Faculty & Research

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Me Versus We: Balancing Cooperation and Competition in Groups Through Emotional Algorithms by C. Loch S. Schneider and C. Galunic

2003/74/TM

Working Paper Series

ME VERSUS WE: BALANCING COOPERATION AND COMPETITION IN GROUPS THROUGH EMOTIONAL ALGORITHMS

Christoph H. Loch, INSEAD and HP Labs Susan Schneider, HEC Genève Charles Galunic, INSEAD

September 24, 2003

Working Paper

Me versus We: Balancing Cooperation and Competition in Groups through Emotional Algorithms

This paper examines emotional algorithms and their role in a fundamental dilemma that confronts human groups—whether actors should take care of “me” (compete) or take care of “we” (cooperate). We argue that human emotions, triggered in algorithmic fashion through four common, albeit culturally specified, mechanisms, powerfully direct humans to compete or cooperate. Drawing on evolutionary theory and work within evolutionary psychology, we first identify and characterize these hard-wired emotional algorithms, presenting evidence for their independent existence and influence. Their regulatory influence on human groups, however, can only be appreciated once we examine them as a system. We show how, as a system, these algorithms help explain the dynamic balance that members of human groups can (and often must) achieve between competition and cooperation. We derive three propositions regarding how these algorithms play out in groups and conclude with a discussion of this system’s evolutionary origins. We also suggest that understanding these dynamics can help leaders better manage cooperation and competition in organizational groups.

1. INTRODUCTION: THE FUNDAMENTAL DILEMMA OF SOCIAL GROUPS

Most of human history happened in relatively small groups of between 50-150 people

(Dunbar 1996a) where individuals were naturally and frequently faced with a fundamental dilemma: the pursuit of self-interest versus the pursuit of group interest.

Excessive self-interest could mean a lack of coordinated effort, and so expose the group—and the individual—to greater environmental risks, such as other tribes and non- human predators (Sober and Wilson, 1998). Excessive cooperation, on the other hand— such as taking the vanguard position during battles—could leave individuals exploited by fellow members and faced with lower survival chances. Indeed, this dilemma of cooperation versus competition is timeless to our species and has attracted the attention of various scientists and approaches (e.g., Kramer 1991, Axelrod 1997, Dawes & Messick

2000). For instance, the conflict of “me versus we” is perhaps most famously explored in economics, in the form of the tragedy of the commons—i.e., the failure to maintain a public good because the individual gains the full and immediate benefits of use while sharing the cost of degradation (e.g., pastures, fisheries, pollution, see Hardin 1968).

Here, self-interest and (indirect) competition is presumed to trump cooperation, and largely because of the imbalance of incentives facing an actor. In fact, the most common view of this dilemma is to regard it as essentially an interplay of the incentives facing the actor—whether to compete or cooperate is portrayed as an analytical response to the structure of incentives.

Notwithstanding the usefulness of these approaches, their adoption of a predominantly rational view of this dilemma is excessively restricted. Whether an actor decides to compete or cooperate is presumed to be a question of reason and cognition, not

1 emotion, and so calculations appear relatively flexible and immediate, effectively uninfluenced by any deeper, latent structures of the human mind, such as our emotional proclivities. Indeed, much of the work on this dilemma within sociology and economics is based on a boundedly-rational decision maker weighing the tradeoffs, short-term and long, between cooperative and competitive behavior (e.g., Axelrod 1997, Frank 1988, Lin

1990, Runciman 1998, Burt 2000). Not surprisingly, much of the spotlight is placed on the logical nature of these tradeoffs, and so the human actor is effectively reduced to the role of a calculator, although, it is acknowledged, an imperfect one.

While no one would dispute the importance of cognition to our understanding of this dilemma, we believe there is much more that informs human decision making when it comes to questions of cooperation or competition. For example, while economists have long proposed property rights and incentive systems to resolve the tragedy of the commons (e.g., Ostrom 1990), it has been shown that traditional pastoralists are well able to sustainably manage their commons not with property rights but through norms of mutual reciprocity and what has been called emotional interdependence (Niamir-Fuller

2002). Also, work in social psychology suggests that while competition for shared resources may create conflict within organizational groups, it is highly influenced by categorizations of group membership (i.e. the salience of higher-order commonalities) such that in-group versus out-group delineations skew any incentives that may be present

(e.g., Kramer 1991, Dawes & Messick 2000).

In other words, emotional responses to social arrangements, operating alongside cognition, have a role to play in the explanation of competitive and cooperative behavior.

Moreover, neurologists have shown strong evidence that rational reasoning alone does

2 not enable humans to make good social decisions if divorced from their emotions (e.g.,

Damasio 1994, LeDoux 1996). And yet it is precisely the role of emotions that has largely been neglected in social dilemmas:

It's not that human beings are not rational – we are. The point is that we are not only rational. What makes us human is the addition of a rational mind to a preexisting emotional base (Massey 2002, 2; see also Nowak et al. 2000).

That emotions should matter to human decision making and behavior is receiving increasing attention amongst organizational scholars (e.g., Heath, Bell, and Sternberg

2001; Lawler, Thye & Yoon 2000). The role of emotions in cooperative versus competitive behavior, however, has been under-explored. It deserves particular attention because recent research into the constitution of human emotions and their origins in human evolution suggests that certain emotional responses are algorithmic in nature (Le

Doux 1996, Damasio 1999) and tightly intertwined with the dilemma of cooperation vs. competition that has been ever present throughout the evolutionary history of our species.

The human mind is deeply rooted with emotion-laden algorithms that help direct behavior in the presence of dilemmas in cooperation. For example, the work of neurologist Damasio (1999) is compelling in its depiction of the automaticity and evolutionary functionality of emotions. They are seen as

… complicated collections of chemical and neural responses, forming a pattern; all emotions have some kind of regulatory role to play, leading in some way or another to the creation of circumstances advantageous to the organism. (…) Notwithstanding the reality that learning and culture alter the expression of emotions, (…) they are biologically determined processes. (…) The considerable amount of individual variation and the fact that culture plays a role in shaping some inducers does not deny the fundamental stereotypicity, automaticity, and regulatory purpose of the emotions (Damasio 1999: 51).

Others have also suggested that emotions operate as an unconscious, “hard-wired” intelligence, which proved—on average—adaptive in the past (Plotkin 1993) and serve a

3 regulatory role. According to this view, emotions are behavioral predispositions that can offer survival advantage—such as the preparation for evasive or aggressive action in a threatening situation (see Griffiths 1997, Damasio 1999).

Focusing specifically on competition vs. cooperation, evolutionary theorists argue that the dilemma of taking care of “me versus we” is imprinted on our minds as rules-of- thumb or algorithms that operate through the arousal of emotions and have evolved to become programmed and inherited (Cosmides & Tooby 1992). Of course, these algorithms do not operate by emotion alone, but neither are they simply composed of conscious cognitive analysis—our mental capacities are simply not sufficient (e.g.,

LeDoux 1996, Damasio 1994 and 1999). These “me versus we” algorithms are triggered by generic social circumstances and, in turn, arouse emotions. As they are not emotions themselves, we will simply refer to them as emotional algorithms. What is important is that certain and specific social circumstances are capable of triggering emotional states which are experienced as ends in themselves, capable of giving satisfaction just through the fulfillment of accompanying behavior—for example, helping a friend in need can be emotionally satisfying in itself, regardless of the favors it engenders. Our basic argument is that these emotion-laden algorithms significantly inform the dilemma of competition versus cooperation and deserve our attention.

Relatedly, most theories of emotion address the basic emotions, such as fear, anger, joy, sadness, disgust, guilt, etc., and largely concern the release of these emotions through events that are singularly focused on an individual and the fulfillment of his/her interests, such as his/her success or failure to attain goals (Scherer 1988, Ekman &

Friesen 1971; Frijda 1993, LeDoux 1996). They do not, however, target social dilemmas,

4 such as competition versus cooperation. Certainly, emotions are also embedded in reoccurring social circumstances and are aroused not only by events related to the self, but also by specific, re-occurring relationships among group members.

The purpose of this paper, therefore, is to depict how each of us is endowed with specialized emotional algorithms that help us to balance our personal interests with group interests—so helping us to resolve the dilemma of cooperative behavior—and without having to make a fully conscious and calculative appraisal of each social situation (De

Waal 1996: 173). Organizational settings are particularly ideal for examining the operation of these algorithms because they contain suitable group settings for studying the dilemma of cooperation. As we develop below, these emotional algorithms include short term (myopic) pursuit of material goods and status-seeking behaviors, which encourage competition, as well as reciprocity and group identification, which encourage cooperation. Below we will first outline these algorithms and summarize the empirical evidence for them, before we show how they work together as a system for regulating group behavior. We will also derive empirical propositions. Finally, we will argue that these emotional algorithms—for competition and cooperation—balance each other and so, in as far as unrestrained competition or unquestioning cooperation may be destructive for groups and individual members, help stabilize and improve group functioning.

Indeed, we will suggest that the system of algorithms may have emerged over human history precisely because of the balance that it offers.

2. EMOTIONAL ALGORITHMS IN BALANCING “ME VERSUS WE”

Emotional algorithms, like the basic emotions of which they are constituted, follow stable patterns, are semi-automatic, functional, and tend to exhibit strong commonality across

5 cultures (although, of course, the “trigger points” of the algorithms are culturally defined and learned, as we shall argue).1 They form a system of action tendencies that help us to continuously balance the “me versus we” dilemma. They are summarized in Figure 1.

