Institutions and Agent-Based Computational Economics

Masaryk University Faculty of Economics and Administration

Master’s Thesis

Brno 2019 Bc. Jakub Weiner

Masaryk University Faculty of Economics and Administration Field of study: Mathematical and Statistical Methods in Economics

Institutions and Agent-Based Computational Economics

Master’s Thesis

Advisor: Author: Mgr. Josef Menšík, Ph.D. Bc. Jakub Weiner

Brno, 2019

Author: Bc. Jakub Weiner Title of Thesis: Institutions and Agent-Based Computational Economics Department: Department of Economics Supervisor: Mgr. Josef Menšík, Ph.D. Year of Defense: 2019

Annotation

Thesis discusses the concept of spontaneously emergent social institutions within Agent Based Modeling. Specific cases of models are discussed and classified through the whole process of emergence, starting within minds of agents and finishing with backward influence of emergent phenomena on the minds. Special attention is being givento the notion of individualistic and holistic points of view towards the world as well as their relations with Agent Based Models (ABMs) incorporating emergence of social institutions. Key finding of the paper is, that the utilized methods of modeling social reality through ABMs in fact exhibit notable similarities with interrelations between the two points of view.

Keywords

Methodology of Economics, Agent-Based Modeling, Social Ontology, Emergence, Indi- vidualism and Holism, Micro-Macro Relationship, Social Institutions, Mind

Declaration

Hereby I declare that this paper is my original authorial work, which I have worked out on my own. All sources, references, and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source.

Brno, 3. January 2019 Author’s signature

Acknowledgements

As it is symptomatic for various types of literature, there is a group of people helping the author to shape the thoughts or just contributing to him not going mad. This case is not an exception.

I would like to thank Dr. Josef Menšík for having enough courage, levity and irony to lead this project independently of the current scope of the sources and time constraints and for strengthening my critical thinking, which is here most expressed through learning to weight every word as the assumption of knowing something is ultimately doubtful.

I would like to thank my parents, Věra and Jiří, for their everlasting moral and material support, including the one towards the three long-term trips abroad, which extensively widened my knowledge and at the same time ensured that I will never want to live in Vienna again. To my sister Joziš for not always requested but always valuable comments on both linguistic and contextual aspects of the text as well as for evil roads and movable wagons.

To my (especially first year) study fellows, Honza Přikryl and Marek Soukup, with whom we explored much beyond the hidden beauty of Social Sciences. To JJJMMMMDDAKPZT for many benefits including tolerance for not delivered lunches and scalped heads. To Christian Baden & Claire Benn of Hebrew University who showed me the worlds of networks and technological ethics. To LATEXfor stimulating my scripting desires despite this being a theoretical study. To Tony Savarimuthu for providing me with scripts of his models and a couple of literature suggestions, which is what also makes me thankful to Neal Tsur. To our dog Indie for being my spare partner on more than 100km of walks during the last week of writing this paper. To my flatmates Viktorie and Silvie to sending me to sleep when it was too late to come up with additional asset for the research. To my superiors and a variety of friends for supporting my off-track mode...

Contents

1 Introduction 1 1.1 The mainstream paradigm and contemporary research questions ...... 2 1.2 OIE, Transformational models and ABM ...... 3 1.3 Aim, scope and structure of the thesis ...... 5

2 Introducing ABMs with emergent Social Institutions 7 2.1 Technical properties of a Agent-Based Model ...... 9 2.2 ABMs with emerging Social Institutions ...... 15 2.3 Overview ...... 23

3 Classifying Mind within emergent Social Institutions 25 3.1 Towards framing of Mind within ABM context ...... 26 3.2 Fixed mean agents ...... 32 3.3 Dynamic mind agents ...... 36 3.4 Overview ...... 40

4 Classifying emergent Social Institutions 43 4.1 Emergence ...... 44 4.2 Macro-level phenomena ...... 47 4.3 Backward influence of Institutions towards Minds ...... 53 4.4 Overview ...... 59

5 Dichotomy of Mind and Institutions in ABM 61 5.1 TMSA and ABMs with emergence ...... 61 5.2 An overview of the classification ...... 64 5.3 Overview ...... 71

6 Assesing relevance of ABMs with emergent Social Institutions 73

7 Conclusion 79

Index 81

List of Tables 89

List of Figures 91

1 Introduction

“The art of modelling is to simplify as much as possible, but not to oversimplify to the point where the interesting characteristics of the phenomenon are lost.”

– John H. Miller and Scott E. Page, 2004, 9

The present volume is a part of discussions on the ontological background of the social reality and viable methodical approaches towards researching it. This is being done mostly by questioning the mainstream Neoclassical Economical theory (based in the late 19th century marginalism, NCE henceforward) from the position of Old/Traditional Institutional Economics (OIE henceforward). Whereas the former concept believes in the world ruled by self-centered agents (as well as the famous Invisible hand) and self- propagating equilibrias, the latter sees the major explanatory power in social institutions.

