (124). This is because there are always individuals, such as Cephu in our earlier example, who are willing to free ride off of the labors of the group. Free riders demand continued and extended cooperation from the remaining group, since the free riding activities may reduce the probability of a successful or efficient hunt. Cephu’s loud ranting forced the remaining tribe members to hunt even more effectively:
Above all the clamor, and in the process of re-coiling the nets and assembling for the next cast, a disgruntled Cephu appeared and complained he had had no luck. He looked enviously at the sondu and the sindula, but nobody offered him a share. . . .We went on for a mile or two 107
and made another cast. Once more, Cephu was unlucky, and this time he complained even more loudly. .. .They were still arguing when they set off for the third cast. I had caught sight of old Moke and stayed behind to talk to him. . . .He said ‘Don’t follow them any longer; they will deafen you with their noise. Cephu will spoil the hunt completely—you’ll see’ (102-103).
Yet, despite the largely unsuccessful hunt, Cephu remained part of the tribe, and was even allowed to partake—though reluctantly—in the meager spoils.
Cephu’s actions had both a short term and long term effect on the tribe’s resources. The short term effect was a severe reduction in the spoils of the hunt.
The long term result was a redoubling of cooperative effort in subsequent hunts, particularly between the best hunters.
Within the framework of the theory outlined here, an increase in future uncertainty increases the probability that the free rider can find willing cooperators within the group, since interdependence is required; this increases the likelihood that the free rider can maintain a free riding strategy over time. In fact, all of these functions are excellent ways to ensure the longevity of a probability maximizing strategy.
The Re-rise of Group Selection
Another way to look at this problem is through the controversial topic of group selection models. Although widely rejected by biologists for more than two decades, group selection models were anticipated by Darwin. Initially, a considerable amount of evidence accumulated that while group selection existed, it was not a strong enough mechanism of natural selection to make a serious impact in the direction of evolutionary variability. However, many biologists, such as Sober and Wilson (1998), Dugatkin (1998) and Richerson, Boyd and Henrich (2003) have recently revived a somewhat softer form of group selection arguments. While group 108 selection is indeed generally a weak evolutionary force, there are conditions under which group selection may make a substantial impact as a force of evolution. Wilson (1998) elaborates:
Consider a behavior that, by itself, would be defined as strongly altruistic. This means that individuals who express the behavior increase the fitness of their group but nevertheless decline in frequency within their group. Even though the behavior is disfavored within groups, it can still evolve if there is a population of groups that vary in the frequency of altruistic behaviors. In this case, the frequency of the behavior in the global population is influenced by the differential contribution of groups, in addition to the differential contributions within groups (268).
Unlike traditional group selection models of the kind advocated by Wynne-Edwards
(1962), contemporary conceptions of group selection provide genetic models which partition variance into within- and between-group components (Dugatkin 1998). Furthermore, new group selection models alter the unit of analysis from the reproductively isolated deme to a population within which every individual feels the effect of every other individual (Wilson 1980). The consequence of such a shift is the implication that groups no longer need to be spatially or temporally isolated for selection to operate. Traits are embedded within the larger population
(Dugatkin 1998). Within this framework, some literature has shown that, contrary to the predictions of extant theories, free riders are socially attractive, at least in the short term (Christie
1970; Bochner, di Salvo and Jonas 1975; Cherulnik, Way, Ames and Hutto 1981). Cooperative and cheating strategies are seen as “a community of social strategies that play off against each other in a heterogeneous environment” (Wilson 1998: 267).
The core idea of the new group selection models is that cooperation can evolve even when it has a cost to the individuals performing it, if within-group costs are offset by some between-groups benefit such that the cooperative groups are more productive than the selfish groups (Wilson 1980; Dugatkin 1998). It is equally clear, however, that complete cooperation within a group is not a stable state (Taylor 1987; Boyd and Lorberbaum 1987; Lorberbaum 109
1994). In fact, there is no pure strategy that is stable in most situations (Farrell and Ware 1989).
Indeed, the point of Axelrod’s (1984) Prisoner’s Dilemma simulation was that the best strategy in an iterated game scenario is embedded within the choice of strategy of all other players.
This implies, of course, that while there exists no single strategy that is stable and robust, there can exist situations in which different (though not necessarily opposing) strategies can exist simultaneously in a group and be successful. In order for this to occur, the strategies must tend toward an equilibrium state (Taylor 1987; Reichert 1998; Giraldeau and Livoreil 1998).
Equilibrium states may rest on a variety of factors. The two most commonly cited forces driving equilibrium are frequency dependence (Giraldeau and Livoreil 1998) and differential maximization (Reichert 1998).
Frequency dependence refers to any situation in which the individual payoff for any strategy depends upon the number of individuals playing all existing alternative strategies
(Giraldeau and Livoreil 1998). Differential maximization refers to the nature of the strategy itself. Usually, two maximization strategies are cited. Profit maximization, as already discussed, refers to a strategy in which the value of the payoff is maximized relative to the cost of pursuing that strategy. Alternatively, probability maximization refers to a strategy in which the probability of receiving any payoff is maximized (Riechert 1998).
The free rider problem may be approached both as a problem of maximization strategy and as a problem of equilibrium. The existence and maintenance of free riders is an issue of both frequency dependence and differential maximization. I argue that free riders can exist in an allostatic equilibrium state with cooperative group members. In fact, I argue that free riders form a cornerstone of group solidarity by driving the group toward an allostatic equilibrium point.
This can be illustrated from a mathematical extrapolation. 110
An Allostatic Equilibrium Model
Take, for example, a social situation in which only two choices for interaction are permitted. You may play a strategy of cooperation (C), or a strategy of defection (D). We equate strategy C with a strategy in which each individual playing C is contributing to the public good, and likewise receives a payoff for such contributions. Strategy D is equated with a free riding strategy, in which each individual playing D is receiving a payoff of non-excludable public goods, but does not contribute to these good (i.e. is a gross free rider).
Thus, strategy D exists only if there are individuals playing C, since in order to receive a payoff, C strategists are necessary to accumulate a public good. Furthermore, the initial value of playing D is not set, but is similarly dependent upon the number of individuals playing C. Due to increased opportunities for exploitation, the greater the frequency of C strategists, the greater value of playing a D strategy. This is consistent with Olson’s (1962) prediction that the propensity for free ridership increases with increasing group size—as long as we interpret this to mean increasing frequency of cooperators.
The proportion of individuals playing C is x, and the proportion of individuals playing D is 1-x. Payoffs (W) of C and D are a function of x:
W(x|C) and W (x|D)
The value of any strategy is dependent upon the number of individuals using the alternative strategy. A group containing all D strategists is never stable, since D can never produce public goods. On the other hand, a group containing all C may be stable under certain strict conditions.
However, any D strategist in a pure C group can invade the group with enormous success. A group of all C strategists is not in equilibrium, since any single individual C strategist in the group can do better by unilaterally switching to a D strategy. 111
If we assume that all players have equal competitive abilities, a game with two alternatives C and D has a Nash equilibrium solution with the proportion of x^ of C when
W(x^|C)=W (x^|D) and when
pW(x|C)/dx|x=x^ < pW(x|D)/dx|x=x^
Thus the population will have x^; and any combination of C and D that leads to x^ is suitable as an equilibrium solution. However, not all solutions will be equally stable in a dynamic environment. Furthermore, we must not assume that this mixed solution may take only one form.
The population may be dimorphic—composed of specialized C and D individuals; monomorphic—where each individual plays C and D with probabilities x^ and x^-1, respectively; or polymorphic—where each individual plays his own frequency of C and D as long as within the population the average proportion of P corresponds to x^ (Parker 1984;
Giraldeau and Livoreil 1998).