Competitive Cooperative Emotional Algorithms Emotional Algorithms Resource Striving Reciprocation Need the craving, and accumulation the longing to return to others in-kind and to of material goods and resources;- develop friendships, with the demand of often short term, myopic desires fairness from others and the spontaneous suspicion of a violation thereof

Status Seeking Group Identity Seeking the craving of status as an end in the longing to belong and associate with a itself group, and to protect and give advantage to those within your group

THE GROUP

THE ENVIRONMENT

Figure 1: Emotional algorithms for Competition and Cooperation

Before we describe these algorithms it is useful to first ask why just four emotional algorithms, and why these four in particular? While exhaustive lists are often hard to justify, there is some precedent for (these) four algorithms. Alan Fiske’s (1992) integrative account of the structures of social life is highly consistent with the four algorithms we describe. Fiske’s ethnographic work and theory concerns the way elementary human psychology informs the construction of social relations, even as an explanation for the diversity of human culture (Fiske, 2000), and so his research purpose, while considerably broader, is similar to our own. Moreover, Fiske’s (1992) four

1 Damasio (1999) calls this “stereotyped.” There is evidence that the emotional algorithms orchestrate the basic emotions by functioning as goal states: if resources, status, reciprocity or identification are fulfilled, an emotions of happiness ensues; if the goal is frustrated, anger, sadness or disgust follows, depending on the circumstances of power and legitimacy (see Scherer’s 1988 and 1997 appraisal theory).

6 elementary forms of human relations (market pricing, which is characterized by self- interested- but not necessarily selfish- exchange within a price-regulated market; authority ranking, which is characterized by our proclivity for linear hierarchies and differences in social importance; equality matching, which is characterized by egalitarian sensibilities and in-kind reciprocity; and communal sharing, which is characterized by unconditional sharing within a group that is characterized by a strong sense of common identity) strongly resemble the four emotional algorithms we describe (respectively, resource striving, status seeking, reciprocation, and group identity seeking). Like Fiske

(1992: 13), we do not claim that our elementary algorithms are exhaustive, only that they are sufficient to describe an enormous range of social behaviors, and this includes social dilemmas of competition versus cooperation. While Fiske emphasizes the mathematical ranking properties of these transaction principles for organizing social relationships, we emphasize (a) the connection to emotions (their algorithmic triggering of emotions through desires or basic goals), (b) the interdependent operation of the algorithms as a system in regulating the “me vs. we” dilemma, and (c) the evolutionary function of this system. Moreover, while Fiske (2000) proposes that the four principles form the basis of culture and that they are probably rooted in “universal psychological mechanisms,” he does not elaborate on the connection to human emotions., arguably the most basic capacity of the human mind (Damasio, 1999).

Of course, our ability to reason and calculate, and not just our emotional impulses, play a substantial role in how we manage the tradeoffs between competition and cooperation—certainly, incentives play a role in the tragedy of the commons and social dilemmas (e.g., Hardin 1968, Glance & Huberman 1994). Nonetheless, Damasio (1994)

7 has, in his research on emotionally disabled patients, shown strong evidence that our logical capacity is insufficient to resolve the cooperation versus competition dilemma.

Emotional forces have a strong influence and, while certainly bedfellows to reason, do not always pull in the same direction as reason, helping us to explain decisions and behaviors where reason alone cannot. Each of these emotional algorithms has, in isolation, been observed by researchers and established by empirical evidence. We briefly describe each in turn, before discussing them as a system.

COMPETITIVE EMOTIONAL ALGORITHMS

Striving for Material Resources

Broadly acknowledged and observed across the social sciences as a universal motivation is the fact that people seek to maximize their own benefits. For example, economists explain individual behavior as the striving to maximize, in some combination, instantaneous and delayed consumption of material goods (e.g., Becker 1976, 284).

Similarly, members of organizations work to avoid penalties but pursue various rewards which offer material benefits (e.g., money and resources), which, in turn, increase one’s capacity for consumption. They also pursue non-material benefits, namely the attainment of higher social standing or reputation (discussed below), although the conventional view is that this pursuit is largely motivated by the additional capacity a good reputation or higher status garners for the pursuit of further (material) rewards. Sociologists implicitly agree with this view of material rewards when they argue, for example, that actors pursue certain social ties simply to advance their careers (see Burt 2000), or that actors pursue

8 social status simply to increase their access to resources (Lin 1990). Greed, in other words, is certainly constituted by calculated thought aimed at material satisfaction.

However, greed also has an emotional side. The emotional side of greed is perhaps best revealed by the fact that the emotional energy of pursuing it often causes us to deviate from economic rationality by compelling us to satisfy current cravings at the expense of our own long-term interest, a phenomenon that Loewenstein (1996) called

“visceral factors”. We place little weight on future benefits and dangers versus current ones; their “time value” is emotionally discounted much more rapidly than finance theory prescribes (Herrnstein 1970). Visceral factors can be explained in evolutionary terms: the farther an organism attempts to calculate into the future, the (exponentially) higher the computational load (Ainslie & Herrnstein 1981), and the higher the chance of error because of uncertainty. In the environment of our ancestors, it may therefore have paid to concentrate the relatively scarce resources of the mind on immediate threats and environmental challenges.

While visceral factors serve us well on average, they can lead to questionable behavior. Selling methods, for instance, have long relied on impulsive, emotion-laden decisions by consumers, and so confectionary products of dubious dietary benefit are often presented in large quantities and within easy view and reach, many immediately next to the point of purchase, and everything from packaged holidays to television infomercials encourage “last minute” decision-making, before the emotional buzz of the offer has dissipated (e.g., Herrnstein 1970, Cialdini 1993). Or consider a recent program for highly indebted consumers who, while unable to save part of their earnings now

(because they could not resist impulse buying), were able to reduce their spending by

9 signing a contract that committed them to save some of their earnings several months in the future: the emotional force to spend future money turned out less overwhelming.

In fact, the only way out may be physical restraint, and so dieters avoid keeping junk food in the house, and smokers do the same with cigarettes (Frank, 1988)—this may be why many readers find the mythical story of Ulysses so compelling, who had his shipmates bind him to the mast so that he could not give in to the song of the sirens.

Reward systems in organizations can also stimulate this type of myopic behavior on the part of individuals. For instance, this has been recently observed in investment banking, where enormous bonuses are legend and job-hopping to increase monetary rewards became normal during the past decade. This mercenary conduct and the resulting reduction in expressed loyalty to the firm, however, may have made it much easier to lay people off when the financial markets collapsed (Atlas 2002, Gilpin 2002).

Status Seeking

Status structures are defined as rank-ordered relationships among actors. “They describe the interactional inequalities formed from actors' implicit valuations of themselves and one another according to some shared standard of value” (Ridgeway & Walker 1995,

281). Given the ubiquity and importance of status competition and status structures (e.g., see Barkow 1989, Loch et al. 2000), it is not surprising that over the years sociologists have examined the role and function of status structures. As noted, however, they have tended to emphasize status as a means to an end (Runciman 1998; examples of such interpretations are Bales 1955, Blau 1964, Lin 1990, Podolny 1997). 2 There is substantial evidence, however, that status is not only a means to an end, but at the same

10 time an emotionally driven end in itself. Evolutionary anthropologists have long recognized status competition as a driver that triggers emotions in our species (e.g.,

Barkow 1975, de Waal 1989, Chapais 1991, de Waal 1996), and a few sociologists have come to the same conclusion (e.g., Kemper & Collins 1990).

A recent empirical study demonstrates that subjects are willing to trade real money for ephemeral and short-lived status recognition that has no further benefits (Hub- erman et al. 2003). In a competitive bidding game with probabilistic outcome, participants could be induced to consciously “leave money on the table” by offering them a symbol of recognition (applause).

Researchers have found that this nice feeling corresponds to higher serotonin levels, which are both a cause and an effect of higher status, as demonstrated in studies of the relationship between serotonin levels and spontaneous changes in dominance in male vervet monkeys. McGuire and Raleigh (1985, 459-460) manipulated relative status and found consistently that the blood concentration of serotonin was high in dominant animals, fell sharply when they ceased to be dominant or were isolated, and rose when they either became dominant once again or for the first time. A similar effect has been observed in college fraternities: males in the highest leadership positions had the highest serotonin levels (Booth et al. 1989).

Emotionally driven status behavior has its roots in a general primate tendency toward social hierarchy, where evolution favors competition among group members (for food, mates, nesting sites) to be performed efficiently with as little injury or risk of injury as possible. Determining which of two competing individuals would likely win the

2 Expectation States Theory (e.g., Berger et al. 1977) examines the effects of status in groups, but not where the motivation for status comes from.

11 encounter, without actual fighting, leads to a status hierarchy in primate groups. Human prestige has developed from the primate status tendency, but has become symbolic.

Symbolic prestige can rest on a large number of criteria that are to a large degree culturally determined, such as skills and knowledge (that are relevant in a given environment), or the control of resources (Barkow 1989). But people crave respect and recognition in all cultures of the world.3 In other words, the striving for status is “built into us,” triggering basic emotions (such as anger, sadness, happiness) depending on whether status is achieved or not. But the criteria along which status is achieved and the symbols of status are cultural.4 In fact, while the basic nature and most common expression of status striving is competitive (it always involves the competition for a relative rank), it can sometimes be “tricked” into cooperative acts, for example when status is garnered in a local setting through the display of cooperative behavior, such as certain acts of heroism (rescuing a neighbor’s cat from a tree) or courtesy (offering someone the last seat on a bus) (see Section 4).