Those can be understood as a general context of our behavior and existence, shaped by a.o. the environment(s) which we occupy. Hence, the ways of how people reason (and then act) in certain situations can be dramatically different given where they come from and what they have gone through. For instance, people from certain countries might feel guilty for throwing food away for various reasons, which would not concern people from other places around the world. Caring for the environmental sustainability or nutrition for the poor is usually not enforced by legal authorities. Nevertheless, some people still do it even though axioms behind the principle of maximalization utilized within mainstream Economics would advice them not to.

A different case are phenomena, which also constrain one’s behavior, but arebacked by punishment. To such phenomena, which can be possibly termed formal institutions, typically belong laws. A particular difference between formal and informal institutions is in the possibility of measurement. While the obediance to the formal institutions can be measured by objective indicators of compliance with their deontic nature (say evidence of crime acts), for informal institutions such as behaving nicely towards older people, objective measurement is much more difficult. Perhaps this difference is among the causes of why some researchers, such as Carl Menger (according to Furubotn and Richter 2005) understand only the informal institutions as truly scientifically interesting1.

Measurement of the impact is not the only conceptual issue related to Social Institutions. Among others it is an issue related to attributing them to particular people within the society. Social institution defining that people in Nordic countries pay respect to the nature

1. Menger was interested in spontaneous emergence of trading unit - money. Hodgson and Knudsen (2004) denote modeling money (consisting at his time of the article by Marimon et al. 1989) in ABM as a rather complicated matter, for in the featured model an emergence of a single monetary unit is not always the case. The current approach of Gangotena (2016) is facing an issue with the need to pre-define liquidity of items within the system, which de-facto prescribes what the future institution is. Both models are discussed within the paper.

1 1. Introduction could serve as a matter for scientific verification. However, the compliance with such social institution is quite hard to identify and therefore measure. This issue can come up once the intention to preserve the nature is not reported by any of the respondents but still there are no cans in the forest. To whom should then such effects be attributed if the behavior is not motivated by compliance with formal rules?

1.1 The mainstream paradigm and contemporary research questions

The current level of technological advance brings important changes to general functionings of the societies, making the informal norms much stronger than before. This can be seen in some general trends of changes in the distrubution of power in decision-making over the past century.

The Hobbesian idea of a powerful state ruler has in the Western world almost diminished over the years. The current role of state in specific processes is then deduced from a trust given to it by single agents rather than from enforced obediance to the system. The states have all along experienced decreasing power over information as the media have at first been privatized by various actors and then made almost equal with various sources on the internet. There is also the issue of borders, which are de iuere (Schengen) or de facto (any other countries without significant constraints on transit of people and goods) disappering towards an open world. The second of the traditional state monopolies, namely Monetary policy is being simultaneously challenged by cryptocurrencies.

All of the mentioned results in a world, inhabitants of which aggregate into small interest groups with actual powers to affect the discourse. Such distribution of power naturally acknowledges greater influence to the informal institutions, which can account for alot of what happens within the parts of this decentralized system.

If the research in Social sciences shall ever keep pace with such trends in explanatory power, it is reasonable to expect its methods to change accordingly. Taking this type of challenges is eigen to the approach of Complexity science. „Complexity refers to objects which are predictable only in the short run and that can be faced only with heuristic and not optimizing strategies,“ defines the setting Biggiero (2001, 3). Tools of conceptualizing such phenomena are among other areas of focus (Mesjasz, 2010). Complexity is an antonyme to Simplicity, associated by Warren Weaver in „Science and Complexity“ (1948)2 with pre-1900 physics, isolating phenomena into two-variables problems. Complexity can stand for both the state of the world and the name of science, which aims to research it. Complexity science aims to explain complexity of the world by various computational techniques, such as cellular automata, genetic algorithms and agent-based models (Sullivan and Haklay, 2000).

2. Noted by Mesjasz (2010) as one of the pioneering attempts to study complex entities along with Ross Ashby’s „Introduction to Cybernetics“ (1961) and Herbert Simon’s „The Architecture of Complexity“ (1962). Durlauf (2003) however assigns the inception of economic complexity reseach to historians of Economics studying environments undergoing path-dependence. 2 1. Introduction

Systems of (often heterodox) interacting agents are said to emerge as Distributed Artificial Intelligence, a field within AI, „concerned with agents as computational entities which can interact with each other to solve various kinds of distributed problems“ (Sullivan and Haklay, 2000, 10). Today, the approach is denoted mostly as Agent-Based Models (ABM), but also as Agent-Based Computational Economics (ACE), Artificial adaptive agents (AAA), Complex Adaptive Systems (CAS) or Multi-Agent Systems models3. ACE is also pictured as a superset of economics-based ABMs (Dessalles et al., 2008)4.

Simulational approaches towards researching multilateral interactions in complex systems are therefore results of both the technological shift as well as possible methods of analyzing it. It is, like any other logical system, able to easily include deontically defined borderlines for behavior of agents within them, here in the form offormal institutions. What remains unclear is how type of system can deal with the nature of informal institutions emerging out of agents’ behavior.