Group size (G) interacts such that GD=(1-x)G individuals playing D will always defect and exploit non-excludable public goods (Taylor 1987) produced by Gc= xG individuals playing
C. We assume that cooperators always obtain a benefit for exclusive use (a) such that 0 Then x^ can be determined by setting IC=ID and solving for x^: X^=a/F+1/G 112 A pure C strategy will be stable if and only if it could obtain 2/3 or more of the resources before defection. For G=10, the fraction increases to 9/10 or more, making it increasingly unlikely to observe a pure C strategy as G increases. Consistent with previous predictions, we would expect individuals within the group to adjust their strategies (C,D) in such a way as to maximize payoff, or to maximize the probability of payoff. Since playing D provides substantially less I than playing C, but at a greater probability of payoff, we would expect free riders to generally be probability maximizers; while cooperative group members would generally operate under a profit maximization scheme. This is somewhat counterintuitive, in that one would be likely to think that free riders would desire a higher payoff at lower frequency. After all, this corresponds to the description of free riders as exploitative: enough attempts at exploitation will eventually reap substantial rewards. However, it should be noted that an exploited C player who loses a considerable sum of resources would be significantly more likely to remember the incident, and significantly less likely to interact with the exploiter again. Certainly, if the loss by C is greater than the benefits gained in status or risk reduction, the exploiter D will likely lose one individual to exploit. By contrast, the accumulation of many smaller payoffs with high probability of success is likely to be more easily tolerated by the exploited C player. If each C player is exploited in rotation, the time factor plays an even larger role in that the incidence of exploitation that results in a small loss is much more likely to be forgotten by the time the rotation ends. Németh and Tákács (2005, unpublished) show similar results using computer simulations. They demonstrate that cooperative strategies, such as teaching, can tend toward an equilibrium state with the addition of exploitative strategies. They show as well that exploitative strategies tend to remain only a selected proportion of the population, since their success relies 113 on cooperative strategies to exploit. This is consistent with the predictions herein, and with the mathematical model offered above. As previous noted, sociological literature on solidarity is typically divided into two general categories—utilitarian and affective. Up to this point, I have concentrated the discussion solely from a utilitarian perspective. In the next chapter, I give just due to affective theories of solidarity, and show how the theory developed here approaches affect of solidarity and free riding. Chapter Summary I began this chapter by offering several examples of the functions of free riders discussed in the previous chapter, and showing their applicability to group selection analysis. Then, I tied together the concepts from the previous chapters by offering one possible frequency dependent equilibrium model to show the embedded nature of free riding strategies and cooperative strategies, and to illustrate the stable nature of allostatic equilibrium in the face of variable constraints. The model demonstrates the logic of the embedded nature of the two equally rational social strategies discussed in previous chapters. 114 CHAPTER EIGHT FREE RIDERS AND EMOTIONS “Men love to wonder, and that is the seed of science.” --Ralph Waldo Emerson 115 Chapter Synopsis In this chapter I deviate momentarily from the discussion of free riders as a functional unit within groups to explore the role that emotions play in the tolerance of free riders. I begin the discussion with a call to abandon the notion of emotions as ‘positive’ or ‘negative’ to be replaced by the less loaded terms of ‘aversive’ and ‘hedonic’. I suggest that these terms better articulate the nature and functions of emotions in general. I then discuss eight of the identified thirteen emotions that have been associated with cooperative behavior. I suggest that the eight discussed herein are the only ones that bear directly on the theory at hand, and that none of the left out emotions are detrimental in any way to the theory. I argue that the function of emotions in tolerating free riders is dependent upon the nature of the exogenous and endogenous constraints of the group. I suggest that emotions play a role in determining the tolerance thresholds of group members that must react to free riders. In this way, I weave emotions into the general theory that I am presenting here. On Positive and Negative Emotions It is worth beginning our discussion of emotions with a short but critical analysis of terms commonly used in emotion research. While most scholars adhere to terms such as ‘positive’ or ‘negative’ to refer to emotions (that is joy, satisfaction, and happiness are considered positive emotions; while anger, jealousy and sadness are negative emotions) I believe this distinction to be unwarranted and theoretically unpalatable given what we currently know about emotions. Emotions have deep roots in evolutionary history (Darwin 1872), and have maintained their usefulness through millions of years of environmental dynamism. Applying such terms as negative or positive implies an understanding of the emotions’ functionality through evolutionary history. Specifically, positive emotions would be emotions that facilitated or 116 enhanced the reproductive success of a phenotype; while negative emotions would be emotions that were selected against due to their deleterious effects on reproductive fitness. If such a distinction were true, it would be nearly impossible to account for the continuation of negative emotions. However, it is obvious at even the most cursory glance that so called negative emotions have positive aspects that enhance fitness. For example, fear is generally considered a negative emotion, while satisfaction is considered a positive emotion (Lovaglia and Houser 1996; Panksepp 1998; Lawler 2001). Yet, fear can have positive consequences—fleeing from danger, for example. Likewise, jealousy, sadness, and other so called negative emotions often lead to positive consequences, not only in immediate context, but also in long term reproductive fitness. To the extent that emotions increase the reproductive fitness of the individual, they are useful and efficient tools, but they are neither positive nor negative—they are simply adaptive (Plutchik 1962; Hamburg 1963; Scott 1980; Dolan 2002). Kemper (1987) offers a less specific distinction. Kemper suggests that emotions are integrating or differentiating based upon the way in which expression of emotions increases or decreases attachment to the group. However, this approach is similarly problematic. Anger is often differentiating in that it creates less trust between individuals, and may damage group relations. However, there are also times when anger is integrating, in that it can also alert you to individuals who are likely to violate trust, or when anger leads to a moment of honesty between individuals and facilitates attachment. In short, Kemper’s distinction relies largely on the context in which the emotion occurs. Thus it is difficult to talk in any complex fashion about emotions using this distinction. A third distinction—and the one that I shall adopt here—is perhaps the most basic. In neuroscience, emotions are categorized as hedonic or aversive (Berridge 2000; Söderpalm and 117 Berridge 2000). Hedonic emotions are those emotions that are associated with approach behaviors, while aversive emotions are associated with avoidance behavior. The strength of this distinction is twofold. First, the distinction is clear to delineate within varying social circumstances in that approach and avoidance behaviors are nearly universal in application. The distinction does not rest on a hypothesized feeling state that is difficult to measure and quantify. Rather, the distinction is made on the motivations and actions that are taken. Also, behaviors that are hedonic or aversive are universal in the living world. They form the valence upon which all other categorizations are based. Unlike Kemper’s distinction, dividing emotions into hedonic or aversive centers around the behavior, which is causally prior to the consequences. The idea that emotions are motivation and action based, rather than consequence based is important in understanding the nature of free riders as engaging in an alternative, but equally rational social strategy. Consequence based emotions are unable to meaningfully inform us about the nature of the interaction beyond that they illicit the emotion that is observed. Motivation and action based emotions, however, offer a window into the nature of the interaction qua emergent phenomenon. This approach avoids another pitfall common to sociological approaches to emotion. Sociological approaches to emotions can understand emotions only as existing within a social framework. However, it is clear that not all emotions are social in nature. For example, one can easily think of incidences of frustration, anger, or fear that require no social interactions and no prior socialization. Using motivation and action based emotions, it seems possible to extract the emotion from the social context, and thus gain a more objective understanding of its use as an adaptive mechanism. How Many Emotions Are There? 118 In addition to the instrumental functions that have so far been the focus of the theory, free riders may also serve affective functions that serve to increase the solidarity of the group. Fessler and Haley (2003) argue that emotions can be understood as mechanisms designed to commit people to behaviors that are likely to lead to long term payoffs, and that curb the temptation for defection. However, such an analysis begs the question why natural selection has not eliminated the implicit handicaps, rather than constructing new compensatory mechanisms (Fessler and Haley 2003). One answer toward this question, which Fessler and Haley make no real attempt to answer, lies in the implicit assumption that there exists one rational behavior, to the exclusion of all other bifurcations of behavior. Fessler and Haley (2003) define rationality as that which serves to maximize subjective utility; however, they proceed with their analysis as though the utility were an objectively real pursuit of profit maximization. They further go on to admit that studies in behavioral economics show that individuals often demonstrate a willingness to incur monetary costs in order to reward partners who have cooperated with them in the past. As already mentioned, however, rationality may be conceived in a variety of ways. Probability maximization is at least as rational a strategy as profit maximization. This bears on Fessler and Haley’s dilemma in that we can now offer a reason why mechanisms for cheating, and the associated emotions, have not been eliminated through natural selection. Such mechanisms are necessary in strategies that are complimentary to the usually dominant profit maximization strategies. These handicaps, to use Fessler and Haley’s term, are not handicaps at all. They are simply mechanisms for an alternative rational strategy. Turner (2000) hypothesizes a complimentary adaptive function for emotions. Turner suggests that it was not emotions, per se, that lead to human sociality, but rather cortical control 119 over emotions. For example, Turner notes that non-human primates are unable to control expression of their emotions. Humans, however, have evolved cortical control over many emotions. This, Turner argues, is what facilitates the complexity of human social behavior. This bears directly on the argument at hand. I have already suggested that there exist punishment thresholds—the amount of resources that an individual is able or willing to contribute, and their individual tolerance for free riding behavior—that serve to limit the degree to which free riders are punished. Consistent with that argument, cortical control over emotions hints at yet another limitation that reduces the frequency and severity of punishment of free riders. Recall that free riders can validate, or even increase, the status of cooperative group members. Displays at anger against free riders who associate with powerful conspecifics can be a dangerous endeavor. In such cases, it is desirable, even adaptive, to be able to control emotions that might cause friction in the group. Fessler and Haley (2003) identify thirteen emotions that are linked with cooperation. Only eight of them will be discussed here, as they bear directly on the argument at hand. Hedonically valenced emotions, such as romantic love and gratitude, are said to mark a valuable relationship, and thus increase the likelihood of cooperation. Specifically, gratitude is seen to prompt the individual to recognize the value in the cooperative act, and the person with whom the act is done, and implies a subsequent willingness to reciprocate in future interactions. Aversively valenced emotions, such as anger, may also play a part in facilitating cooperation. Anger, according to Fessler and Haley, is the opposite of gratitude. Anger is elicited by actual or attempted exploitation or harm. To put it another way, anger is an emotional response to the infliction of a cost. Many empirical studies, including Fehr and Gächter (2000) and Prashnikar and Roth (1992), indicate that subjects often feel angry with free riders. Of 120 course, no distinction was made in these studies between types of free riders, so it is difficult to generalize the results. However, neither can the reports of anger be overlooked or