Going for the “brass ring” can create healthy competition in organizations. So, offering “status” can be highly motivating without being too costly, unlike the case of bonuses paid to investment bankers. The striving for status can be productive for an organization if it rests on criteria that are connected to productivity (Frank 1984), but can also be very unproductive if the criteria lead to political posturing (Loch et al. 2000).

Status games can also result in talents and contributions by some group members

3 In addition, Daly & Wilson (1988) report of a genealogical study of Portuguese noble families, which showed that status predicted the number of adult children, even after controlling for wealth. 4 At the same time, “primitive” status biases still (weakly) exist: tall men tend to be listened to more, are conceded more respect, and have, on average, better career progress than short men (e.g., Cialdini 1993, 181 - 182). Moreover, tall men tend to have more reproductive success, that is, there is active selection for stature in male partners by women (Pawlowski et al. 2000).

12 being overlooked. For example, in some companies it is necessary to have an advanced degree from the right school to be recognized or listened to. Or, women are often “not heard” where they hold a position of lower status in society. Indeed, there is evidence that women tend to seek status less overtly than men, which may (together with male networks of reciprocity) contribute to the “glass ceiling” in organizations (e.g., Tannen

1991, Maccoby 1998, Campbell 2002).

COOPERATIVE EMOTIONAL ALGORITHMS

Reciprocation Need

Cooperation takes many forms, but certainly one common instance of cooperation is the bestowal of favors—that is, acts which aim to help or benefit a recipient, and at some cost to the giver, but without any immediate exchange implied. The motivation for such altruistic acts has long been a puzzle for researchers (Trivers, 1971), and perhaps the most common explanation invokes reciprocity, that is the expectation that the favor will be returned at some unspecified point in the future. This is, of course, the same principle that underlies any rational transaction – I give you a good, and you give me another in return – a trade which can be largely understood in non-emotional and calculating ways

(e.g., what are the chances this person will return the favor if called upon?).

The transaction approach, however, raises a problem if the returned favor is delayed: most animals are not intelligent enough to foresee the possibility of the return, and humans (and intelligent primates) are subject to the temptation of free riding, that is, not returning the favor at all—or subtly cheating by returning slightly less. Enforceable contracts help to control this temptation, but such contracts are available only in rare circumstances.

13 Biologist Robert Trivers (1971) showed that reciprocity can arise even when the parties cannot foresee or commit to a returned favor. He showed in a simple game- theoretic model that cooperation emerges as a stable (programmed) strategy, in which individuals want to give a favor even without respecting a return. This can happen if a repeated exchange of favors does, in fact, represent a mutually beneficial arrangement.

But the individuals do not need to realize it; the intelligence is in the programmed behavioral algorithm, not in conscious decision making by the individual.

The conditions under which such “reciprocal altruism” can arise by evolution as a stable strategy in a population are as follows: the members of a population are mutually dependent such that they typically benefit from altruistic acts (the acts create sufficient value) and can productively return an altruistic act; they meet repeatedly over their lifetime (that is, they live long enough and are not too dispersed), and cooperators have sufficiently complex memories and senses to be able to detect, remember, and punish cheaters (see also Nowak et al. 2000). Empirical studies have since confirmed that animal populations that fulfill these conditions, including humans, tend to exhibit favors by individuals for one another (Trivers 1985, Cosmides & Tooby 1989).

The reciprocity algorithm helps us because we may not be able to see the future benefits from reciprocation, or because they are so far away (for our short-term oriented viscerally influenced mind) that we are unable to take them into account with our rational intelligence alone. Trivers explained how the reciprocity algorithm works by triggering emotions. If someone does something for me, I feel gratitude, and I like that person.

Thus, it makes me feel happy to do something for her, which then appears as “returning” the favor (even if I do not see a future return). While these positive emotions represent

14 additional benefits of reciprocating, they also deter free-riding: if I fail to reciprocate, I feel guilt, and the other side feels indignation (a version of anger) and may act aggressively in return.

Trivers (1971) also predicted that humans should be able to easily detect cheating—or rather that our minds were highly sensitive to, and searched for, evidence of cheating. Such sensitivity was subsequently observed by psychologists, who showed in experiments that people are much better in solving a logical puzzle if it involves a social contract in which one side may cheat (Cosmides 1989, Cosmides & Tooby 1992), and that people solve the puzzle in completely different ways depending on which side cheats

(Gigerenzer & Hug 1993). These researchers drew the conclusion that we have a

“cheating module” in our brain5: in situations of social exchanges, people are likely to make sharp observations, and to spontaneously be on the lookout for breaches of trust.

Economists and sociologists have also observed that material exchanges are not only rational transactions, but also emotionally driven (e.g., Frank 1988, Bester & Güth

1998, Lawler et al. 2000). For example, Uzzi (1996) followed market exchanges between

New York fashion retailers and their suppliers over time. He observed that relationships between firms (i.e., the people this involved) when successful, transformed over time from economic transactions to relationships based on “friendship and altruistic attachments” (Uzzi 1996, 681). The motivations of behavior changed from economic considerations to “doing something nice for my partner”. Uzzi called this phenomenon

“social embeddedness.” Interestingly, the nature of the interactions strayed so far from the initial economic rationale that a very high level of embeddedness reduced the survival

5 The interpretation of these results is still contested (e.g., Burnham 1997, Fodor 2000). While the cheating module results cannot be dismissed, more work is needed to settle the matter.

15 probability of firms. This once again demonstrates that emotional algorithms may deviate from rational behavior and may, while helping us on average, be harmful in some situations. In other words, they can help us explain social and economic outcomes which rational approaches cannot.

Another example of the reciprocity algorithm is fairness, which universally

(across cultures) raises strong emotions of anger when violated (Scherer 1997, Nowak et al. 2001, Sigmund et al. 2002). This has been shown experimentally in the Ultimatum

Game (Fehr & Gächter 2002): players are paired, and player 1 is given an amount of money, of which he can give a part away to player 2. Player 2 can accept the offer or reject, in which case neither player gets anything. The game is played anonymously

(there is no reputation effect) and one-shot (there is no future). Thus, the rational outcome would be for player 2 to accept any offer, and for player 1 to offer very little.

Yet, the empirically observed “focal point” is a 50-50 sharing, and deviations by player 1 in his/her favor provoke strong emotional reactions (anger, disgust) and “punishment” by player 2, who is willing to forego money although there is no possible future benefit from punishing.6 We are pre-programmed with a social algorithm to seek fair exchanges and feel an emotional benefit from enforcing them.

Indeed, organizational theorists have also observed that people compare their input (effort, expertise, experience) and outcome (rewards, recognition) with comparable others. If they perceive the ratios as unbalanced, they tend to become demotivated and possibly leave the firm (e.g., Adams 1963). Relatedly, the procedural justice literature

6 The position of the focal point of fairness is influenced by the cultural context – for example, status differences that the players have been told about. However, deviations from the focal point are overwhelmingly in the direction of selfishness of player 1, who has the power (sets the ultimatum), and are met, across cultures, with emotional reactions (Sigmund et al. 2002).

16 suggests that fairness in how decisions are reached is considered to be more important than actual outcomes when it comes to how humans perceive an event or situation (e.g.,

Lind & Tyler 1988).

Group Identification and Selection

A fundamental question of biology in the past century was how can altruistic behavior arise in biological evolution if the conditions for reciprocal altruism are not met

(consciously or emotionally), that is if there is no possibility of the favor being returned?

Hamilton (1975) was the first to give a rigorous answer: group selection.

Programmed behaviors may spread overall, even if they reduce the fitness of the individual within the group, if the behavior benefits the group of which the individual is a member. Group selection may outweigh individual selection if different groups with different compositions of the programmed behaviors are (partially) isolated and compete with one another.

Group selection may work at different levels of populations—for example, the behaviors may be programmed (or influenced) genetically, in which case “individuals” are the genes, and the population may be a single body, or a group of bodies. Group selection more commonly applies to culturally programmed behaviors, in which case the individual is the “behavioral rule” in question, and so the groups are isolated culturally rather than physically (see Sober & Wilson 1998, Chapters 2, 4).

Group selection makes it likely that an emotional algorithm arose in evolution which compels individuals to neglect their own interest for the benefit of the group.

There is some empirical evidence for how this algorithm plays out. In an experiment by

Devos et al. (2002), the identification with the group is so strong that events happening to

17 a fellow ingroup member are appraised and trigger emotions as if these events happened to the self. This would represent a powerful emotional trigger by which individuals are motivated to perform altruistic acts on behalf of fellow group members—albeit, members of a particularly close-knit group.

One instance of group selection and an associated emotional algorithm is nepotism, or kin solidarity: if an individual shares a significant number of genes with another, it may be in his/her interest to be altruistic, to maximize the total number of its own genes that survive. Hamilton (1963, 1964) coined the term “inclusive fitness” for this motivation of altruistic behavior, and it is widely observed in animals.