1.2 OIE, Transformational models and ABM

Possibilities of decomposing social reality to the level of individuals are being discussed within Individualism-Holism debate. Proponents of OIE are standing on the side of Holism - position incorporating mostly disbelief towards such possibility. According to holism the social reality is constructed out of complementarity between individuals and social institutions and reducing it to single individuals and their relationships is not possible.

The position of the Old Institutional Economists towards formalising their ideas is rather reserved as can be seen in some works capturing the phenomena (Lawson, 2003; Gräbner, 2014; Weiner, 2015; Soukup, 2017). In contrast, NCE perceives society as a function of the involved units, not allowing for any properties remaining in the void between them. Such world would be occupied by agents (people) with characteristics such as perfect rationality and possesion of perfect information, making their behavior predictable not unlike computers but far from real-world inhabitants.

Lawson (2003) expresses dissapointment with the way how Economics incorporates mathematical methods into its thinking, which includes reducing social phenomena to purely deductivistic rules. His own alternative to this approach5 uses mainly textual models with focus on discussion of the reasons behind some observed phenomena actually happening.

3. The word model is in the case of the studied simulations inseparable from the rest of the string, since Multi-Agent System alone can stand for a real system with given rules and reciprocally communicating agents (Woolridge in Savarimuthu 2011), such as autonomous vehicles (Stone and Veloso, 2000). 4. Which can be seen as a subset of even wider group - complexity and interactive microeconomics, portrayed in special microeconomical approaches (Elsner, 2012; Elsner et al., 2015). 5. As well as other researchers within Cambridge Social Ontology and Epistemology Group.

3 1. Introduction

Figure 1.1: Bhaskar’s TMSA as displayed in Dessler (1989, 453). In the case of emerging institutions, the process starts at the level of unit-unit (agent-agent) interaction. Upward- oriented arrow 3 stands for the process of emergence. If the institution has already been emerged (with which the agent intervenes), then this arrow stands for changes being implemented within the institution. As an example of continuous intervention to emerged institution can be seen languages, which are being changed by the way how agents use them (such as by widening them by slang). Arrow 1 shows the backward influence of institutions towards the agents. The issues concerning institutions preexisting the moment of internalization to the mind as well as the existence of arrow 2 (ability of agents to critically review what they are internalizing) are subjects to ongoing debates. Within the linguistic example the presented order would stand for a creation of a new language.

An example of an approach towards specifically institutions-agents interrelation are Transformational Models of Social Activity (TMSA henceforward, see Figure 1.1).

Transformational models include some general prescription of interrelations between agents and institutions. Institutions are being understood as features, which emerge out of the minds of agents and after some time may become constraints for their actions. This can take a form of never-ending process, revolving around mutual interactions between agents and institutions.

The class of TMSA models originates from critical realism of Roy Bhaskar (1998), sum- marized within The Possibility of Naturalism (originally published in 1978). Similar structuration can however be found already within Thomas Berger & Peter Luckmann’s (?) Social construction of reality which was originally published twelve years earlier. Further contributors to the debate include Tony Lawson (2003), his brother Clive Lawson (1994), Nuno Martins (2007, 2011), Rom Harré (2009) and David Dessler (1989). Structuration of the debate have been presented in a previous paper of mine (2015)6.

Most of the contributions within the debate are describing the dichotomy between agents and institutions from a rather holistic position without imposing much influential power

6. Which is in the same time general source of informatin from within the debate on Transformational Models unless stated otherwise.

4 1. Introduction to the agents. Even in the case when this would be a correct description of the reality, it is reasonable to believe that every institution must have emerged at some point. Within the debate, Harré (2009) made an effort towards integrating more of agency.

Agent-Based Models, on the other hand, start with agency. The question is, whether they also feature a structure, identificable with thoughs of old institutional economics. An interesting thought towards putting together OIE and ABM has been raised by Holland and Miller (1991), who divide general modeling techniques in Social Sciences into Mathematical models (e.g. NCE: consistent, but in the same time rigid) and Linguistic descriptions (e.g. OIE: flexible, but sometimes logically inconsistent). ABM is saidas promising heuristic for combining strengths of the both, for they combine language (albeit programming) with mathematical components.

1.3 Aim, scope and structure of the thesis

A major feature of ABM models, according to some (Holland and Miller, 1991; Tesfatsion, 2003, 2006), lies in the ability to produce emergent behavior, leading to the development of the system without further interventions of the modeler7. Many other novelties are available, such as treating any object within the system individually from the initial run of the model (special behavior, position or any other attributes) through all other timestamps. This allows the researcher to study not only the change between the initial and the final state of the system, but to apply arbitrary views. An example of those can be gradual reactions of various agents within a city to changes in the infrastructure.

The fact that the world within ABM model carries a separate set of attributes (such as its look, rules, dynamics etc.) with which the agents can interact, opens a space for contemplation on whether such system can be used for depicting issues connected to the Individualism & Holism debate. Moreover, there is a question of whether ABM models can move us forward in the research of Social Institutions and their relations with actions of agents?