dismissed out of hand. In terms of solidarity, anger has two main effects. First, it reinforces the in-group ties to individuals who feel likewise. Second, it is at least an attempt to encourage free riders in more cooperative behaviors. There are likely, however, conditions that must be met before anger would be acted upon to force compliance. For example, anger would do little good if the angry individual could not afford the cost of acting on that anger, especially if the probability of inducing cooperation is low. As already discussed, resource thresholds are important considerations in understanding how and when anger is manifest as behavior. Envy, more than the mere desire to have something that somewhat else has, also contains an element of hostility toward the more fortunate individual (Dundes 1992; Fessler and Haley 2003). Envy seems to play a role in facilitating cooperation in a way that is directly consistent with the free rider strategy. Behavioral ecologists have often argued that much apparent sharing of resources is tolerated theft by envious conspecifics (Blurton Jones 1987; Fessler and Haley 2003). This is especially true when the cost of defense exceeds the value of the resources being forsaken. This is in direct contrast to the strong reciprocation behaviors hypothesized by Fehr and Gächter (2003). In Fehr and Gächter’s scenario, individuals are unwilling to sacrifice to force compliance to norms. In either case, the emotion of envy—or more precisely the behavioral manifestation of the emotion—is at least partly dependent upon resource thresholds. Another aversively valenced emotion—guilt—has been found to increase cooperation among previously uncooperative players in several iterated public goods games (Ketelaar and Tung Au 2003). This is consistent with the predictions of Equity Theory, which argues that individuals who receive too much payoff feel as guilty as individuals whose payoff is perceived 121 to be too small (Adams 1963; 1965). Individuals who feel guilt about their lack of contribution, or their unilateral decision, often alter their behavior in subsequent iterations to reflect a greater degree of cooperation. However, only a small percentage of uncooperative players report feeling guilt and changed their behavior accordingly (Ketelaar and Tung Au 2003; Fessler and Haley 2003). As noted earlier, there seems to be a steady group of individuals who fail to cooperate even in the face of punishment (Fischbacher, Gächter and Fehr 2001; McElreath et al. 2003). It is unlikely that these individuals feel much guilt about their social strategy; and why should they, since their strategy is completely rational within its context? The opposite of guilt, righteousness, seems to elicit behavior in a fashion that promotes the formation and maintenance of group ties (Fessler and Haley 2003). Contempt has also been implicated in the formation and maintenance of cooperation. Fessler and Haley (2003) correctly argue that in certain cases, the benefits derived from defection exceed the benefits of cooperation, such as in groups where there are many cooperators and few defectors. Contempt, they argue, offers a means by which individuals who have little to offer the actor are marked, with the purpose of avoiding substantive interaction with devalued individuals. Again, however, this would imply that defection behaviors would be selected against, since if every cooperator was aware of whom not to interact with, the free rider would eventually be at a loss to find anyone within the group to exploit. This is solved by applying Axelrod’s (1984) discount parameter. Uncertainty of the future increases interdependency with conspecifics. Additionally, as incidents of exploitation recede into the future, they are less likely to be remembered; or more likely to be forgiven. Those individuals who are aversely marked by contempt are lead to induced shame, which motivates appeasement or avoidance (Gilbert 1997; Elster 1998; Fessler 1999). 122 Shame is an aversely valenced emotion experienced when the actor is aware that his conspecifics are aware he has acted in a blameworthy fashion (Fessler and Haley 2003). Conversely, pride is an hedonically valenced emotion when the actor is aware that his conspecifics are conscious of his commendable behavior (Gilbert 1997; Fessler 1999; Katz 1999). In other words, pride acts as a subjective payoff for norm adherence within the group, while shame acts as a subjective penalty for norm violation within the group. Each of these emotions bears directly on the construction of maintenance of cooperation. Hedonic emotions, such as pride, are self-rewarding. There may not be an extrinsic award for prideful behavior, yet it is approached for the self-satisfaction of having done something prideful. Interestingly, each of these hedonic emotions is norm reinforcing. In social situations, one is usually motivated toward pleasing the group, especially if there is likely to be future interaction within the same conspecifics. Alternatively, there is often a motivation to improve one’s position in the group, either in terms of power or resources. After all, self interest is the natural state of living things (Dawkins 1976). Self interest often includes maintaining ties to a group. However, as Olson (1965) points out, there seems to be an upper boundary to the degree of self support available in a group. To put it another way, the benefits derived from a group are subject to the law of diminishing utility. Once a group exceeds a certain critical mass, each additional group member confers less benefit than the one before. Thus, it is not surprising that self interest within a group can sometimes take an egoistic trend. The larger number of individuals in a group, the more difficult it is to keep track of the actions of any one group member (Fessler and Haley 2003). One is then tempted to defect, even at cost to the group (qua group). Emotions play a central role in motivating an individual toward a specific course of self interested action. Guilt 123 may or may not be a consideration in such an instance, since the larger the group, the less loss each individual will have to bear as the result of defection. Also, the larger the group, the easier it becomes for any individual to free ride on the act of punishing other defectors. Second order free riders become progressively more likely. Emotions as they pertain to social cohesion must be seen as resource and time dependent. How they function in a social setting is determined in part by the endogenous and exogenous constraints that exist in a particular point in time for the particular group. There is little doubt that free riders can elicit genuine feelings of animosity among cooperative group members. However, these aversively valenced emotions seem relatively weak when compared to the effects of hedonically valenced emotions. This is evidenced by the fact that free riders are often tolerated in groups, even when their role is not clearly understood. Feelings of group identity and social cohesion seem more powerful, if not more prevalent, than feelings of anger or frustration. Both aversive and hedonic emotions facilitate social cohesion. This once again belies thinking of emotions as positive or negative, but reinforces them as adaptive mechanisms. In the following three chapters, I will show how the theory outlined here can be applied to aid in understanding real world systems and scenarios. Chapter nine discusses free riders in the welfare system, and shows how the system, and the free riders who benefit from it, exist in a state of equilibrium that becomes self facilitating. Chapter ten discusses the theory in terms of its application to soup kitchens and related charitable organizations. Chapter eleven links the theory to the perpetuation of public broadcasting—specifically public radio. Each example is chosen specifically for its illustrative properties of particular points articulated in the theory. Chapter Summary 124 This chapter introduced the role of emotions in the tolerance of free riders. Beginning with a reconceptualization of emotions as aversive or hedonic, rather than positive or negative, I have argued that the function of emotions in tolerating free riders is dependent upon the nature of the exogenous and endogenous constraints of the group. I have argued that this may be one more reason why free riders are often tolerated in groups. 125 CHAPTER NINE FREE RIDERS IN THE WELFARE SYSTEM “Science is simply common sense at its best—that is, rigidly accurate in observation, and merciless to fallacy in logic.” --Thomas Henry Huxley 126 Chapter Synopsis In this chapter I apply the theory I have laid out to the first of three real-world scenarios. First, I offer a brief discussion of the American welfare system, and argue that the trend of American welfare policy is consistent with the theory outlined here. I show how the three functions I have hypothesized for free riders is operational in the American welfare state, and how such functions serve to maintain the group (in this case, American society) at a stable equilibrium point. I argue that welfare dependence is a form of probability maximization, and exists only within the larger framework of a profit maximization strategy. Finally, I show how free riders operate as a means by which the group may adapt to environmental upheaval and tend toward a new allostatic set point. That is, free riders facilitate the society that facilitates their existence. The Misunderstood Welfare System The purpose of this chapter is not to argue the morality or necessity of the welfare system. It is merely to show how the theory of free riders developed here can be applied to a large scale social structure. I shall show that the American welfare state is a system of tolerated free riders; and how the system facilitates free ridership. I shall show how the facilitation of free ridership increases solidarity through status validation, reduction of risk, and increasing interdependence. The stated purpose of the American welfare system was not to alleviate poverty, but rather to keep people from falling into poverty through no fault of their own (Marmor, Mashaw and Harvey 1990). In other words, the welfare system was originally conceived of as a means to provide temporary relief to people threatened with indigence (Friedman and Friedman 1980). The system has evolved, however, into a system in which both passive and active free riders are 127 supported in a nearly indefinite cycle of subsidies and payments, ostensibly with the goal of eventually eradicating poverty. The largest welfare program in the United States is social security. While some argue that this program correctly falls under the rubric of social insurance, Friedman and Freidman (1980) and Henry (1994) disagree, noting that social security programs have the same function as other welfare programs, and do rely on a redistribution of wealth to fund their payments. This program has changed little over the years, and remains focused on providing aid to passive free riders— the elderly who have worked, and the disabled. Other social welfare programs, such as cash payments, food stamps, and subsidized housing, while geared in principle to the support of passive free riders, have also supported active free ridership. This is perhaps one of the reasons for calls to reform and limit such programs that have recently arisen. Criticisms of the welfare state have been geared mostly toward support of active free riders (Marmor, Mashaw and Harvey 1990). Not surprisingly, most reforms or adjustments in the welfare system have been geared to reducing or limiting support for active free riders. Yet, despite this ostensible punishment of active free riders, the public overwhelmingly supports the continuation of welfare programs, geared both toward active and passive free riders (Marmor, Mashaw and Harvey 1990; Shapiro 1997). Traditional theories of free riders—those stemming from rational action theories, for example—would predict that welfare recipients will be forced into compliance or punished. This prediction is difficult to reconcile with the attitudes and behaviors of the American populace and political leaders. Given the overwhelming public support for social welfare programs, and the ineffectual reforms that have been used to pacify the minority opposition, the fact is that the 128 American welfare system is a system that no only tolerates free riders, but facilitates their existence as well. This is consistent with the theory I have developed here. The Function of Free Riders in Social Welfare Programs I have argued that free riders perform at least three functions in the group that serve to increase group solidarity and maintain equilibrium. As already noted, it is not necessary that free riders perform all of those functions simultaneously, or that it performs all of them at any time. Some aggregations, for example, may have norms that reduce the free rider’s function of status validation to negligible effect. Some functions may manifest themselves under some contexts, but not in others. However, I believe that the American welfare system offers a good test case for the theory because it does allow a supportable demonstration of all three latent functions articulated herein. There are several ways in which the welfare system validates the status of productive group members. First, it is often argued that a stigma has evolved toward individuals who subsist on public monies. The stigma is particularly harsh toward individuals who remain on the welfare rolls for several years (Gilder 1981; Tanner, Moore and Hartman 1995). The stigma is considerably less, however, if the recipient is seen as a passive free rider (Tanner, Moore and Hartman 1995). The stigma, in whatever form and however harsh, certainly serves to validate the status of those individuals whose earned wealth is being redistributed to help free riders; and to the politicians who implement such redistributions. Individuals who are the recipient of welfare payments are generally categorized as lower class or poor. Whatever the label, it is a clear contrast from the middle class or upper class, complete with stereotypical differences in lifestyle, culture, social influence, and politics. The labels attached to welfare recipients clearly imply a distinction between ‘working poor’ and ‘idle 129 poor.’ To be part of the working poor is somewhat less stigmatizing than the alternative; since there is implicit in the term ‘working poor’ notice of a contribution to society, however minimal it might be imagined. The working poor may be classified as subtle free riders, since they are usually on some sort of public assistance, the value of which is greater than the amount of taxes or contributions to the public good they make. The overwhelming public support of social welfare programs is understandable in the context of this function as well. Ideologically, wealth redistribution is generally unsupportable (Freidman and Freidman 1980). In practice, however, it is clear that a majority of the population has little issue with foregoing a part of their income to be redistributed to free riding individuals. This perception of having an abundance of resources leads to the social acceptability of supporting conspecifics through welfare redistribution. We may also see this as reducing risk to the productive members of society in two ways. First, by contributing to the welfare system in general, productive individuals are sustaining a system that they may someday rely upon. It has perplexed scholars for a number of years why support for the welfare system increases in times of economic hardship, when the general population has less to contribute and the public surplus is generally low (Freidman and Freidman 1980; Tanner, Moore and Hartman 1995). Again, however, it becomes clear when considered in the context of this theory. If individuals perceive a potential future need for the welfare system, they are more likely to contribute to it, even though it is against their immediate interest. This analysis is also illustrative of one assumption upon which the theory rests. I have argued that, in most cases, the needs of the individual and the needs of the group are correlated. In this case, the need of each individual for a social safety net is congruent to the need of the society as a whole to support its citizens in times of economic hardship. 130 Similarly, support for the social welfare system, and the free riders who depend upon it, ensures protection from risk. If risk comes in the form of severe economic hardship—such as a federal bankruptcy—the productive members of society are secure in that there exists a system, to which, by their previous contributions, they are entitled to use. Welfare recipients also overwhelmingly work low paying support jobs, which serve the more productive members of the economy. Often, the sum total of benefits derived from welfare exceeds that of the wages of a job the recipient is qualified to do (Tanner, Moore and Hartman 1995). In this way, the welfare system extends and perpetuates poverty (Gilder 1981). Welfare benefits erode work and family, and thus keeps the recipients in a cycle of poverty (Gilder 1981; Schmidtz 1997; Husock 1997). This serves to protect productive members of society from economic hardship. Welfare recipients also serve to drive down the discount parameter. While most research indicates that welfare programs consume just over 1% of the federal budget, there are many programs left out of the analysis. The figures upon which this conclusion is reached considers only cash payments, which are added to by the individual states from block grants received from the federal government that is not specifically earmarked for welfare spending (Marmor, Mashaw and Harvey 1990). There are over 100 federal programs in place that qualify as wealth redistribution social welfare programs (Freidman and Freidman 1980). In 1990, the total of all welfare programs under the control of the federal government constituted roughly 49.5% of federal spending (Marmor, Mashaw and Harvey 1990; Elder 2000). In addition, Goodin (1988: 233) notes that “the major U.S. transfer programs, all taken together, are probably responsible for a total reduction of work hours by recipients amounting to 4.8 percent of total work hours for all workers in the U.S.” Danzinger, Haveman and Plotnik (1981) and Schmidtz (1997) calculate that cash payment programs are reducing work effort by more than 30% on average. 131 The burden of the welfare system is, therefore, not as small as is often thought. Welfare not only requires the use of public monies, but also reduces the productivity of those receiving the money, and of those earning it. The monies and time to maintain welfare recipients, whether active or passive, does pose a burden to society as a whole. Were there no welfare system in place, group productivity would necessarily rise; the amount of public monies would grow; and the discount parameter would rise. Many of the potent political questions of our age concern how to maintain a reasonable standard of living for productive, working individuals, while at the same time funding adequate welfare programs from public funds. The more funding welfare systems get, the more money needs to be drawn from the productive populace. Therefore, the productive populace—with more money taken and less to take home—have a declining discount parameter. That is, the future is more important to them, since their individual surplus has fallen. Interdependence rises as predicted. In short, our free riders pay the cost of incurring greater risk and reducing ours, supporting our place in the social hierarchy, and driving down our individual and national discount parameter. This may be one reason why industrial societies seem to evolve into welfare states. The support of free riders actually encourages growth by putting more monies into the hands of those who ordinarily would not have it. Wealth redistribution, whatever the moral arguments to be made, is certainly a way to drive down the discount parameter of individuals and the society as a whole. As previously noted, this has the effect of increasing group interdependence. The analysis presented here implies that elimination of poverty can have deleterious effects by disturbing the social equilibrium of society. For example, the elevation of free riders to an economic standard that is comparable to that of the average member of society offers a 132 disincentive for the free rider to work, since the defection of the free rider is paying suitable profit maximization dividends as well as probability maximization dividends. Similarly, productive members of society are provided with a disincentive to work. It is as if the payoff matrix for the Prisoner’s Dilemma game has been changed to give greater weight to defection. Indeed, the move is away from a Prisoner’s Dilemma type game to an assurance game, where the cost of defection is lower in relation to the cost of cooperation. In other words, it pays for everyone in an assurance game to defect (Taylor 1987). The disincentive to be productive in such cases is so high as to curtail or eliminate contributions to the public good. Indeed, there is evidence for this trend from several nations. Freidman and Freidman (1980) note that during the communist takeover of China, thousands of refugees found haven in free Hong Kong. The refugees, note Freidman and Freidman, were hard working, industrious, and entrepreneurial. However, emigrants after the communist takeover were described as having no initiative; rather uncooperative; and wished only to collect public benefits, rather than contribute to them. In fact, efforts to increase the material and status inequalities have been less than successful in every communist nation (Eberstadt 1979; Freidman and Freidman 1980). The major symptom of this failure has been a reduction in the productivity of workers (Freidman and Freidman 1980; Henry 1994; Husock 1997). In fact, several scholars have noted that there exists a wider disparity of income in communist states than in capitalist ones (Eberstadt 1979; Freidman and Freidman 1980; Henry 1994). The vital thing, Henry (1994) notes, is not to maximize everyone’s performance, but to ensure maximal performance from the most talented individuals. To put it in terms of the ideas discussed here, equilibrium will exist only if there is 133 balance—if some individuals pursue a strategy of profit maximization, and other individuals pursue a strategy of probability maximization. Indeed, the welfare system is a probability maximization institution. The payoff is rather low in most cases, but the probability of getting anything better than you started with is high. In summation, the welfare system (whether in public or private form) is a necessity in societies that have industrialized to the point of having a large accumulation of public wealth. Just as too much income disparity increases the number of free riders in a society; it seems that too little income disparity also increases the number of free riders. The reason is simply that free riders are a major mechanism for achieving a balanced equilibrium state in a society. Either extreme is highly susceptible to unilateral changes of social strategy. However, when society is composed of a mix of strategies, it tends toward a stable equilibrium state. Chapter Summary In this chapter, I applied the theory to the first of three real world scenarios. I showed the applicability of the theory in analyzing the American welfare state. Specifically, I showed how all three functions hypothesized for free riders are present within a system that operates at a stable equilibrium point. I further showed how the notion of embedded rationality is applicable in that welfare recipients are considered probability maximizers, while producers are considered profit maximizers. I showed the necessity of each for the facilitation of the existence of the other. In the next chapter, I will apply the theory outlined here to the operation of soup kitchens and food pantries. I will show how free riders, through these systems, increase the solidarity of neighborhoods in which the supporting institutions are found. 134 CHAPTER TEN SOUP KITCHENS, FOOD PANTRIES AND FREE RIDERS “I was amazed when I went into academic work—and it still baffles me today—why so many people take the first available path off their main argument into trivia land.” --Robert L. Trivers 135 Chapter Synopsis Chapter ten continues the application of the theory to real world issues. In this chapter, I apply the theory to soup kitchens and food pantries. I show how these institutions, which have very different goals than the social welfare system, are nonetheless amenable to analysis by the theory I have presented. I argue that free riders can be seen as performing all three of the functions I have hypothesized, and that these functions are key to the survival of the institutions, which in turn are key to the maintenance of free riders. This once again demonstrates the embedded nature of the free rider problem. I argue that this understanding fundamentally disqualifies soup kitchens and food pantries as purely altruistic organizations. I then approach the argument from the point of view of the free rider to show how the same conclusion can be reached. Free riding is an embedded social strategy that exists only in terms of a larger cooperative setting, and that free riding increases the duration and solidarity of such groups by performing three key functions. Why Facilitate Active Free Riding Unlike welfare systems, whose ostensible goal is to lift free riders out of poverty and coax them toward productivity, soup kitchens, shelters, and similar charitable organizations exist for the sole purpose of maintaining free riders. Usually existing in concert with the welfare system, but often not directly compensated by it, soup kitchens and homeless shelters often provide aid indiscriminately. Either passive or active free riders may make use of the services, with little or no distinction made between them. It will be interesting to see how the theory that I have proposed applies to organizations whose sole reason for existence is to maintain free riding. In such cases, it is obvious that free riders perform functions that facilitate the existence and 136 maintenance of such institutions; the question, rather, is whether or not the functions of free riders within these contexts are applicable as I have argued. In soup kitchens, free riders function to validate the status of more productive group members. Consider, for example, a shelter that is run by a church organization. The authority of the members of the church is doubtless recognized and supported by free riders who take advantage of the services, especially in cases where the condition of use is that the free rider listens to a sermon or participate in church services. In such cases, there is little doubt about the status hierarchy, and the free rider’s place in it. Status validation also comes internally to the operating members of the soup kitchen, who know that they are helping those ‘less fortunate.’ Finally, the group as a whole may be validated by the neighborhood. Nearby stores and shops often donate materials to maintain the soup kitchen, thus providing a validation of the status of the businesses, as well as a validation of the status of the soup kitchen within a larger community context. Reduction of risk is perhaps more indirect, but it still exists. Similar to the welfare system, contributions to the soup kitchen provide a means by which extreme hardship may be combated. One anecdotal illustration of this comes from a recovering alcoholic who served as a cook in a homeless shelter and soup kitchen he had previously frequented. The individual’s skill as a cook were such that he could have found work making more than the minimum wage the soup kitchen could afford to pay him. In fact, the soup kitchen was sometimes not able to pay the individual for his services at all. When asked why he remained as the cook there, rather than seeking better employment elsewhere, his response was that he wanted to make sure that the soup kitchen stayed open in case he ever needed it again. He could not, of course, be certain that 137 any other aid would be there if he needed it. Literally one drink away from living on the street, the individual recognized that by maintaining free riders, he was reducing his own potential risk. Soup kitchens also provide reduction of risk more generally on the community level. By providing a high probability opportunity for subsistence, the soup kitchen maintains free riders in a controlled environment. This benefits the community in that there is a reduced chance of crime and fewer homeless individuals cluttering the streets. More generally, there are fewer opportunities for free riders to free ride. Furthermore, small donations to the soup kitchen from each community business or individual may be considerably less than the cost to any one of them if they were to deal with the free riders themselves. The more cooperators there are in the neighborhood, the less the cost to each individual cooperator. The benefits of such an organized and controlled environment are doubtless higher than the cost of support. This provides a nice segue into the third function of free riders: increased interdependence. A group that is supporting free riders can often support them as a group. Certainly, as previously noted, free riders often attach themselves to particularly high status and highly productive individuals within the aggregation; however, the high status individual has high status only in the context of the immediate aggregation. His status--and the resulting power- -are emergent properties of the relations that occur within that context. When aggregations—in our example, a community of businesses and individuals that make up the neighborhood—are forced to come to terms with the free rider problem, it is often in their collective interest to cooperate to bring the problem under control. The cost to any one individual or business is often prohibitive; cooperation, however, lowers the cost to each individual or business. Interestingly, as Taylor (1987) notes, the collective action of dealing with free riders itself constitutes a free rider problem. For if each individual or business realizes that 138 each of the others is going to contribute, there is an incentive to free ride and contribute nothing to the cause, while certainly reaping the benefits of the collective action of others. Current rational action models cannot resolve this conundrum. It can be understood only in terms of equilibrium states, which form the cornerstone of the theory outlined here. Consider that when the community cooperates to deal with the free rider issue, they are fundamentally changing the dynamics of the neighborhood (qua group). A shift in the allostatic set point of the neighborhood requires a return to equilibrium—in this case, the creation of additional, second order free riders. Again, the rational actions of each potential contributor cannot be treated independently. They may be understood only as rationalities embedded in the social fabric of the neighborhood struggling to maintain equilibrium in a dynamic environment. There is not one distinct rational course of action. Rather, there are numerous rational actions embedded in the action of all other members, and influencing subsequent actions for each embedded individual acting entity (Zey 1992a). In our example, the choice to collectively control free riders via a soup kitchen or food pantry is embedded in the rational decisions of others to free ride. Similarly, the decisions to free ride are rational insofar as the decision is embedded within a group that will cooperatively support free riding. The point is that support of free riders is not an altruistic act on the part of the contributors. It cannot be equated with the thanksgiving tradition of setting an empty place at the table in case a destitute individual stops in for a meal. Collective actions to control free riders through supportive ventures are not, I propose, created in an attempt to eliminate the free riding population, as is commonly supposed. Rather, a controlled free rider population offers a means by which the aggregation can further insulate itself against risk. 139 In addition to reducing the temptation of free riders engaging in theft of other prohibited activities, the group that offers a controlled free rider environment creates a cushion to reduce the impact of environmental changes. The cooperative aggregation members ensure themselves a means of survival should any one of them become a free rider involuntarily. Similarly, a small investment from each cooperative entity reduces the cost to each entity individually, therefore allowing each entity to keep more individual resources than if he supported the free rider alone. From the Free Riders’ Point of View One might think that a controlled environment, good for the neighborhood members, is not desirable to free riders. This fallacy comes from the faulty assumption that free riders and the cooperative neighborhood are operating with the same social strategy. Recall that it was specified that cooperative group members are profit maximizers, and free riders are probability maximizers. Neighborhoods that cooperate generally seek to maximize personal gain. Free riders seek to maximize the probability of getting anything better than they started with. What better way to maximize that probability than a controlled scenario that is generally indiscriminate? A controlled situation to accommodate free riders also accommodates profit maximizers, in that it pools the cost of maintenance, and collectively increases benefit. It is conceivable to imagine that many altruistic acts began in this way. We may imagine that traditions of altruism—such as setting an empty place at the thanksgiving table—emerged and evolved from means of mutually beneficial free rider control. It should be noted that it seems equally important to produce a controlled environment for both active and passive free riders. Systems such as social security, a program designed primarily for the maintenance of passive free riders, fit the rubric of a controlled free rider scenario. The contributions of each contributor, taken in the form of a tax on earnings, are presumably less for each individual than if each 140 individual had to deal independently with free riders. Joint contributions such as those mentioned here increase the solidarity of the group by not only increasing the level of cooperation by contributing members, but also by offering free riders a means of high probability maintenance of their social strategy, thus increasing their ties to the group. In the next chapter, I will show how the theory presented here may also be applied to yet another real world situation—publicly supported media. I will show that individuals who free ride public television and radio serve functions that increase the solidarity of the aggregation of listeners—a group with presumably undetermined boundaries. Chapter Summary I have applied the theory of free riders to the analysis of soup kitchens and food pantries. I have shown how free riders can be seen as performing all three of the functions I have hypothesized, and how these functions are key to the survival of the institutions, which in turn are key to the maintenance of free riders. I have shown that free riding is an embedded social strategy that exists only in terms of a larger cooperative setting, and that free riding increases the duration and solidarity of such groups by performing three key functions. 141 CHAPTER ELEVEN FREE RIDERS AND PUBLIC MEDIA “Behind every argument is someone’s ignorance. Re-discover the foundation of truth and the purpose and causes of dispute immediately disappear.” --Louis D. Brandeis 142 Chapter Synopsis In the previous two chapters I have applied the new theory of free riders to understanding the American welfare state and soup kitchens. In this chapter, I apply the theory yet again to a real world scenario. I begin the chapter with a brief discussion of the link between group size and contribution size. I argue that extant theories of free riders are not wholly consistent with data on the link between group size and contribution size. I show how the theory constructer here is consistent with the data. I argue that the theory of free riders outlined here can be used not only to understand organizations that exist to facilitate free riding, but also to understand organizations that ostensibly desire to eliminate free riders. Public broadcasting is a non-profit organization that attempts to curb free riding by appealing to the emotions of the free rider. Specifically, pledge drives are periodically held to raise money for the station. During these pledge drives, the station appeals to the guilt of free riders in order to induce them to contribute to the collective good. I argue, however, that it is not beneficial to the station to curb free riding. Rather, I argue that free riders provide an equilibrium by which the station balances its current situation and future contributions. I show how the three functions of free riders that I have discussed are relevant to the analysis of free riders in public broadcasting. Group Size and Contribution Size In the last chapter, we saw how neighborhood groups may contribute to the maintenance of free riders through collective contributions that are presumed to be less than any would individual member would have to contribute individually to achieve the same result. The greater the number of contributors, the smaller each individual needs to be. As the minimum contribution threshold decreases, the temptation to defect rises. The degree to which free riders 143 are tolerated will rise accordingly. Indeed, Andreoni (1988) demonstrates that as group size grows infinitely large, the proportion of the group contributing to the public good reduces to near zero. Fries, Golding and Romano (1991) show that as group size increases, average contributions decrease. These trends constitute a second order free riding problem, since there will be a temptation to defect from contributing to the maintenance of the public good (Taylor 1987). Once again, the logic of current theories has reached an impasse. For even if we solve the second order free riding problem, there is an infinite regression of free rider problems. The passage by Taylor’s (1987: 30) quoted earlier is worth repeating: . . .the maintenance of a system of such sanctions itself presupposes the solution of another collective action problem. Punishing someone who does not conform to the norm—punishing someone for being a free rider on the efforts of others to provide a public good, for example—is itself a public good for the group in question, and everyone would prefer others to do this unpleasant job. How can we reconcile this problem with the current theory? Consider that as group size increases, the minimum amount of necessary contribution to the public good decreases. The investment of any one individual to the collective good is reduced with increasing group size. At the same time, the desire to free ride increases. Yet, it would seem that decreased minimum investment to the group would be associated with fewer incidents of free riding. As the cost of contributing decreases, the difference between the cost of cooperation and the benefits of free riding grows increasingly smaller. As the choice to free ride and the choice to contribute converge toward equality, they become more nearly equally rational. This problem can be solved in two ways, neither of which is logically available to rational action theories. The first solution is to employ the typology of free riders discussed in chapter five. Distinguishing between active and passive; and gross and subtle free riders would 144 allow a more specific analysis of the trends. It is, as I have already shown, a mistake to assume similar motives for all free riders. The typology offers a possible solution in that as group size increases, the number of free riders may increases simply due to an increase in the number of passive free riders; or, we may alternatively suggest that the number of redundant members produce so little in relation to the most productive group members that their contributions seem consistent with subtle free riding. In fact, it appears perfectly reasonable to explain the two contradictory trends by suggesting that as group size increases, the number of redundant members increases, thus reducing the individual contributions of the redundant members relative to the more productive members. This, if perceived as free riding, appears as an increase in the number of free riders. Both trends are thus explained. The second possible solution is perhaps less elegant, but is equally understandable within the boundaries of the theory outlined here. I have previously suggested that free riding and cooperating toward the public good represent two distinct, yet interrelated, social strategies that converge on an equilibrium point. Free riders represent a strategy of probability maximization, while cooperative group members can be seen as using a profit maximizing strategy. It is clear that profit maximization is the more common of the two strategies, and that its abundance creates the conditions that encourage and facilitate cooperation among productive group members. From this conception of free riding, we would predict that increases in the number of cooperative individuals would be accompanied by an increase in free riders, ceteris paribus. This is distinct from the predictions made by rational action models, and seems consistent with experimental data (Andreoni 1988; Brunner 1998). However, rational action theories, as I have already argued, would predict a negative association of contribution size and group size; while the theory outlined here predicts no necessary change in contributions as group size changes. 