Favoring relatives is universally known among humans. Every culture knows a variation of the saying “blood is thicker than water” (e. g., Wilson 1998, 162). For example, family-governed firms have very different dynamics from publicly owned firms

(e.g., Magretta 1998, Economist 2001, Burt 2001). Child abuse and child murder are much more prevalent toward step children than natural children (Daly & Wilson 1988,

Chapter 3). In the culture of the highly aggressive Yanomamö tribe in the Amazon forest, in which men have a low life expectancy, women may have children from several men, not only their husbands. Here, men invest as much or more parental care into their sisters’ sons than into the sons of their wives, who may be fathered by someone else.

Apparently, they invest in the sons with the highest average degree of genetic relatedness

(Alcock 1989, 535 – 537).

While families are to some degree culturally defined (e.g., cousins are seen as kin in some cultures but not in others), kin solidarity is universal and partially driven by biological mechanisms (Wilson 1998, 199). For example, a shared early childhood

18 between a man and a woman seems to strongly suppress sexual interest, a mechanism that reduces the chance of (biologically harmful) incest because a shared childhood is strongly correlated with being blood relatives, also known as the Westermarck effect (e.g., Wilson

1998, 193 – 199). Some animals also rely on a shared childhood to recognize kin, while others are able to identify relatives by the smell of body odors (e.g., Alcock 1989, 480-

495).

Yet, a second instance of an emotional algorithm for group identification also exists for arbitrarily defined groups. Psychologists have long known that it is very easy to create group identity by channeling human interaction, even arbitrarily (Sherif 1966,

Tajfel 1970). Groups are spontaneously defined on any socially relevant criteria, especially status-relevant ones. Group identity helps create a positive attitude toward ingroup members and often a negative disposition toward outgroup members; ingroup members are viewed as differentiated individuals while outgroup members tend to be viewed anonymously, often as a stereotyped “category”; and there is a tendency towards minimizing in-group differences while maximizing the differentiation with the outgroup.

Individuals are more willing to help ingroup members (Tajfel 1982) and to view them as trustworthy and cooperative (Kramer 1991, Chatman et al. 1998).

Such group identity leads to hostility toward external groups if there is perceived interdependence or competition for some desired good (Kramer 1991). Strong group identity can lead to unconstructive behavior, often with the only aim being to disfavor the other group (e.g., Dawes & Messick 2000). Group identity can also lead to the dismissal of external ideas, one example being “Groupthink” (Janis 1971). This implies a need to

(re)construct boundaries in a way that group formation helps rather than separates groups

19 that should work together. Group identity also seems to lie behind the difficulties of virtual teams, where the emotional “triggers” for group solidarity and cooperation— because of a lack of face-to-face contact and the importance of location to group identity—cannot be easily activated (see Burnham 1997).

In society at large, ethnic differences are a natural basis for group formation and have systematically led to hostilities and violence over history. Ethnic riots can be extremely violent and tend to follow a stable pattern that shows evidence of a mix of calculation (for example, “test attacks” which minimize risk for the perpetrators) and emotional frenzy (including memory loss and moments of ecstasy) (Horowitz 2001).

Recently, Kurzban et al. (2001) have shown that racism (or ethnic aggression) is not distinct from ingroup-outgroup behavior in its mechanism. They were able to easily re- direct outgroup hostility away from race-based groups to groupings defined by other salient cues.

Sociologists have explained group identity by material or self esteem benefits

(e.g., Kramer 1991). Evolutionary theory offers an alternative hypothesis, which can explain the remarkable ease and intensity with which ingroups can be triggered in our minds by almost arbitrary criteria. Our hunter-and-gatherer ancestors lived in fission- fusion groups with rapidly changing factions and groupings. Flexibility and, at the same time, intensity of allegiance were necessary in order to allow the groups to act cohesively

(overcoming internal tensions) in the face of threats by the environment and neighboring groups. Thus, we are cognitively and emotionally prepared to readily identify with a new group along new criteria with intense emotional force (Barkow 1989, Goodall 1994,

Kurzban et al. 2001).

20 3. THE BALANCE OF EMOTIONAL ALGORITHMS: THREE PROPOSITIONS

The emotional algorithms described above are familiar to most of us and have, individually, been the subject of considerable research. We are becoming much better informed about, say, the lure of status on individuals or the powerful influence of one’s social group. What is missing, however, is an attempt to view these algorithms holistically, as a system. When viewed as a system, one of their most striking qualities is the dynamic balance they provide to the dilemma of cooperation versus competition, particularly in the context of groups. We argue that the unfolding of competition and cooperation within human groups is kept in some degree of balance through the joint influence of these emotional algorithms. Below we develop three propositions that consider how these algorithms provide balance between competitive and cooperative behavior within groups.

As starting point, it may be useful to once again consider the role of emotionally driven behaviors within groups alongside the role of reason or calculation. It is vital that we regard both as simultaneously operating, enduring and systemic forces with a continuous, and non-random, impact on group functioning. Both are continuously engaged and continuously inform behavior but need not always point in the same direction (one may over-rule the other). Frank (1988, 51-53) provides a useful example.

“Behavior is governed by a psychological reward mechanism, (…) and rational calculations play only an indirect role. (…) Suppose a hungry person calculates that being fat is not in his interests. The rational calculation informs the reward mechanism that eating will have adverse consequences. This prospect then triggers unpleasant feelings. And it is these feelings that compete directly with the impulse to eat. Rational calculations, understood this way, are [only] an input into the reward mechanism. Feelings and emotions are, apparently, the proximate causes of most behaviors.” [See also Barkow 1989, Chapter 15].

Emotional algorithms are also universal across cultures in that evidence of these basic algorithms can be found in all cultures. Local cultures, however, shape the actual

21 experience of these algorithms, their “trigger points” for the release of emotions and the detailed shape of responses. Scherer (1997), for example, observed that culture causes

“variations around a universal theme” in how emotions are triggered. Relatedly, Cialdini

(1993; 2001) has empirically identified a set of emotional “levers of persuasion.” They coincide with the emotional algorithms identified here and are common across different cultures (again, only the “trigger points” differ, see Cialdini 2001, 67). In sum, a considerable amount of research has pointed to the universality of certain human emotional algorithms but which are expressed in culture-specific ways (see also

Campbell, 2002).

There have been, however, some dissidents. For example, Markus and Kitayama

(1991) argue that the construal of self is fundamentally different in Western versus

Eastern culture, and consequently, ‘different’ emotions are at work. However, an alternative interpretation of their work is that, on average, different cultures may find somewhat different equilibria, or balance points, across the basic algorithms. For example, in Eastern culture “greater value is placed on proper relations with others, and on the requirement to flexibly change one’s behavior in accordance with the nature of the relationship” (p. 228), that is, a greater weight on reciprocity. Also, sensitivity to others is mostly accorded to a select group of people (ingroup) where one has an expectation of reciprocity, and it takes great self-control and agency to adjust oneself to various interpersonal contingencies (p. 228). Cravings for material resources and status, on the other hand, are relatively suppressed, that is not finding as easily cultural channels for expression. This does not mean that these algorithms are somehow non-existent in such peoples and unexpressed within the culture, only that cultures may differ in the balance

22 they strike between the expression of competition and cooperation.7 The opposing and contemporaneous forces of these emotional algorithms remain intact, even though cultural circumstances and local conditions may emphasize one algorithm over another.

We summarize this discussion with Proposition 1.

Proposition 1. (a) The balance of collaborative versus competitive behavior within groups is influenced not only by the rational pursuit of future individual benefits, but also by biologically programmed emotional algorithms (desire for resources, status, reciprocity and group identity). (b) These emotional algorithms serve as psychological goals that trigger emotions when fulfilled, missed or obstructed. (c) The emotional algorithms are universal across cultures, although cultures define both the “trigger points” by which the algorithms are activated and the balance points for the cultural expressions of competition and cooperation.

One of our central claims is that emotional algorithms may operate simultaneously, interacting and influencing one another as they impact behavior within groups. This can be summarized through a system dynamics diagram (see Figure 2).

Looking first at the dynamics surrounding each emotional algorithm, we notice several self-reinforcing loops. For example, resource striving may experience positive reinforcement (top left loop), where every member may act mainly out of self-interest, with very limited collaboration across members, as in the case of spot-market transactions. This is similar to Goffee & Jones’ (1996) organizational culture framework—a two-dimensional framework that examines interest commonality

(solidarity) and mutual friendship relationships (sociability)—where this loop

7 Similarly, Kim & Nam (1998) argued that the “dynamics of face” are uniquely Asian in nature. However, “face” can be interpreted as status striving; it simply represents the culturally defined status

23 corresponds to the “mercenary culture,” that is where people are motivated largely by self-interest and group cohesion is manifest only as far as members’ interests overlap.

Resource

+ + – scarcity

– + Resource Reciprocity, striving friendship

–

+ +

+ + – Status, Group

–

recognition identification +

–/ + –/ + + External Threats

Figure 2. The emotional algorithms form a dynamic system

Status recognition may also experience positive reinforcement (bottom left loop). Strong status competition in a group reinforces itself as each tries to outdo the other in obtaining some symbol of higher status—the more entrenched status symbols become, the more people want them (i.e., “status competition” Frank, 1984). The status striving itself is universal, but the symbols are numerous and vary across organizations and cultures, including, for example, money, competence and skills (e.g., the number of papers or patents in a research organization, or persuasive skills in a sales organization), resource control and power (size of budget or headcount, size of one’s office), or closeness to powerful actors (“I had the CEO over for dinner last night”).

indicator. Drives for group are balanced with the importance of giving “face” to the individual, and while cooperation exists within groups, competition can be fierce with out-groups.