To summarize, the aim of the thesis is to classify the different ways institutions are modeled using the agent-based computational economics approach, while focusing on the Mind-Institution dichotomy. Moreover, mutual relations of the different approaches will be studied, and their relevance will be assessed.

7. Tesfatsion denotes this property as dynamic completeness. NetLogo, a computational laboratory (Tes- fatsion, 2003) /CompuTerrarium (Epstein and Axtell, 1996) for agent-based-modeling, on the other hand allows the users (given underlying code) to intervene with the system during the run. Althought such approach is not in accord with spontaneous emergence per se, it could allow for displaying impact of formal institutions in the role of shocks to spontaneously-emerging system. Popular environments for ABM development include scripting environments such as R and Python or programming languages such as C or Java. Overall, ABMs seem not to be significantly dependent on programming environment, so the whole approach can be seen as rather environment-agnostic.

5 1. Introduction

The thesis offers a discussion on the ways of how the institutions are emerging outof the minds and behavior of virtual agents and how they affect behavior of the agents since their emergence. Chapter 2 introduces the reader to the topic of ABMs and shows various examples of models incorporating emergent phenomena.

Chapter 3.1 conceptualizes minds of agents within the selected models. Chapter 4 focuses on the processes of emergence and backward influence of institutions towards the minds. Chapter 5 wraps the whole classification, compares it with alternative approaches and discusses parallels with the concept of Transformational models.

Chapter 6 features author’s opinion on relevance of models with respect to methodical debate and their teleology. Chapter 7 concludes.

6 2 Introducing ABMs with emergent Social Institutions

„Neoclassical economics describes the way the world looks once the dust has settled, we are interested in how the dust goes about settling“

– Peyton Young, 1998, 4

Agent-Based Models (ABMs) are a virtual environment, allowing for a research of complex systems. Its approach of simulation is by Axelrod (1997, 3) considered to be a third way of doing science, widening the former dichotomy between induction and deduction1. The difference coming with this approach lays especially in the point of focus. While induction and deduction are concerned mostly with the explanation of the final state, the purpose of simulation can be solely to study the process of its emergence. Also the outcome is not limited to establishing the theory or (dis)prooving it, but can consists of information about happenings inside the system at every point of time (Gräbner, 2014).

Teleologically, ABMs can serve as for instance a heuristic for finding a solution to a complex problem by letting the virtual agents face it (Tuyls and Weiss, 2012) or to undertake experiments which would be difficult to organize in the real world (Hassan et al., 2010).

There are currently two general approaches to inclusion of Social Institutions within the models. First, institutions are used as constraints or various other types of pre-defined elements affecting the undertakings of agents within the model. This branch includes variety of models tackling topics other than social institution/norm emergence, where institutions serve only as one of the features to make the system reality-alike. This is being done for instance through designs of complex normative architectures. Second, institutions can be a result of emergent processes, growing spontaneously out of (sometimes selfish) undertakings of agents within the model. The latter type of models is considered as explanandum for this thesis and introduced in this chapter.

Two modeling traditions

As recognizes Neumann (2008), two approaches to modeling emergent institutions can be seen within ABMs, where the first one emerges from Game Theory (GT henceforward) and the second from Cognitive Artificial Intelligence (AI henceforward). The main point of his differentiation is, that the former „typically refer to game theoretic literature for the characterisation of the interaction structures“ while the latter „typically contain references to conceptual articles relating to agent architectures“ (par. 4.3.). Af- ter comparing examples of the two approaches, Neumann claims, that the models are

1. The parallel with a third way of doing science is also used by Gilbert and Terna (2000), however at that time as an addition to argumentation and formalisation.

7 2. Introducing ABMs with emergent Social Institutions

complementary - while Axelrod’s initial model of Norms game (GT) does a good job in explaining how the agents are transformed to follow the institutions and spread such behavior further, Castelfranchi’s and Conte’s adaptation of Sugarscape (AI)2 can account for how the norms affect the social level. Consequently, GT models are argued to be a dynamic approach, where the norm is only an aggregation of individual interactions, while the AI approach offers cognitive functions, but with rather static objects (Neumann, 2008; Beheshti, 2015). By noting this he aims to say, that the teleological differences between the approaches can be bridged by models combining strengths of the two. He however fails to find a reasonable example, that would represent sucha bridge.

While Neumann was mostly concerned with sources which authors of the models use, Beheshti (2015) draws a similar distinction with a focus on properties of the systems:

1. Cognition-based Approaches: „These methods provide high-fidelity models of the cognitive aspects of normative behavior, while focusing on the internal part of the norm lifecycle (Elsenbroich and Gilbert 2014). In comparison with the interaction-based models (...), this category relies less on the use of reward and punishment to motivate norm adoption, moving beyond the carrot and stick approach“ (p.34)

2. Interaction-based Approaches: „Interaction-based approaches create agent models that can detect norms from what they observe in the environment and their interactions with other agents. Often the agents are equipped with the ability to learn from experience, and interactions among agents are modeled as repeated games with payoff matrices. The simplest interaction approach is to imitate other agents in the environment — while in Rome, do as the Romans do3 “ (p.35).