145 This is because in considering contributing to the public goods as a cooperative social strategy, we assume that cooperators will usually cooperate if the opportunity presents itself, regardless of the group size, and all other things remaining equal. As more individuals are added to the group, there will generally be an increase in the number of people with whom to cooperate. This is not to say that individuals in a group cannot unilaterally switch strategies. Certainly, free riding individuals can become productive cooperators when the situation suits them; and cooperators can similarly change to a free riding strategy under certain conditions, some of which I have alluded to here. In either case, changing social strategies is not an arbitrary choice, and would occur only when conditions that are specific to the aggregation are met. These conditions may or may not be universal. In the situation described in this chapter, when the cost of cooperating and the benefit of free riding converge, there is no true incentive for any member to unilaterally change strategies. It is also noteworthy to consider that the prediction that there is no necessary association with group size and contribution size is not suggesting that there is never an association. Rather, I argue that there is no reason that the two variables must necessarily be correlated. Under some conditions a correlation may indeed exist; but it need not be so in every case. For example, at least one study found that when individuals have identical preferences but different incomes, the larger the range of income, the smaller the proportion of the population contributing to the public good (Bergstrom, Blume and Varian 1986). On the other hand, Lipford (1995) found no significant association between the size of a church congregation and individual contributions. Brunner (1998) found that increasing group size is associated with an increase in the number of free riders in public media. Goetze, Glover and Biswas (1993) report that an increase in group size resulted in a significant reduction in free 146 riding. Rational action theories have no consistent means by which to explain these drastically disparate findings; the theory outlined here can. Free Riding in Public Broadcasting A closer look at Brunner’s (1998) analysis offers some interesting insights. Brunner used data on the number of listeners and contributors to public radio to determine whether the proportion of contributors falls as group size increases; and whether contributions fall as group size increases. Brunner found that increases in group size result in significantly more free riding. Both current theories and the theory I have outlined here would predict this outcome. Brunner discovered, however, that group size had no effect on contributions per contributor, a finding contrary to the predictions of traditional theories of free riding, but wholly consistent with the predictions that come from the theory I have argued for here. We can see this more clearly through a more in-depth analysis of free riding in public radio and public television. Public radio and television have two primary sources of funding— corporate donations and donations by individual listeners. Contributing group members often make monthly or yearly pledges to the stations, but there is no way to prevent non-paying individuals from receiving the programming. Rational action theories would predict, then, that it is to the interest of each individual to free ride—to not contribute to the station. This is merely another version of Hardin’s tragedy of the commons, since any one person may benefit from such a strategy; but if this strategy is pursued by every listener, there can be no public good created. The station would have no means of maintaining its broadcasts, and all of the listeners would lose. In broader terms of the theory presented here, defection is a disequilibrium state. 147 But if every individual listener contributed to the station, the results would be equally disastrous. As surprising as this sounds, the reasoning is quite simple. In order to solicit contributions to the station, programming is regularly interrupted for pledge drives. It is through this means that most of the revenue is received. Pledges are usually organized by levels, with each level of pledging offering a progressively better gift. Levels are also used as a means by which non-contributing members of lower incomes may be enticed to contribute, since they are told that they need to give only what they can afford. In analyzing this problem, it must be assumed that each listener is not aware how many others are contributing. This is equivalent to the Prisoner’s Dilemma game discussed earlier in which the most beneficial choice is not known. In such cases, there is no unilaterally correct decision; rather, a particular strategy is adopted to either maximize profitable gain or maximize the probability of getting anything better that what was available at the starting point. Assume then that every listener, unaware of what all of the others are doing, contributes to the station. We cannot assume that the amount of the contributions decreases as group size increases, since each listener is unaware of what the other listeners are doing. The station, having everyone contributing, has no incentive to offer gifts for contributions, since everyone is contributing without the use of positive sanctions. This would devalue the gifts such that they afford no extrinsic value. However, in a situation where only a fraction of listeners contribute to the station, the gifts take on not only the role of positive sanction, but also the role of status builder. Individuals who own such gifts demonstrate their relative status when juxtaposed with listeners who free ride. Reduction of risk occurs in the necessity of the pledge drive. If every listener contributes to the station, there is no reason for the station to hold a pledge drive, since such an event would 148 cost the station air time and person time with no positive effect. However, stations that have free riders always have an incentive to hold a pledge drive, to encourage free riders to contribute. This also gives contributors a chance to renew their contributions or to raise the level of their contribution. Pledge drives serve to remind the listeners that the public good provided is dependent upon public contributions, and would not continue without the support of the listeners. Pledge drives, given for the benefit of both the contributor and the non-contributor, serve to reduce the risk that the station will fail. This ensures that the public good will continue to be available. Similarly, free riders serve to drive down the discount parameter. During the pledge drives, stations talk at length about the needs for funds, and the need for everyone to contribute. Stations are quick to advertise their budget shortfalls, reiterating the need for continued pledges. However, if every listener contributed to the stations such that a shortfall would not exist, there is thus no reason for additional listeners to contribute. As ostensible shortfall, however, serves to encourage contributions from cooperative members who value the station’s services and who are therefore concerned with its continuation. Even though public media is a non-exclusionary resource—an all or nothing one, at that—it is still subject to a perceived reduction in resources. It might be said that it is not the media itself that is the resource, but rather the money that is necessary to maintain it. If the money is perceived to be less than that which is necessary to maintain the station, it is likely to operate under the same principle of discount parameter and increase cohesion and membership by enticing contributions from already contributing members, even though the members of this aggregation never meet. Chapter Summary 149 In this chapter I have shown how free riders among public broadcasting listeners can be understood as an embedded strategy that performs all of the functions I have hypothesized in previous chapters. I have argued that the theory of free riders outlined here can be used not only to understand organizations that exist to facilitate free riding, but also to understand organizations that ostensibly desire to eliminate free riders. I have argued that free riders provide an equilibrium by which the station balances its current situation and future contributions. I demonstrated how the three functions of free riders that I have discussed are relevant to the analysis of free riders in public broadcasting. In the final chapter, I shall briefly review the main tenets of the theory, and review the major hypotheses that the theory generates. I shall also discuss possible objections to the theory, and how those objections might be answered. Finally, I shall discuss where the theory leads us, and how we might get there. 150 CHAPTER TWELVE HYPOTHESES, OBJECTIONS AND CONCLUSIONS “To generalize is to be an idiot.” --William Blake 151 Chapter Synopsis The final chapter serves three main purposes. First, it offers a review of the major tenets of the theory. Second, it uses these tenets to identify a non-exhaustive list of testable hypotheses can be derived from the theory. Each hypothesis is dealt with individually, with some suggestions for testing. Third, the chapter deals with a number of possible objections to the theory. First, I briefly articulate the objection, and then respond to it in an attempt to answer the criticism. I conclude by discussing the limitations and scope of the theory, and the implications of the theory. Review of the Theory Traditional approaches to the free rider problem, whether from biology, economics, or sociology, have been unable to explain the disparity between the predictions that are derived from those theories and the observational data. Specifically, these approaches, though they begin in different places and take drastically different paths, converge on a common prediction that free riders are detrimental to solidarity, and will be punished into compliance with the cooperative norms of the group. I have shown, using observational and empirical studies, the prediction falls far short of reality. While it is certainly true that free riders are sometimes punished for not cooperating, there are numerous instances when they are not. It is also clear that punishments, when they are levied, are often not successful in inducing compliance. I have offered one explanation here. While I do not suggest that the explanation I have put forth is universally applicable, I do believe that it is broad enough to explain a great deal of the disparity between current theories and the numerous exceptions that exist. The theory articulated here is equally applicable to aggregations or groups; it is also equally applicable to 152 animals or humans. It draws on a wide variety of disciplines, synthesizing the information from them into a coherent whole that seeks to understand the nature and functions of free riders. I have argued that tolerated free riders are often functional in groups. I believe that free riders form a cornerstone of group solidarity by protecting core group members from risk; validating the high status of core group members; and increasing the uncertainty of the future, thus increasing group interdependence. Free riders also offer a measure of fitness for the group as a whole. The maintenance of free riders indicates a group that is able to support idle members with little strain on public resources. To that end, I have argued that groups enter a state of autopoiesis, and take on a number of characteristics of living entities. Like living entities, groups must tend toward an equilibrium state in order to remain functional in a dynamic environment. Groups, like individual organisms, adjust their behaviors to adapt to environmental dynamism. They seek out a new set point that maintains optimality in the face of changing constraints. I have made the argument that at least four types of free riders exist. Free riders may be categorized as gross or subtle and active or passive. I believe that much of the disparity in research on free ridership can be overcome simply by applying this typology to the experimental protocol. This allows the researcher to more clearly tailor the experimental manipulation to a specific type of free riding behavior. Understanding the typology offers insight into how various constraints affect the nature and frequency of free riding. I have argued that free riders operate under a social strategy that is competitive, but complimentary to, the more cooperative strategies. Free riders are frequency dependent upon the number of cooperative conspecifics, and serve to balance the group in a state of allostatic equilibrium, which makes the group robust against exogenous and endogenous change. The 153 frequency of free riding individuals will wax and wane as group circumstances change. The availability of resources, the number of cooperating members, and the nature of external and internal constraints limit the degree to which free riders can be supported. To illustrate this, I have offered one very basic equilibrium model that demonstrates the frequency dependence of free riders on several variables. I have discussed the role of various emotions in the maintenance of free riders, concentrating on how various hedonic and aversive emotions affect compliance to group norms. I have argued that understanding emotions not as a social construction, but rather as an adaptive mechanism, offers clarity to understanding tolerated free ridership. Specifically, I have argued that many emotions commonly thought of as detrimental to solidarity (i.e. negative emotions) can operate to increase solidarity and allow for the maintenance of free riders. I have also demonstrated how the theory may be applied to real world situations. In this case, I have demonstrated its applicability to three aggregations commonly found at various levels in post industrial society: the welfare state, soup kitchens, and public broadcasting. In each of these examples, I have shown how each of these groups tolerates free riders, and facilitates their existence in the group. I have also shown how free riders contribute to the solidarity of each of these groups. In the next section, I shall extract some of the specific hypotheses that are generated by the theory. Testable Hypotheses The theory outlined here is easily tested. There are a variety of testable hypotheses that are generated by the theory outlined here to drive a program of research that will confirm or deny the theory. In this section, I shall discuss each of a variety of testable hypotheses, along with 154 offering at least one way in which the hypotheses may be tested, and the possible implications. Additionally, I will identify several theoretical predictions and avenues for future exploration. There are at least four broad hypotheses that may be deduced from the theory outlined here: 1) There are at least four distinct types of free riders, which can be distinguished based upon their contributions and the motivations for those contributions. 