24 Status and resource striving also reinforce each other, such that gains in resources lead to gains in status, and gains in status lead to gains in resources. That is, control and wealth themselves represent status-carrying symbols in many cultures (Frank 1999,

Barkow 1992), and status tends to translate into an enhanced capacity to secure power and wealth (e.g., Thye 2000). The entire left-side of Figure 2 forms a feedback loop combining differentiated status with differentiated compensation. For example, the lower status people may have to be “compensated” for their rank to prevent them from leaving

(leading to salary compression), but it is also possible that the lower status group members are so proud of being together with the high status members that they are willing to forego some compensation (Frank 1984).8 In sum, the left-hand side of the diagram represents an integrated system of forces which, in general, boost the expression of competition by individuals within groups.

Similarly, the right-hand side of Figure 2 also contains positively reinforcing loops. So, the loop reinforcing reciprocity (upper right) represents an equilibrium of ties that compel people to cooperate for the sake of their friends, which creates closer friendships, which in turn garners greater cooperation on behalf of these friends, and so on (this corresponds to the second dimension (sociability) in Goffee & Jones’ 1996 framework, see also Uzzi 1996). Indeed, the tendency to reciprocate at least in-kind can result in modest escalations of helpful behavior—indeed, as Marcel Mauss suggests

(1950), there is competition within reciprocity, prompting gift recipients to at least return the generosity and not be outdone. The outcome, however, is enhanced solidarity.

Similarly, collaboration that rests on group identity (“we are an elite”), and particularly

8 E.g., Toyota in Japan does not need to pay high salaries to attract the best people because working there gives status; Frank (1984) calls this the “high school reunion effect.”).

25 where it is reinforced by external threats—which can loom larger as the group becomes more cohesive and its elite status threatening to outsiders—can be self-reinforcing (lower right loop). This has been used by company leaders to mobilize their “troops against an external enemy” (recall the motorcycle wars of the 1980s between Honda and Yamaha, when Honda management galvanized its troops with the slogan “kill them, destroy them, bury them,” see Abegglen & Stalk 1985), and it has also been observed as a syndrome of teams that lose touch with their environment (e.g., Levy 2001). Finally, there is also a relationship between reciprocity and group identification, namely strong identification with the in-group tends to strengthen the willingness to appreciate and engage in reciprocal relationships (e.g., Kramer 1991). Thus, the right-hand side of the diagram represents another integrated system of forces which, in general, boost the expression of cooperation by individuals within groups.

Thus, both cooperative and competitive systems (the right and left sides of the systems diagram) contain self-reinforcing processes, resulting in at least the maintenance and possibly the amplification of cooperative/competitive behavior. Alone, these systems could spiral out of control, as we will consider below. Yet, more often than not, these sets of emotional algorithms are kept in balance through the contemporaneous influence of the each other. This does not mean that there are no “brakes” in place within each system. For example, an exclusive focus on individual rewards and self-interest is limited by a possible decline in group performance, and thus the absence of rewards themselves, and groups tend to lose their reciprocating character as they grow, as friendship ties become too difficult for individual members to map and so the possibility of monitoring social cheating is much weakened. Nonetheless, an additional, and

26 powerful, attenuating influence on cooperative and competitive behavior is the cotemporaneous presence of the other set of emotional algorithms. This balancing influence is represented by the lines that run across the middle of Figure 2. We can describe various ways in which this balance is played out.

First, status differences weaken reciprocity and friendship, and vice versa, as there is no balanced relationship possible between individuals of very different rank (Trivers

1971). One creates a natural dampening force on the other, and so any tendency to, say, constantly reciprocate another’s act of generosity would be dampened by status-striving and differences (e.g., a middle manager invited to dine in the home of a senior executive may not be expected to return the favor; indeed the dampening of another’s desire to reciprocate may itself be a symbol of status). On the other hand, acts of reciprocation and friendship amongst group members may help dampen status-striving by individuals.

Reciprocity and friendship also erode the competitive striving for resources—it is simply more difficult to compete for resources against a friend, and so the master who has become the friend of the servant can no longer treat him as he wishes. Finally, a strong group identity tends to reduce status differences within the group, emphasizing commonality (Tajfel & Turner 1986), while highly engaged status-striving will tend to erode the bonds of the group. This is not to say that status-striving will always extinguish group identity. For example, a “halo effect” may be evident, where the high status of one group member brings recognition, fame, and so solidarity to the entire group, as others relish being a member (Frank 1984, Cialdini 1993, Tajfel and Turner 1986).

Nonetheless, excessive status-striving will, under many if not most circumstance, erode group solidarity. On the whole, cooperative and competitive systems, composed of

27 emotional algorithms, serve as dampening forces on one another, naturally maintaining some degree of balance between cooperation and competition within human groups.

Finally, it is useful to consider the influence of exogenous environmental conditions on the degree of balance between cooperation and competition. For example, worsening resource scarcity and a growing group size heighten the potential for interest conflicts, decreasing reciprocity (Dunbar 1996b demonstrated this for animal populations and Pierce and White 1999 for organizational groups). The presence of external threats and a high mutual dependence, in contrast, heighten the benefit of cooperation (pointing to the enemy prompts the feeling of solidarity, Pierce & White 1999). Strong group cohesiveness, in turn, increases the tendency to perceive the environment as a threat

(Levy 2001).

This naturally leads to a consideration of the dynamics of such a system. Indeed, interdependence and the impact of external events imply that the collaborative balance in a group evolves dynamically over time, constantly being re-balanced by (often unpredictable and sometimes quite small) events over the history of the group—in other words, the exact position a group finds itself in with respect to the degree of competitiveness or cooperativeness of its members will be dynamic and path dependent.

Consider the following hypothetical but representative example of a project team: an external threat emerges in the form of a budget cut threatening project continuation. This welds the group together against a common enemy, and in a concerted action the team lobbies and successfully produces a counterproposal for continuation (heightened group identity). But after the threat has passed, the team bickers over how credit should be shared—the most senior person claims ownership of a decisive analysis that was

28 performed by a junior team member (status competition). The conflict is diffused when the junior team member heavily pitches in to help the senior person quickly respond to an urgent special request from upper management. The senior member now protects the younger person (“he saved my neck!” – reciprocity). Thus, the focus of behavior in resolving dilemmas of “me versus we” shifts repeatedly, engaging different expressions of these basic emotional algorithms, although, eventually, buffeted by the countervailing influences of alternative emotional algorithms which, as a system, may find some equilibrium that depends on the cultural context and the characters in the group.

While one cannot predict an equilibrium without knowing a lot about the context, we can predict that certain group states are very unlikely to ever occur—for example, if all benefits of the group go to a subgroup, a violation of fairness will compel other group members to resist. The opposite is also unlikely; perfectly equal sharing of group benefits can only be achieved under very strong (and strongly enforced) norms of behavior, otherwise, the group members with more power will usually give in to the temptation to enforce an unequal sharing. This has an implication for organizational cultures – certain behaviors occur almost spontaneously, while others, although desirable, can be achieved only through very strong reinforcement that may be too costly.

In general, this discussion leads to Proposition 2.

Proposition 2. (a) Emotional algorithms represent a system in which several algorithms may act simultaneously, whether upon a single individual or through distinct algorithms being experienced by different individuals within the group. A group’s collaborative behavior changes dynamically over time, influenced by events and group composition. (b) While this evolution is path-dependent and cannot be fully predicted, there are moments of intense focus on one or a few algorithms, where interactions temporarily

29 emphasize individual rewards, status, both resources and status, friendship, or group identity. Over the longer run, however, the system of emotional algorithms has a balancing tendency, with human groups resorting to some cultural-specific equilibrium or balance point.

We have, so far, always spoken of some form of balance between the expression of competitive and cooperative algorithms. It is possible, however, that regulatory forces may be, at times, impeded within a group and so the system in Figure 2 fails to regain its equilibrium and spirals into ever more extreme behavior. Leaving aside the possibility of an environment which, at least in the medium to long run, selects for extreme behavior in groups, the consequences of extremism is likely to be destructive. Moreover, the fact of such extremism is, in practice, almost impossible to explain rationally.

For instance, despite the surprise that recent accounting scandals have generated, excessive greed is not new. Unchecked power and status may lead to unlimited pursuit of self-interest, and there is much evidence in human history. For example, powerful kings have committed acts of horrific cruelty and megalomania—a ruler who beheaded subordinates to entertain guests, a duke who starved his people to build a castle of a gigantic scale, and so on (see Daly & Wilson 1988). Top managers may also suffer from extreme behavior. Hayward and Hambrick, for example, find that corporate acquisitions tend to produce firms that are “too large” in terms of efficiency, because CEOs are motivated by the pursuit of status associated with presiding over a larger company

(1997). In both cases, the ruling body is placed in jeopardy, as is the group.