Learning

A very comprehensive debate is ongoing within the sphere of Multi-Agent Learning (MAL henceforward). This contains not only a dispute on whether MAL is a question or an answer to a question (Shoham et al., 2007; Sandholm, 2007; Stone, 2007; Hernandez- Leal et al., 2018) but also a wide classification of learning methods. The classifications of learning approaches (Shoham et al., 2007; Tuyls and Weiss, 2012; Hernandez-Leal et al., 2018) also work with the already presented tradition-dependence, standing as an explanation for to which extent the agents learn from interaction (GT) and cognition (AI) respectively. As pointed out Shoham et al. (2007), these can be also denoted as model-based and model free respectively, where the former focuses on building a model of opponent’s strategy while the other uses (a.o.) reinforcement learning to learn more about agent’s undertakings. A particularly popular approach for reinforcement learning

2. Both models are presented within section 2.2. 3. This saying is being utilized also by Epstein (2001) as a part of the motivation behind accomodative behavior within Rules of the road model.

8 2. Introducing ABMs with emergent Social Institutions is Q-learning (together with its modifications), in which the agent runs a series of trial- and-error action to find out their consequences and shape her own strategy (Watkins and Dayan, 1992; Tuyls and Weiss, 2012; Gu et al., 2016).

The other ways of sorting the methods (Shoham et al., 2007; Tuyls and Weiss, 2012) relate to agent-goal-group relation (competition/cooperation) and responsibilities towards learning (learning in isolation/filling common learning base asa swarm).

One of the simplest techniques for interaction-based learning are Genetic Algorithms, which were originated by John Holland (1995).

These offer a heuristic for coping with evolution in a simple computational environment. Genes, which equal strategies, are represented by strings of binary values. Holland suggests that evolution is designed by a three step process. First the fitness of agents is calculated to determine their ability to mate4. Then they are matched with other agents together with recombination5 of genes and finally, the new generation replaces randomly chosen agents within the former population. In some of the models agents are allowed to reproduce asexually and hence produce identical offsprings with no partner needed.

The heuristic offers a variety of creative uses, such as the one in the model ofMacyand Skvoretz (1998), where the agents can change a part of the genome during interactions.

The approach of genetic selection has initially served as an effective way of how to model evolution regardless the limited computational equipment available in 1980s and 1990s. Some of the examples of its use will be shown throughout the paper, with specific impact on interpretation of cognitive functions of the agents shown in section 3.2. Series of sensitivity tests have shown that Genetic Algorithms might resemble the real evolutionary aspects of the society to even greater degree than their original proposers thought. The findings and their interpretation are presented in section 4.1.

2.1 Technical properties of a Agent-Based Model

The point of view which ABM holds is inherently different than the one of NCE, partly due to the technological advance, providing today’s researchers with unprecedental growth of computational power6 as well as the available data.

Some of the authors within ABM debate are in the reffered articles trying to uncover the novelties coming with Agent-Based approach (Miller and Page, 2007, 79) or heterodox

4. In relation to a scaling function, featured for example as Equation 3.1 within this volume. 5. The process of recombination creates a new genome as a combination of parental strings (denoted as crossover) with a possibility of random mutations (changes to single bits). 6. Which, in the same time, doesn’t mean that all ABM models are computationally intensive. Some examples of those which are not are given throughout the paper.

9 2. Introducing ABMs with emergent Social Institutions

Table 2.1: Santa Fe institute’s fellow Brian Arthur thinking about a new, complex, attitude on economics (Waldrop, 1993, 37-38).

Old Economics New Economics Decreasing returns Much use of increasing returns Based on 19th-century physics Based on biology (structure, pattern, (equilibrium, stability, self-organization, life cycle) deterministic dynamics) People identical Focus on individual life; people separate and different If only there were no externalities Externalities and differences become driving and all had equal abilities, we’d force. No Nirvana. System constantly unfolding reach Nirvana Elements are quantities and prices Elements are patterns and possibilities No real dynamics in the sense Economy is constantly on the edge of time. It that everything is at equilibrium rushes forward, structures constantly coalescing, decaying, changing. Sees subject as structurally simple Sees subjects as inherently complex Economics as soft physics Economics as high-complexity science

approach to Economics in general (Waldrop7, 1993, 37-38). According to them, the overall motivation behind the approach is closer to OIE rather than to NCE and to biology rather than to physics. The search for equilibrium is substitued by studying processes of constant change. Elements as increasing returns or externalities are no more taken as unexisting or unfavourable, but can serve as driving forces of the whole system. Agents differ in headcount (∞ of agents is not a case for ABM), properties, roles and positionsDifferentations serving as a matter for this paragraph are depicted in Table 2.1 and Table 2.2.