1a.) relative status will differ for different types of free riders 1b) free riders will be punished differently (i.e. frequency and severity of punishment) according to their typology. 2) Different free riders will have different relative status positions within the group. Specifically: 2a) passive subtle cheaters will have the highest relative status. 2b) passive gross free riders will have lower status than passive subtle free riders, but higher status than any active free riders. 2c) active subtle free riders will have higher status than active gross free riders, but lower status than all passive free riders. 2d) active gross free riders will have the lowest relative status. 3) Free riders are frequency dependent upon the number of individuals that pursue cooperative strategies. 4) Free riders will follow a probability maximization strategy. 1). I have identified numerous catalogued examples of tolerated free riders, both in the animal and human world. I have stated that free riders may perform functions in a group that facilitate their tolerance. I argue that tolerance is based on a number of factors, such as the resources of the individuals comprising the group and the resources of the group qua group. Tolerance will also vary with the nature and motivation of the free rider. I argue that at least four distinct types of free riders exist. These distinctions are based upon the nature of the free riding behavior (gross versus subtle) and the nature of the motivation (active versus passive). Doubtless there are other typologies which may be considered. The point, however, is that the relative status of the free riders, and the punishments that may be meted out by the group will differ with 155 the typology. Both the behavior of free riding and the motivation behind the free riding are easily quantifiable. 2) Borrowing first from Trivers (1971), I divided free riders into two general categories based upon the degree of free riding. That is, free riders may be classified as active or passive, depending upon their relative return contribution to the public good. Gross free riders make no return to the public good, while subtle free riders make a return that is less than the amount of public good that they consume. I further subdivided free riders into active or passive, based upon the apparent motivation for the free riding behavior. Passive free riders are unable to contribute to the public good due to infirmity, illness, or age. Active free riders, on the other hand, are unwilling to contribute to the public good. Free riders will generally of lower status than core group members. I have further argued that passive free riders will have relatively higher status than their active counterparts. Similarly, I hypothesize that active gross free riders will be the most often and most severely punished, followed by active subtle, passive gross and passive subtle. Such hypotheses are likewise easily testable in a laboratory setting. First, it would be prudent to test the hypothesis that different types of free riders are recognized in a group based upon the criteria already mentioned. If these distinctions, or similar distinctions, are supported empirically, it is not a stretch to test the relative status of different types of free riders. Means of testing the relative status of individuals is already available. The researcher need only make periodic checks of the relative status of free riding individuals after incidents of free riding behavior. Similar tests could be constructed to test whether certain types of free riders receive more frequent or more severe punishment than other types. A distinction between active and 156 passive, and gross versus subtle, free ridership is easy to manipulate experimentally, and offers a ready test for the hypothesis. 3). I have laid out one possible equilibrium model by which the hypothesis may be tested. There are certainly more complex models of equilibrium that consider far more variables than I have done. Certainly, refinement of a specific model of the theory would be necessary. However, as a beginning point, the equilibrium model presented here could be subject to experimental tests. The main point of the model is to argue that free riding behavior is frequency dependent upon core cooperative members; and that frequency dependency operates as an allostatic set point in equilibrium. This can be easily tested by manipulating the number of cooperative group members, as well as manipulating the constraints such that the equilibrium is disturbed. 4). Falsifying the hypothesis that free riders pursue a fundamentally different social strategy than core group members is not an easy one. One immediate problem is how a social strategy can be identified and measured. There is some precedent for doing so in game theory analysis. There is also the problem of individuals changing social strategies as circumstances change. Wilson (1998) makes it clear that more sophisticated game theory model, such as those offered by Boyd and Lorberbaum (1987), Dugatkin (1990) and Dugatkin and Wilson (1991) predict a mixture of various social strategies “maintained by environmental heterogeneity and frequency-dependent forces’’ (Dugatkin and Wilson 1991: 263). Wilson goes on to note that individuals should be able to switch social strategies to some extent. However, some degree of specialization should be manifest. This offers at least a beginning point by which we can understand social strategies and how competitive but complimentary strategies interact. There are doubtless many more hypotheses that can be gleaned from the theory presented here. I have presented the ones that I believe are the most theoretically justifiable, interesting, 157 and important. The theory opens the door to a new direction in research in solidarity, a direction which requires a multidisciplinary approach. There are also many avenues for future research. The direct implications of the theory presented here suggest that the degree of solidarity in a group will ebb and flow with changes in exogenous and endogenous constraints. Solidarity, rather than being a stable point, is rather a highly dynamic characteristic of groups that varies with varying environmental circumstances. Observing this phenomenon would require the ability to measure not only the effect of constraints, but also an accurate measurement of group solidarity. A reliable external measure is difficult to envision and operationalize. Researchers would perhaps be confined to rely on group reports of solidarity, which will almost certainly vary with social strategy. Although a detailed analysis is the subject for a different project, I suspect that group solidarity will generally follow the pattern of an Elliot wave. That is, solidarity will rise to a set point, until the equilibrium is disturbed, when it will precipitously decline until the group adjusts to the new constraints. Solidarity will then rise again to a point higher than the original set point, decline again as groups constraints change yet again. The decline, however, will not reach a point below the point of first decline. This pattern is often associated with the pattern of the stock market. I suspect that this pattern of solidarity will more or less describe the nature of a group tending toward equilibrium. I have argued that many groups function autopoietically. That is, groups may act as a living organism. While this hypothesis is not essential to the falsification of the general theory I have outlined here, it is nonetheless helpful if such an assertion is supported. Like an organism, an autopoietic group will be able to adapt itself to environmental dynamism. Both exogenous and endogenous constraints require adaptation to the unique circumstances to maintain long term 158 survival. I have suggested that free riders and redundancy are two ways in which autopoietic groups facilitate adaptability. More specifically, I would argue that the types of free riders present would affect the way in which the group responds to the changes in environment. Sociological studies of emotions have made great strides in recent years. However, dominant sociological understanding of emotions puts too much emphasis on emotions as a social entity. This inevitably leads to an understanding of emotions as positive or negative, a conception which is certainly flawed. Evolutionary conceptions of emotions square solidly with recent neurological findings (Dolan 2002). These findings conceive of emotions as adaptive mechanisms, probably first evolved as a primitive means of cognition. The role of emotions in social interaction is probably exaptive. This is not to downplay the role that emotions play in social interaction. Emotions certainly play a very important role in the social world of numerous species, including humans. It is important, however, to have a clear picture about the origins of emotions in order to fully understand the role they play both ontogenetically and phylogenetically. An evolutionary understanding of emotions belies the categorization of emotions as positive or negative, a distinction that is certainly dominant in sociological and psychological literature. When we adopt an evolutionary perspective on emotions, we can more clearly understand the roles they play in the creation and maintenance of solidarity. We can resolve how sociologically ‘negative’ emotions have positive consequences on the solidarity of the group. The role of emotions in interaction with free riders likely varies with the resources available and the general tolerance level of the individuals in the interaction. It would also depend upon the type of free rider being considered. Passive free riders are likely to evoke fewer aversive emotions, and with less intensity, than their active counterparts. However, I would 159 suspect that aversive emotions will be less effective on active free riders than on passive ones. Relatedly, gross free riders will evoke more aversive emotions, and with greater intensity than subtle free riders. Objections to the Theory As with any theory, numerous objections can be raised. I shall attempt to articulate some of those objections and answer them here. Extended discussion of many of the criticisms is prohibitive, so I shall concentrate on the main points of each, rather than give a complete account of how they should be answered. One objection I have commonly heard in the development of this theory is my treatment of groups as autopoietic. Indeed, this is a controversial subject, and much has been written about it on both sides of the issue. I shall answer only that I have not stated that groups must be autopoietic, only that they can be autopoietic. Under what conditions groups may take on this characteristic is the subject for another project. At any rate, autopoiesis, while certainly helpful to the logic of the theory I have developed, is not essential to it, and is not a condition of falsification. It does seem clear that there are emergent properties in many groups that are not reducible to their individual components. Similar properties can be seen in organisms themselves. Metazoa are comprised of numerous cells performing specific functions in cooperative balance. Yet, we do not consider the organism to be merely a conglomeration of individual cells. The organism is thought of and treated as a gestalt, with properties distinct from its component parts. Equally important, it is not the cells themselves that are pertinent to an understanding of the organism; rather, it is the relationship that each cell—or more accurately, 160 each category of cell—has to all others. This is what gives rise to the emergent properties that define the metazoan. Likewise, the relationships between members of group—not the individuals themselves—give rise to the emergent properties that define the group. It is not specifically necessary that we designate the group as an organism qua organism. We should, however, at minimum acknowledge the analogous nature of the comparison. Several times I have heard the objection that the premises of the theory tread too close to group selection models. As I have already noted, revised group selection models are making a qualified resurgence in