Unlimited reciprocity, or friendship, may also be extreme to the point of risking one’s life. Friendship and mutual commitment among soldiers in a battle group is a key

30 driver of braveness—they risk their lives for not letting down their comrades more than for the flag, a fact that military training utilizes in boot camps (Fukuyama 1998, 37); similarly, reciprocity drives cohesion in drug gangs (e.g., LeBlanc 2003, 64). Reciprocity may also have undesirable effects—for example, the German term “Seilschaft” (literally, a mountaineering party sharing a rope) refers figuratively to a group of friends who support one another at the expense of the organization or group as a whole. In many ways, Janis’s (1971) “groupthink” phenomenon is a good example. Here, the psychological drive for consensus and mutual, reciprocated agreement denies the group the advantages of alternative ideas and opinions and, in the name of cohesiveness, drives them towards sub-optimal decisions.

Unlimited group solidarity, to the point of giving up the self, has also been observed. A combination of devotion to the ingroup and hating the outgroup has motivated kamikaze pilots to crash their planes into American ships, and Tamil suicide bombers in Sri Lanka to kill members of the hated group as well as themselves (e.g.,

Horowitz 2001, Waldman 2003). In summary, there is evidence that all emotional algorithms may run to the extreme if not balanced by the others. Moreover, the long term survival of the individual and group is, in many instances, at considerable risk.

Proposition 3. The balance of self versus group interests can derail and lead to extreme behavior: self interest that endangers the group and even the individual, or altruism leading to the destruction of the individual and, possibly, to the detriment of the group. In the long run, selection pressures will favor some semblance of balance, even though the exact balance point will vary.

31 4. EMOTIONAL ALGORITHMS AND HUMAN EVOLUTION : IMPLICATIONS AND FUTURE RESEARCH DIRECTIONS

It bears considering why our species seems to have developed balancing emotional algorithms. This is an ambitious consideration, and one whose full resolution is beyond the scope of this paper. Yet, it is likely that the balanced, hard-wired emotional algorithms described above have an evolutionary explanation.

There is enough evidence about the evolutionary benefits of the behaviors prompted by the emotional algorithms, and about their collective balance, to hypothesize that they co-evolved as a system. Evolutionary Psychology suggests that this system of both cooperative and competitive emotional algorithms is “bred into us” as the result of two competing selection pressures: individual and group level selection (e.g., Sober and

Wilson 1998). On the one hand, individual selection—that is, which one of us succeeds in passing the most copies of his or her genes into the next generation—favors competitive instincts. Individuals who were better equipped by nature with emotional states that improved their ability to compete fiercely for resources and status would have, all other things equal, enjoyed survival advantages over their peers and enhanced procreation.

On the other hand, group selection pressures9—that is, the survival of entire groups over others because of some inherent property of the group—is likely to favor cooperative instincts. Individuals who were better equipped by nature with emotional states and concomitant social desires (friendship and group identification) that improved their ability to cooperate placed their group in advantageous positions with respect to other groups. If this advantage allowed cooperative groups to out-compete less

32 cooperative ones—for example, by sharing food during hard times, or by collaborating during hunts and battles—individuals with cooperative desires, constructing cohesive groups, could spread (procreate) faster than those who are less cooperative, and in spite of being vulnerable to exploitation within the group. 10

Indeed, so robust may have been the selection pressures that favor the balanced system of algorithms that some of them have even evolved “defenses” against being manipulated or overwhelmed. For example, and as we noted, there is evidence that the collaborative algorithms have done so because the behavior that they prompt makes the individual vulnerable to exploitation. Thus, the strong emotional reactions to violations of fairness and our hard-wired ability to quickly detect and process evidence of social cheating represent “safeguards” to keep exploitation and free-riding in check (e.g.,

Cosmides 1989), which in turn allows for more collaborative behavior.

In summary, we have argued that a combination of individual level selection pressures, favoring competitive instincts, and group level selection pressures, favoring cooperative instincts, had a hand in ensuring some balance amongst the emotional algorithms that are part of human psychology (Sober & Wilson 1998).

We need to be careful, however, not to imply that the transmission of emotional algorithms and the balanced way in which they inform human behavior is entirely a question of evolutionary biology. This is unlikely to be the case. First of all, this is

9 Building on Trivers (1975), Sober & Wilson (1998) generalize the group selection argument to “multi- level selection” and give evidence that genes, individuals and social groups can be units of evolution. 10 One other reason, in addition to the empirical observations, for proposing four emotional algorithms (and not 2 or 6) may have to do with the requisite complexity such a system offers but at reasonable developmental cost. As our system analysis in section 3 suggests, the interactions among four algorithms (two on each side of the “me vs. we” dilemma) allow sufficiently varied and complex combinations. Only one algorithm on each side would not be complex enough, while more than two on each side would not offer substantially higher flexibility versus the high cost of maintaining additional psychological mechanisms.

33 because emotional algorithms are not, once again, simply synonymous with emotional states—they are also cultural. Every emotional algorithm that we illustrated can be connected to certain ideas—that is, the various symbols and criteria of wealth, status, friendship, group identity, and so on—and articulated or described as a rule operating within a social context. For example, while we may experience a strong desire to obtain an object of status, the definition of that status object, for the most part, is a social construction. In other words, the strong emotions that we feel and which impel us to act in various ways are intimately tied to local triggers in culture. It is hard to imagine, therefore, answering questions like “Where do emotional algorithms come from?” and

“Why do we have the emotional algorithms that we do?” without also understanding something about the evolution of culture and ideas.

By cultural evolution we mean more than simply the observation that cultural triggers—that is, the specific objects of status, sources and symbols of wealth and power, prompts for reciprocity, the various expressions of group solidarity—change over time, and so education and independence of mind may carry more weight today as objects of status than they did for Victorians, while nepotism has become a less common, or at least more heavily disguised, expression of group solidarity. Cultural evolution should also concern a different sort of change, that is how new associations of powerful emotions and local ideas are wrought together to form these re-occurring, algorithmic behaviors, and so how some triggers supplant others within each of the four basic algorithms. Again, it is because emotional algorithms consist of both basic emotional states—with a long evolutionary timescale—and cultural triggers and contexts—with relatively shorter, but not ephemeral, timescales—that the study of cultural, and not just biological, evolution is

34 necessary. Indeed, at any point in time there will be multiple cultural triggers and expressions within each of the four basic algorithms, some of which will attract greater attention and emotional grip and become relatively common, seen and transmitted widely, while others, lacking such attention, will die out. Cultural evolution would concern, amongst other things, both the genesis of novel triggers and then why some are selected and retained while others discarded. Once again, we see that to answer the question of why we have the exact emotional algorithms that we do, and why do they exhibit balance along the broad dimensions of competition and cooperation, we need to be careful to include alongside evolutionary biology the possibility of cultural evolution

(see Weeks and Galunic 2003, Dennett 1995, Dawkins 1992). This strikes us as an essential, albeit complex, undertaking for future research, parsing-out, and then integrating, how not only biology begets culture but how culture begets culture and, possibly, how culture begets biology (see Ridley, 2000:185-194). It may be important not least because we strongly suspect that any attempt to explain why we have the emotional algorithms that we do will need to bridge these two worlds. It is also convenient that emotional algorithms provide a reasonably tractable arena for such integration and study.

Fiske (2000) has suggested a promising starting point for this research agenda: the four algorithms (or principles) may function like a “universal grammar” in language, in the sense that they provide “templates” that constrain cultural interactions into categories, yet allow essentially an infinite variety of “parameterizations” of trigger symbols and importance weights. This combinatorial system may permit the full range of observed cultural diversity while limiting the computational learning demands on the brain.

35 Finally, this paper offers a new angle on the age-old dilemma of managing self and other-regarding interests within business groups, which has been the subject of study by economists, organizational theorists and sociologists alike. The evolutionary argument is that selection operating both at the individual and at the group level has forced our species to balance competition and cooperation. Arguably, organizational groups and teams are not much different. For example, decision-making and negotiation is found to achieve the best outcome when individual as well as group interests are met

(Schwieger et al, 1989; Fisher & Ury, 1983).

From a normative viewpoint, the suggestion is that managing this tension or dilemma is worthwhile and the aim of managerial work. This means, on the one hand, encouraging competition—access to status and recognition and material rewards in non- equal measure—and so ensuring motivated individual members by giving answer to the question “what’s in it for me.” In other words, team members need to feel valued for their unique input and given appropriate recognition and rewards vis-à-vis other members. One source of group dysfunction, therefore, may be where teams fail to adequately recognize unique contributions by members. On the other hand, team solidarity must be developed, encouraging familiarity through social bonding events, developing a team identity, and strengthening a sense of common purpose and mission.

Sometimes, groups benefit when members place their interests secondary to others or the interests of the group as a whole, and so nurturing selfless cooperation is also important.

In other words, both competition and cooperation are natural to human beings and have an important role to play in regulating group behavior (see also Uzzi, 1997:42).

The usefulness of these insights becomes apparent when considering the

36 contradictory and confusing advice given to group leaders: should incentives sharpen the distinctions amongst members (e.g., see Désgagné 1999) or not (e.g., Kohn 1993)?

Should teams be cohesive and “cult like” (e.g., Gupta & Govindarajan 2001), or are cohesive teams dangerous (e.g., Levy 2001)? The wisdom from human evolution may be that only in managing the tension between competition and cooperation, and so encouraging and tolerating both in some measure, will groups operate at their best. This means ridding ourselves of the fallacy that just because competitive and cooperative instincts come naturally to humans they will be adequately manifest and balanced in every human group—this is clearly not true, and so, for example, managers may become over-ideological with one approach, over-emphasizing one force to the neglect of the other and to the ultimate detriment of the group. Or, as we depicted, one dynamic may spiral out of control and suffocate its alter. The point is that groups and teams do benefit from sound management: Human evolution has seemingly given us two, rich dynamics which help motivate members and regulate group functioning, and so the lesson for managers may be to embrace and utilize both, in the balance necessary given the task and competitive context at hand.