The rationality of agents within ABMs is usually not the same one as in the case of game theory. Agents are therefore unperfectly informed and unable of objective choice assesment, but in many senses bounded8. Boundedness of agents within ABM usually starts with knowledge, where the limit on data access (given e.g. by environment used) makes them constrained in optimizing through all possible wisdom or solutions. S

7. Thoughts within reffered table are actually coming from Brian Arthur with Mitchell Waldrop serving only as their interpreter. 8. The idea of bounded rationality comes from Herbert Simon (1979, 2008). The minds of his agents resemble a computer program, optimizing the ultimate goals by breaking them into smaller ones to make them tracktable. For bounded agents is no more possible (and necessary) to find the perfect solution to given problem, and hence they rather get by with a good enough one. The theory has a great importance towards the paradigm, for instance Brian Arthur’s paper with Santa Fe model (1994) carries it in its name (Inductive Reasoning and Bounded Rationality).

10 2. Introducing ABMs with emergent Social Institutions

Table 2.2: Comparison of approaches by Miller and Page (2007, 79).

Traditional Tools Agent-Based Objects Precise Flexible Little process Process oriented Timeless Timely Optimizing Adaptive Static Dynamic 1, 2, or ∞ agents 1, 2..., N agents Vacuous Spacey/networked Homogenous Heterogenous

Agents, their relationships and the environment in which they reside have already been used as refferal objects. Following some other authors (Holland and Miller, 1991; Macal and North, 2010) these will now serve as units for model decomposition. An alternative way for thinking about composition of the model adapted from works of Macy and Willer (2002) is shown in Figure 2.1.

Agents

When thinking about defining the agent it might be interesting to lookat The Evolution of Cooperation by Robert Axelrod & William D. Hamilton (1981)9. The book features results of an iterated Prisoner’s Dilema strategies tournament. There the agents written by variety of people ranging from professional scientists to computer enthusiasists com- peted throughout hundreds of moves for the highest cummulative payoff10. Althought every agent is usually not programmed separately by contestors, it can still be said that agents are (homogenous or heterogenous) units which to some extent independently represent some agenda.

9. Axelrod’s and Hamilton’s book is together with Holland’s genetic algorithms (1995) and Simon’s bounded rationality (1979) highly influential towards ABM research (considered on the basis oflater publications’ references). 10. The winning agent was playing Tit for tat, an easy strategy of cooperating in the first move and repeat- ing the counterparty’s last move ever since then. The authors have seen it’s pros merely in the quickness of reaction and wilingness to restore the cooperation even with a long-time defecting partner. This strategy has also worked well in simulations of natural-selection. Its limits lay in the built-in expectation of endless relationship between the agents which made authors believe in it being best applicable to high-frequency relationships. A few real-world applications of the findings are recognized. First, a variety of business agreements are being held on purely verbal basis, as the mutual trust grows from expectations of long-term relationship. Second, Tit for tat itselves could be a way how to manage the politics of deterrence (for example the one present between the USA and the SSSR at the time of the research). It should however be visible, that the tit being played by one side is a reaction to tat of the other and not a mutual escalation of the situation. This is said to be achievable for example by defecting moderately than the former player, which should progressively decrease the scope of defection until status quo after a few moves.

11 2. Introducing ABMs with emergent Social Institutions

Demonstration Evolution

Experiments Learning

Model is used Adaptation for based on

Model designs

Interaction Ties Global (fully connected Forced graph) interaction

Local Elective

Figure 2.1: First, second and third level of conceptualization by Macy and Willer (2002).

„Agents can range from active data-gathering decision-makers with sophisticated learning capabilities to passive world features with no cognitive functioning. Moreover, agents can be composed of other agents, thus permitting hirearchical constructions. For example, a firm might be composed of workers and managers,“ describes the scope Tesfatsion (2006, 835-836) and adds examples of „individuals (e.g., consumers, workers), social groupings (e.g., families, firms, government agencies), institutions (e.g., markets, regu- latory systems), biological entities (e.g., crops, livestock, forests) and physical entitites (e.g., infrastructure, weather, and geographical regions)“.

As emphasize Conte and Castelfranchi (1995, both 214) in reference to the term agent sounding „lofty“ when applied to C++ object on 2D grid, one can understand agents as some kind of routine processing unit; „Whenever an agent is said to do something, this is equivalent to saying that a given routine has been applied and that a given object (the agent) is to be considered as its performer.“

Setting of the model can determine in which ways and to which extent the agents can change (develop) their own properties once they populate the model. The properties generally contain parameters, which together with strategy serve as an input to decision- making. The abilities of agents are in some models essential for emergence, as they cover the mental abilities to (usually in terms of bounded rationality) memorize and learn11.

11. The exact connection between minds and emergence is covered within the classification in chapter 3.

12 2. Introducing ABMs with emergent Social Institutions

Environment

Environment is the virtual world occupied by the agents. As can be seen on Figure 2.2, there are various possible topologies. Older models, which are not directly questioning the spatial position of the agents (e.g. implementation of original GT games) are usually utilizing the so-called Soup topology, which is technically a fully-connected network with random interactions. Behavior of agents in Cellular automatas (Checkboard and Torus) is co-determined by their neighbors (Macy and Willer, 2002). The difference between the two types of Cellular automata is that while Checkboard is constrained by the borders, Torus symbolizes a wrapped world. Interactions with neighbors on spatial grids are made either within von Neumann’s (N,E,S,W) or Moore’s (all 8 directions) neighborhoods.