biology, particularly among biologists using game theoretic analysis. Group selection does appear to be a legitimate form of natural selection, but not always a strong one. Wilson (1998) lays out conditions under which group selection can be seen as a having a major impact on selection. In that free riders affect not only within group relations, but also between group relations, they offer a selective advantage to groups who maintain them. A related objection involves the understanding that free riders operate under a distinct, but equally rational, social strategy I have called probability maximization. What evidence have we of this beyond the crude Prisoner’s Dilemma? One limitation of the PD scenario itself is the premise that all players will try to get the highest payoff possible (Barash 2003). As pointed out by Simpson (2004), a significant number of players in PD studies play the game as if it were an Assurance Game. Simpson found that individuals identified as prosocial (i.e. cooperative), in which mutual cooperation offers the highest payoff. Players identified as individualists (i.e. defectors) play the Prisoner’s Dilemma scenario as it is, in which defection offers the highest probability that the individual will get anything better than they started with. Consistent with the findings of McElreath et al. (2003), Fishcbacher, Gächter and Fehr (2001) and Fehr, Fischbacher and Gächter (2002), Simpson (2004) identified 29.9% of subjects as individualists. Additionally, 161 Wilson (1998: 263) notes that “there is every reason to expect a single population to evolve into a multi-strategy community with respect to cooperation and exploitation.” Admittedly, the PD scenario offers only a crude analysis of social strategies and their relationships to one another. Yet, the PD game also offers a rather clear insight into the embedded nature of social strategies, as well as the kinds of strategies that could be pursued. More sophisticated game theoretic models certainly exist, and I am confident that they will support the analysis laid out here. I have argued that there exist at least four distinct types of free riders, each having its own consequences within the group. One scholar disgustedly objected to this typology, suggesting that it is a useless distinction because free riders in one situation may be productive in another. His conclusion, however, does not follow from the premise. It is true that free riding is often situational. Individuals often change social strategies to adapt to a changing environment. In fact, that is precisely the point that I wish to emphasize. Such flexibility is necessary—in fact essential—for the maintenance of the group over time. However, even with the flexibility of social strategies, it is hard to ignore the non-negligible percent of individuals who always free ride, regardless of the consequences. It is equally difficult to ignore the fact that punishment for free riding is not as frequent or effective as current theory predicts. The distinction is useful not only for the predictions that it makes about status and power within the group, but also because it offers a means by which the disparate empirical findings might be understood. In terms of understanding why many free riders are tolerated, it is useful to consider the motivations of the free riders within that context. 162 It has been noted that, despite the handful of illustrative examples offered here, that free riders are often punished into compliance. This is certainly true. What needs to be answered, however, is why free riders are punished in some situations and not in others. It is possible that the distinction based on the nature of free riding (i.e. gross versus subtle and active versus passive) plays a role in the determination of whether or not to punish free riders in that situation. I have also hinted at the fact that resource and tolerance thresholds play a role in this determination. In many cases in which free riders are not punished, it is possible that the cost to each individual of punishing free riders is greater than the benefit derived. Coupled with the fact that not everyone in the group will have the same amount of resources available to punish, or will have the same tolerance for free riding, it is not difficult to see why some free riders would often escape punishment. There is also the problem of preventing second and subsequent order free riding from occurring, which increases the cost of punishment to those who contribute. It has also been noted that examples can be found of free riders that do not perform all of functions specified. This again is true, but is not sufficient to dismiss the theory. I am not claiming that all three functions outlined here must be present in every group or situation. Nor am I claiming necessarily that any function at all will be seen. I am claiming only that in situations where free riders persist, they likely perform functions that aid in group survival. Furthermore, there may be a distinct difference between the functions of free riders and the actions taken against them in natural versus contrived social groups. That is, individuals may react differently to free riders in a laboratory setting than in a natural setting. Contrived experiments typically do bear some relation to real world scenarios. Yet, the scenarios are generally more clearly specified in a contrived setting than in a natural one. Additionally, laboratory studies are typically forced to saddle group members with constraints that are more 163 clearly delineated than in the real world, or which may not be common in natural groups. Therefore, we should not necessarily assume that empirical examples that contradict the theory are generalizable to every natural group. Several critics have argued that they dislike the political implications of the theory. To that I can respond only that I have had no intention of making a political or ethical statement. In fact, I have tried very hard to avoid one. Whatever political statements may be gleaned from this theory are the constructs of the readers. Indeed, it is difficult to respond to this criticism in any other way, since my intention was not to favor one political viewpoint over any other, but merely to explain a phenomenon. I know of no criterion that explanations must be consistent with politically desirable ends, even if such ends could be identified. The criticism, in my view, is hardly a criticism, in that the mere fact that the political implications may be socially unpalatable does not render a theory untrue. This has been clearly illustrated by Darwin’s theory of natural selection. Whatever political ends are to arise as the result of the theory offered here is left to the purview of the ethicists and politicians. One interesting criticism concerns the issue of why the theory is needed at all. It has been noted that current theories—and in particular equity theory—offer a more parsimonious explanation than I have offered here. At first glance, this argument appears persuasive. However, further investigation will quickly reveal the shortcoming of the argument. One main principle of equity theory is that the output gained in an interaction should be proportional to the input. Those individuals who put in more effort should be rewarded by greater gains. When the gains are perceived as not proportional, aversive feelings are manifest. Similarly, equity theory argues that too much gain is perceived as just as bad as too little gain. 164 Aversive feelings, such as guilt, are the result of an individual’s perception that they have received more than a proportional gain. Now, let us consider this in light of free riders. Equity theory predicts that free riding would be curbed by feelings of guilt or shame when free riders receive an output that is disproportional to their input. Shame and guilt are two of the most vividly remembered feelings (Masson and McCarthy 1995). In fact, shame has been called ‘the master emotion’ in which societies use to enforce norms (Masson and McCarthy 1995). If free riders were guided by shame or guilt, we would expect a reduction in free riding behavior, especially as greater numbers guilty free riders change to a cooperative strategy. However, this is not what is seen. As we already know, in several empirical studies, 20%-40% of individuals exhibited free riding behavior regardless of the circumstances. These individuals remained defectors even after significant punishment, inducement of guilt or shame, even threatening serious sanction. Given the pervasive nature of free riding both from empirical studies and from observational data, it does not seem likely that input/output ratios are a major concern of the free rider. It does not appear as though guilt or shame is a motivator to unilaterally change strategies that are successfully embedded and in equilibrium. Conclusions, Implications and Future Research It has been suggested that the theory is severely limited in scope, since I have plainly stated that there are numerous examples of punished free riders, and that free riders need not perform all of the functions outlined in this treatise. This criticism is warranted in fact, if not in spirit. A comprehensive understanding of free riders and their relation to group solidarity is prohibitive, given the state of knowledge today. However, I am confident that as the various disciplines continue their struggle to understand this fundamental fact of social behavior, 165 evidence will arise that will broaden the scope of the theory considerably. I have laid out only a framework by which we may begin to approach research in this area from a different angle. The work is not meant to be an exhaustive exposition on the subject. Of necessity, I have excluded much valuable and interesting work on the subject, and have concentrated specifically on works that bear directly on the argument made herein. Even in this limited disquisition, the research implications cannot be overlooked. The theory seeks to explain the existence and persistence of tolerated free ridership in groups and aggregations. Prevailing theories argue that free riders are detrimental to the solidarity of groups. I challenge that notion, and argue that free riders often form a cornerstone of group solidarity. Each of the many disciplines may benefit from the discussions of rationality. I have attempted to cast doubt on the unidimensionality of rational choice. Specifically, I have attempted to convince my readers that there are at least two equally rational strategies that can be properly understood only in relation to one another. This forms the cornerstone of the theory. I have brought the issue of equilibrium to the fore. Already widely used in some circles of economic sciences and evolutionary biology, I feel that equilibrium models will prove their worth in a variety of other disciplines as well. In this case, I have used a frequency dependency model to illustrate the fact that free riders can exist along side cooperative group members in a state of allostatic equilibrium. This offers a phantom of a future in which many competing social behaviors and forces will be seen as frequency dependent, and functional. I have also suggested that at least four different types of free riders exist. Such a notion offers a testable alternative to the prevailing methodology of treating all free riders in the same way. I firmly believe that pursuit of this typology will alleviate much of the disparity of results in 166 empirical studies from a variety of disciplines. This offers the best test as to whether or not the distinction is—as one critic suggests—a useless one. I have hinted at, but made no formal argument for, a more broadly conceived equilibrium model of solidarity, in which I hypothesize group solidarity as an Elliot wave. The implications of this are legion, but are the subject for another disquisition. Also of interest pending the outcome of the theory is Cosmides and Tooby’s (1992) assertion that human possess mental module for a cheater detector. Cosmides and Tooby argue that selection pressures on early humans facilitated the development of mental abilities to detect individuals who are likely to act in a purely egoistic manner and defect from reciprocal cooperative efforts. The theory presented here, if true, renders the cheater detector hypothesis incorrect. Consider that if there are indeed at least four different kinds of free riders, a mental module would need to be able to distinguish between every type of free rider. Modular mind theory—which relies on assumptions of non-plasticity—would need to hypothesize both a separate module for detecting different kinds of cheaters, and similarly a module for each type of cheating that would likely develop. Yet Cosmides and Tooby hypothesize one seemingly static algorithm that arguably works equally well for all types of cheaters—even types of cheaters that may not have existed in the Pleistocene, when such modules are said to have developed. If the hypothesis of an evolutionary arms race holds merit, we ought furthermore to have evidence for a module to deceive cheater detectors. To date, Cosmides and Tooby have neglected this important consideration. In the end, I have offered a means of solving a persistent disparity between theoretical prediction and empirical fact. More accurately, I have merely turned the problem around. I have longed believed that solving scientific mystery is less about finding the right answers, and more 167 about asking the right questions. Traditionally, the question that has been asked about free riders is ‘how do we get free riders to cooperate with the rest of the group?’ It is a good question, and an important one. Yet, I feel that a much better foundational question is ‘why should we desire to get free riders to cooperate?’ It is this question that has driven this project. 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