5. CONCLUSION

This paper has argued that emotional algorithms that have become programmed, with the help of evolution, to manage the dilemma of taking care of me versus we. Survival as a group seemingly requires that both self-interest and group interest must be balanced in dynamic interaction. We have provided recent evidence from theory and research in evolutionary biology to demonstrate the existence of these emotional algorithms and how they may operate in organizations. Furthermore, we have described how these emotional

37 algorithms may interact in order to create the dynamic balance required. In fact, seen from this light, the “dilemma” of competition versus cooperation is not really a dilemma at all; rather, the tension inherent to these opposing modes of behavior, which at any given moment may pose tricky questions for individuals, is in fact a necessary, natural, and important part of human development.

Emotional algorithms themselves are a combination of basic emotions and social conditions that have a programmed quality. The basic emotions have been well examined and described by the psychology and neurology communities but primarily at the individual level. An emotional algorithm, for example status seeking, enters as a goal into emotional appraisal and leads, for example, to joy when fulfilled, to anger when obstructed, or to sadness when lost. Or the algorithm may be considered as an action or behavioral tendency, which is the cause or effect of emotions. The relationship between the emotional algorithms and emotions is still under discussion in the academic community (e.g., Frijda 1993). Nevertheless, emotional algorithms are considered to be programmed and universal, and in much the same way as emotions. So while cultural preferences for pursuing self versus group interests are clearly evident, for example in individualist versus collectivist nations (Triandis 1972), or the importance paid to power and status in different regions of the world (Hofstede 2001), nonetheless, the basic drives described by these four algorithms exist within all cultures and are the building blocks for cultural tendencies.

We have argued that being aware of these emotional algorithms can help managers to maintain a constructive balance of competition and cooperation, supported by reading and understanding the sources of nonverbal clues of emotional arousal. We

38 have also argued that the interaction between the emotional algorithms and the evolution of cultural rules is an exciting open research area, which may illuminate the link between culture and human nature.

REFERENCES

Abegglen, J.C., G. Stalk. 1985. Kaisha, the Japanese Corporation. New York: Basic Books. Adams, J.S. 1963. Toward an understanding of inequity. Journal of Abnormal and Social Psychology, 67, 422-436. Ainslie, G., and R. Herrnstein. 1981. Preference reversal and delayed reinforcement. Animal Learning and Behavior, 9, 476-482. Alcock, J. 1989. Animal Behavior. 4th ed., Sunderland, MA: Sinauer. Atlas, R. 2002. How big banks chased a mirage. International Herald Tribune, May 28. Axelrod, R. (1997). The Complexity of Cooperation. Princeton, NJ, Princeton University Press. Barkow, J. H. 1975. Strategies for Self Esteem an. d Prestige in Maradi, Niger Republic. In T. R. Williams (Ed.), Psychological Anthropology. The Hague: Mouton, 373 – 388. Barkow, J. H. 1989. Darwin, Sex and Status. Toronto: Toronto University Press. Barkow, J. H. 1992. Beneath new culture is old psychology: gossip and social stratification. In: Barkow, J. H., L. Cosmides, J. Tooby (eds.): The Adapted Mind. Oxford: Oxford University Press, 627 - 638. Bester, H., and W. Güth. 1998. Is altruism evolutionarily stable? Journal of Economic Behavior and Organization 34, 193 – 209. Blackmore, S. 1999. The Meme Machine. Oxford University Press. Booth, A., G. Shelley, A. Mazur, G. Tharp, and R. Kittok. 1989. “Testosterone, and Winning and Losing in Human Competition,” Hormones and Behavior, Dec., 556 – 571. Boyd, R., P. J. Richerson. 1985. Culture and the Evolutionary Process. Chicago University Press. Boyd, R., P. J. Richerson. 1992. Punishment allows the evolution of cooperation (or anything else) in sizable groups. Ethology and Sociobiology 13: 171 – 195. Burnham, T. W. 1997. Engineering altruism: an empirical investigation of anonymity and gift giving. Working paper, Harvard Business School. Burt, R. 2000. The network structure of social capital. Research in Org. Behaviour 22, 345 – 423. Burt, T. 2001. Families remain firmly in the driving seat. Financial Times, March 26, 9. Campbell, A. 2002. A Mind of Her Own: the Evolutionary Psychology of Women. Oxford: Oxford University Press. Chapais, B., 1991. Primates and the origins of aggression, power and politics among humans. In J. D. Loy and C. B. Peters (eds.), Understanding Behavior, Oxford: Oxford University Press, 190 - 228. Chatman J. A., J. T. Polzer, S. G. Barsade, M. A. Neale. Being Different Yet Feeling Similar. Administrative Science Quarterly 43 (4), 749 – 780. Cialdini, R. B. 1993. Influence. New York: Morrow. Cialdini, R. B. 2001. The science of persuasion. Scientific American, February, 62 – 67. Cosmides, L. 1989. The logic of social exchange: has natural selection shaped how humans reason? Studies with the Wason selection task. Cognition 31, 187 – 276. Cosmides, L. and J. Tooby. 1989. Evolutionary Psychology and the Generation of Culture. Ethology and Sociobiology 10, 51 - 97. Cosmides, L. and J. Tooby. 1992. Cognitive adaptations for social exchange. In: Barkow, J. H., L. Cosmides, and J. Tooby (eds.): The Adapted Mind. Oxford: Oxford University Press, 163 - 228. Daly, M. and M. Wilson. 1988. Homicide. New York: Aldine De Gruyter. Damasio, A. 1994. Descartes’ Error. New York: Putnam. Damasio, A. 1999. The Feeling of What Happens. Harcourt Brace. Dawes, R., and D. M. Messick. 2000. Social dilemmas. International J. of Psychology 35 (2), 111 – 116.

39 Dawkins, R. 1975. The Selfish Gene. Oxford: Oxford University Press. Dawkins, R. 1982. The Extended Phenotype. Oxford: Oxford University Press. Dennett, D.C. 1995. Darwin's Dangerous Idea. New York, Touchstone, Simon and Schuster. Devos, T., L. A. Silver, D. M. Mackie, and E. R. Smith. 2002. Experiencing Intergroup Emotions, in: Mackie, D. M. and E. R. Smith (eds): From Prejudice to Intergroup Emotions: Differential Reactions to Social Groups, Philadelphia, PA: Psychology Press. De Waal, F. 1989. Chimpanzee Politics. Johns Hopkins University Press. De Waal, F. 1996. Good Natured: The Origins of Right and Wrong in Humans and Other Animals. Cambridge, Mass.: Harvard University Press. De Waal, F. 1999. The end of nature versus nurture. Scientific American 281, December, 56 – 61. Desgagne, B. S. 1999. How to restore higher powered incentives in multitask agencies. J. of Law, Economics and Organization 15(2), 418 – 433. Dunbar, R. I. M. 1996a. Grooming, Gossip and the Evolution of Language. Cambridge, Mass.: Harvard University Press. Dunbar, R. I. M. 1996b. Determinants of group size in primates: a general model. Proceedings of the British Academy 88, 33 – 57. The Economist. 2001. Families in the boardroom: Under the influence. November 17, 65-66. The Economist. 2003. Jolly Boating Weather. January 4, 53. Ekman, P., and W. V. Friesen. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17 (2), 124 – 129. Fehr, E., S. Gächter. 2002. Altruistic punishment in humans. Nature 415, January 10, 137 – 140. Fisher, R. and Ury, W. 1982. Getting to Yes. London: Hutchinson Business. Fiske, A. P. 1992. The four elementary forms of sociality: Framework for a unified theory of social relations. Psychological Review 99 (4), 689 – 723. Fiske, A. P. 2000. Complementarity theory: why human social capacities evolved to require cultural complements. Personality and Social Psychology Review 4 (1), 76 - 94. Fodor, J. 2000. Why We are so Good a Catching Cheaters. Cognition 75, 29 – 32. Frank, R. H. 1984. Interdependent preferences and the competitive wage structure. RAND Journal of Economics 15, 510 – 520. Frank, R. H. 1988. Passions Within Reason. New York: W. W. Norton. Frank, R. H. 1999. Luxury Fever. New York: W. W. Norton. Frijda, N.H. 1993. Moods, emotion episodes, and emotions. In M. Lewis and J.M. Haviland (eds) Handbook of Emotions: 381-403. New York: Guildford. Fukuyama, F. 1998. Women and the Evolution of World Politics. Foreign Affairs 77, Sept.-Oct., 24 – 40. Garcia-Prieto, P., E. Ballard, and S. C. Schneider. 2000. A dynamic model of diversity, emotion and conflict in teams. Paper presented at the 2nd International Conference on Emotions in Organizational Life, August 10 – 11, Toronto, Canada. Gigerenzer, G., K. Hug. 1993. Domain-specific reasoning: social contracts, cheating and perspective taking. Cognition 43, 127 – 171. Gilpin, K.N. 2002. Bonuses start to look thin again. International Herald Tribune, December 12. Glance, N., B.A. Huberman. 1994. The dynamics of social dilemmas. Scientific American March, 76 – 81. Goffee, R. and G. Jones. 1996. What holds the modern company together? Harvard Business Review, November – December, 133 – 148. Goodall, J. 1994. The Chimpanzee, the Living Link Between Man and Beast, The Third Edinburgh Medal Address, 14 - 17. Griffiths, P. 1997. What Emotions Really Are. Chicago: University of Chicago Press. Gupta, A., V. Govindarajan. 2001. Building an effective global business team. Sloan Management Review, Summer, 63 – 71. Hamilton, W. D. 1963. The evolution of social behavior. American Naturalist 97, 354 - 356. Hamilton, W. D. 1964. The genetic evolution of social behavior. Journal of Theoretic Biology 7, 1 – 52. Hamilton, W. D. 1975. Innate social aptitudes of man: an approach from evolutionary genetics. In R. Fax (ed.), Biosocial Anthropology, 133 – 155, New York: Wiley. Hardin, G. 1968. The tragedy of the commons. Science 162, 1243 – 1248. Hayward, M. L. A., and D. C. Hambrick. 1997. Explaining the Premiums Paid for Large Acquisitions: Evidence of CEO Hubris. Administrative Science Quarterly 42, 103 - 127. Heath, C., C. Bell and E. Sternberg (2001). “Emotional Selection in Memes: The Case of Urban Legends.”