The more-to-reality approach uses network in the form of a map in order to consider variety of map-related objects (landmarks, usual paths, district), which is nicely conceptualized by Filomena and Verstegen (2018). Topology of a map can be useful for applying the models to specific places of the world.

The mentioned bounding does not concern only the agent’s reasoning capacities, but also the environments. Novel approaches include placing the agents in bounded world, giving them bounded sight or bounding their decisions externally (as will be shown throughout the paper). A recently popular exercises with ABMs with institutions include manipulations with the topologies for checking the effect of locality on emergence. The first example of such exercise is according to Mahmoud et al. (2014) the model ofKittock (1994). His finding is, that agents in tightly constrained setting are much more likelyto create local clusters of behavioral code than those who can freely wander through the space (as they reach a coalition of bit value once given enough time).

Kittock’s approach, which concerns agents of fixed positions, is close to the one of Epstein (2001), who bounds agents in vision. The latter author, in the approach of looking for thougthless acception of an institution gives agents variable sight and looks for the dependence between the sight and the emergence of implicit acceptance of the institution.

Relationships

Given the topography of the world, agents are allowed to interact with each other. Gen- erally, experience obtained during relationships (say value of edges) could play important role in norm emergence, as the agents often recognize with whom they interact and care about it.

Utilization of dynamic networks (Savarimuthu and Cranefield, 2009) is an example of overlapping spheres of environment and relationships. The dynamics allow for the default topology to change throughout the runs of the model and so to demonstrate the

13 2. Introducing ABMs with emergent Social Institutions

(b) Fully connected network (abbrevi- (a) Network (this one is a random ated for the case with random interac- Erdos-Renyi with edge probability p = tions by Macal and North (2010) as Soup 0.3) model).

(e) Euclidian space (Gavin, 2018, 321)

Figure 2.2: Some of the environments used within ABMs with emerging social institutions. The scheme is inspired by the one of Macal and North (2010) and updated to the current state of art. More cases, such as usage of Voronoi’s diagram (Figure 4.1) or combination of multiple environments will be shown troughout the thesis.

14 2. Introducing ABMs with emergent Social Institutions fact, that relationship among real-life agents can be (dis)appearing over the course of time.

2.2 ABMs with emerging Social Institutions

The previous pages have shown some plausible approaches for recognizing groups within the modeling discourse.

Apart from the featured distinction by Neumann (2008) there are some attempts of other authors to sort the approaches in relation to discourses which they should be representing. An example of such perspective is the one presented by Andrighetto et al. (2013), who identifies the literature within the field as originating within two spheres, eventually resulting by the models resembling either the mind processes of engineering or those of sociology. Another popular theme is to compare the ways of approaching the norms from the computational point of view and a different paradigm such as Legal Science (Conte et al., 1999) or Sociology (Saam and Harrer, 1999). Publications within Social sciences generally do not spend much time on contemplating the technical properties of the approaches, but instead think more about various types of institutions and other topics related to the social reality.

Sullivan and Haklay (2000) identify four meta-topics of ACE’s interest at their time. The first line of their differentiation was the distinction between economic and sociological model, where the first type (e.g. El Farol problem, which will be shown in this section) focuses on decision-making under bounded rationality, while the second on behavior of masses (e.g. Sugarscape or Racial Segregation, which will also be featured). The other two classes are then focusing on the unit of analysis, as they distinguish between human movement and flocking of animals.

The concrete cases of ABMs with emergence revolve around a group of topics ranging from cooperation through decisions biased by external influences to emergence of money. These topics are used to sort the models within the next parts of this section. The selection of the models (which will be also discussed in more detail throughout the thesis) has been made on the basis of stratified randomisation. This approach aims to incorporate models of various types with respect to time of their publication, origin within the two traditions, theme and level of complexity.

Surveys of institution-incorporating contributions, some out of which have been used for finding the models have recently been made by various authors (Vriend, 2006; Savarimuthu et al., 2007; Neumann, 2008; Savarimuthu and Cranefield, 2009, 2011; Hollander and Wu, 2011a,b; Elsner, 2012; Andrighetto et al., 2013; Mahmoud et al., 2014; Gräbner, 2014; Gräbner and Kapeller, 2015; Beheshti, 2015).

15 2. Introducing ABMs with emergent Social Institutions

Figure 2.3: Metanorms game (Axelrod, 1997, 53)

GT-based evolution of cooperation

The first group of models is based in the tradition of Game-Theory. As a consequence of that, models are mostly focusing on random interactions within fully connected environment (Soup) similar to those featured in GT.