40 Journal of Personality and Social Psychology 81(6): 1028-1041. Herrnstein, R. 1970. On the law of effect. Journal of Experimental Analysis of Behavior 13, 242 – 266. Hofstede, G. 2001. Cultures Consequences. Second edition. Thousand Oaks, California: Sage. Horowitz, D. L. 2001. The Deadly Ethnic Riot. Berkeley: University of California Press. Huberman, B. A., C. H. Loch, A. Önçüler. 2003. Status as a valued resource. Forthcoming, Social Psychology Quarterly. Janis, I.L. 1971. Victims of Groupthink. Boston, Mass.: Houghton Mifflin. Kemper, T.D., R. Collins. 1990. Dimensions of microinteraction. Am. J. of Sociology 96 (1), 32 – 68. Kim, J. Y., S. H. Nam. 1998. The concept and dynamics of face: implications for organizational behavior in Asia. Organization Science 9 (4), 522 – 534. Kluckholn, F. and Strodtbeck, F. 1961. Variations in value orientations. Evanston Illinois, Row Peterson. Kohn A. 1993. Why incentive plans cannot work. Harvard Business Review, Sept. – Oct., 54 – 63. Krackhardt, D. 1990. Assessing the political landscape: structure, cognition and power in organizations. Administrative Science Quarterly 35, 342 – 369. Kramer, R. M. 1991. Intergroup relations and organizational dilemmas: the role of categorization processes. Research in Organizational Behavior 13, 191 – 228. Kurzban, R., J. Tooby, L. Cosmides. 2001. Can race be erased? Coalitional computation and social categorization. Proceedings of the National Academy of Sciences 98 (26), December 18, 15387-15392. Lawler, E. J., S. R. Thye, J. Yoon. 2000. Emotion and group cohesion in productive exchange. American Journal of Sociology 106 (3), 616 – 657. LeBlanc, A.N. 2003. Random Family. New York: Scribner. LeDoux, J. 1996. The Emotional Brain. New York: Touchstone. Levy, P. F. 2001. The Nut Island effect: When good teams go wrong. Harvard Bus. Rev. 79(3), 51 – 59. Lin, N. 1990. “Social Resources and Social Mobility: a Structural Theory of Status Attainment.” In Social Mobility and Social Structure, edited by R. L. Breiger. Cambridge University Press: 247 - 271. Lind, E. A., T. R. Tyler. 1988. The Social Psychology of Procedural Justice. New York: Plenum. Loewenstein, G. 1996. Out of control: visceral influences on behavior. Organizational Behavior and Human Decision Processes 65(3), 272 – 292. Loch, C. H., B. A. Huberman, S. T. Stout. 2000. Status Competition and Performance in Work Groups. Journal of Economic Behavior and Organization 43, 35 – 55. Lumsden, C. J., E. O. Wilson. 1981. Genes, Mind and Culture. Cambridge: Harvard University Press. Maccoby, E. E., 1998. The Two Sexes - Growing up Apart, Coming Together, Belknap, Cambridge. Magretta, J. 1998. Governing the family-owned enterprise. Harvard Bus. Review, Jan.-Feb., 112 – 123. Markus, H. R., S. Kitayama. 1991. Culture and the self: implications for cognition, emotion and motivation. Psychological Review, 98, 224 – 253. Massey D.S., 2002. A Brief History of Human Society: The Origin and Role of Emotions in Social Life. American Sociological Review 67, 1-29. Mauss, M. 1950. The Gift. New York, WW Norton. McGuire, M. T., M. J. Raleigh, 1985. Serotonin – behavior interactions in vervet monkeys. Psychopharmacology Bulletin 21, 458-463. Moreland, R., L. Argote, R. Krishnan. 1996. Socially shared cognition at work. In: Nye, J., A. Brower, What’s Social About Social Cognition? Thousand Oaks, CA: Sage. Nelson, R. R., S. G. Winter. 1982. An Evolutionary Theory of Economic Change. Cambridge: Harvard University Press (Belknap). Niamir-Fuller, M. Managing mobility in African rangelands. Chapter 4 in: Meinzen-Dick, R., Knox, A., Place, F., Swallow, B. (eds.) Innovation in Natural Resource Management: the Role of Property Rights and Collective Action in Developing Countries. Johns Hopkins University Press. Nowak, M. A., K. M. Page, and K. Sigmund. 2000. Fairness versus reason in the ultimatum game. Science 289, September 8, 1773 – 1775. Ostrom, E. 1990. Governing the Commons: the Evolution of Institutions for Collective Action. Cambridge: Cambridge University Press. Pawlowski B., Dunbar R.I.M., Lipowicz A. 2000. Tall men have more reproductive success. Nature 403, 156. Pierce, B. D., and R. White. 1999. The evolution of social structure: why biology matters. Academy of Management Review 24 (4), 843 - 853. Plotkin, H. 1993. Darwin Machines and the Nature of Knowledge. Cambridge, MA: Harvard Univ. Press.

41 Podolny, J. M. 1997. A Status-Based Model of Market Competition. American Journal of Sociology 98 (4), 829 - 872. Ridgeway, C, Walker, H. A., 1995. Status Structures. In Cook, K., Fine, G. & House, J. (eds.), Sociological Perspectives on Social Psychology, Allyn and Bacon, 281 – 310. Ridley, M., 2000.Genome. New York: Harper Collins Perennial. Runciman, W. G., 1998. The selectionist paradigm and its implications. Sociology 32, 163 – 188. Schein, E.H. 1985. Organizational Culture and Leadership. San Francisco: Jossey Bass. Scherer, K. R. 1988. Criteria for emotion-antecedent appraisal: a review. In: V. Hamilton, G. H. Bower, N. H. Frijda (Eds.), Cognitive Perspectives on Emotion and Motivation, Dordrecht: Nijhoff, 89 – 126. Scherer, K. R. 1997. Profiles of Emotion-antecedent Appraisal: Testing Theoretical Predictions Across Cultures. Cognition and Emotion 11 (2), 113 – 150. Schneider, S. C., J.-L. Barsoux. 2003. Managing Across Cultures. 2nd edition. London: Prentice Hall. Schwieger, D.M., Sandberg, W.R. and Rechner, P.L. 1989. Experiential effects of dialectical inquiry, devil's advocacy, and consensus approaches to strategic decision making. Academy of Management Journal 32, 745-772. Sherif, M. 1966. In Common Predicament. Boston: Houghton Mifflin. Sigmund, K., E. Fehr, M. A. Nowak. 2002. The Economics of Fair Play. Scientific American, January- February, 81 - 85. Sober, E., and D. S. Wilson. 1998. Unto Others. Cambridge: Harvard University Press. Tajfel, H. 1970. Experiments in Intergroup Discrimination. Scientific American 223, 96 – 102. Tajfel, H. 1982. Social psychology of intergroup relations. Annual Review of Psychology 33, 1 – 39. Tajfel, H., J. C. Turner. 1986. The social identity theory of intergroup behavior. In: S. Worchel and W. Austin (eds.), Psychology of Intergroup Relations, Chicago: Nelson Hall, 7 – 24. Tannen, D. 1991. You Just Don’t Understand: Women and Men in Conversation. Ballantine. Thye, S. R. 2000. A status value theory of power in exchange relations. American Sociological Review 65, 407 – 432. Triandis, H.C. 1972. The analysis of subjective culture. New York: Wiley Interscience. Trivers, R. L. 1971. The evolution of reciprocal altruism, Quarterly Review of Biology 46, 35 – 57. Trivers, R. L. 1985. Social Evolution, Menlo Park: Benjamin/Cumming. Uzzi, B. 1996. The sources and consequences of embeddedness for the economic performance of organizations: the network effect. American Sociological Review 61, 674 – 698. Uzzi, B. 1997. Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly 42, 35 – 67. Waldman, A. 2003. Masters of suicide bombing. New York Times, Jan. 14, A1. Wilson, E. O. 1998. Consilience – the Unity of Knowledge. London: Little, Brown and Company. Weeks, J., C. Galunic. 2003. A cultural theory of the firm: the intraorganizational ecology of memes. Forthcoming, Organization Studies.

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