Axelrod (1997) builds on findings from the above mentioned contest by designing a virtual environment for optimal strategy determination by selecting the most succesfull strategies in iterated Prisonner’s Dilemma with Genetic Algorithms12. Within the same contribution Axelrod fully exploits the mutual interaction between agents by introducing Norms and Metanorms games.

In sequential Norms game the agents decide whether to cooperate or defect in Prisonner’s Dilemma, while the others reason about (not) punishing them for a possible defection. Strategy consists of two dimensions: boldness (Bi) (preference to defect) and vengefulness (Vi) (preference to punish the others). Each of the actions has a defined payoff, hence the agents get 3 for a Defection and give 2 for punishing other agent. Based on actions of the others they can also loose 9 (being punished for spotted defection) or 1 as a result of someone else’s defection. In the simulation, Axelrod randomly generates the values for vengefulness and boldness in the range from 0/7 to 7/7 (U) each. Throughout the game agents have four opportunities to defect. For each of them they get a random chance of being seen (S ∼ U(0, 1)) which they compare with boldness and in the case of S < Bi defect. S is in the same time a proportion of agents seeing the defection and possibly punishing the villain.

12. Agents raise offsprings, which headcount is based on a function of their total payoffs. This mechanism (utilising John Holland’s genetic algorithms), works for the sake of succesfull strategies’ prevailence. Utilized scaling function will be shown in section 3.2. Axelrod states, that the population size remains 20, however doesn’t explain by which means, the possible options are later evaluated by Mahmoud et al. (2010).

16 2. Introducing ABMs with emergent Social Institutions

Metanorms game13 is an extension of the former, where non-vengefulness is punished14. Axelrod notes, that metanorms are usual during mobs or in communistic regimes15, where the people focus on denouncing those who do not act in accord to dominant code of conduct. Consequently, the model is enriched by another part of the sequence, in which the agents are able to see whether a defeating player was punished or not and possibly punish the one who didn’t punish. The parameter V (vengefulness) as well as the gains & costs resulting from (non) punishing someone stay the same as in the case of punishing the defectors themselves.

Mahmoud et al. (2017) were testing this possibility on networks with low connectivity, which consequently leads to a total evaporation of vengefulness. This was caused by the fact that the outlying nodes on the network profit from defecting and the hubs can loose much of their score by punishing them. They have found that no matter the size of the population, norm establishment gets more likely when there are more neighborhoods (as there are overlaps in between the neigborhoods and the agents therefore face possible punishment from more sides).

Tag-based cooperation (Riolo et al., 2001) is a constellation of non-reciprocated cooperation. Each agent there posseses a tag (float) and a tolerance treshold. Agents donate to those agents, whose tag is sufficiently similar to their16 own . Consequentally, agents soon fall into each other’s treshold but the less tolerant agents take over the population after some generations (they do not donate to others but only benefit from obtaining donations). The case of possibly reciprocated cooperation is Trust game (Bicchieri et al., 2004), a one-sided PD in which agents can decide whether to donate to others, who then decide whether to reciprocate. The scenarios with actual Prisonner Dilemmas have been incorporated within ABMs by Lindgren (1997) and Macy and Skvoretz (1998).

In Social Evolution model (Eshel et al., 2000) are agents divided into altruists and egoists. They are informed about the average payoff of being either of the two types within their neighborhood17. The agents switch between strategies either with certainty or with some level of confidence (positive probability). Mutations (random changes tothe norm) are sometimes also applied (with a small probability). Noise does not eliminate altruism. Agents operate within learning neighborhood (to get stats from)18 and action neighborhood (to interact in).

13. Presented together with Norms game in Figure 2.3. 14. Euphemistically said, „The key idea underlying Axelrod’s metanorm mechanism is that some further encouragement for enforcing a norm is needed“ (Mahmoud et al., 2012, 3). 15. I’d say that in any totalitarian regime or a structure, where a certain code of conduct is being enforced. Associating this type of misbehavior specifically with communistic societies might have something to do with peaking Cold War at the time when the model was originally introduced. This type of context can also be influential towards overall focus of the game, in some ways resembling Cold War dilemmas nicely pictured in for instance Stanley Kubrick’s movie - Dr.Strangelove. 16. The difference between own tag and the one of the counterparty is equal less the treshold. 17. Althought not matched with individuals - this process can be abbreviated as gossips. 18. Results get problematic when individual adapts strategy from far away but acts only locally, which is a nice parallel to the real life.

17 2. Introducing ABMs with emergent Social Institutions

While Game theory presumes agents to act in accordance with unconstrained maximalization, some of its models disregard this axiom while experimentally used. The Ultimatum Game is quickly catching up with the Prisoner’s Dilemma as a prime showpiece of apparently irrational behavior, opens their article Nowak et al. (2000, 1). There a two players are assigned to distribute a given amount of money between self and the other player. The counterparty can then either accept the proposal (leading to its execution) or reject it, causing no division to occur19. The agents in Ultimatum game can be identified as promoting the social norm of fair division. While the promoting party tries which offers can be carried through, the counterparty provides a direct feedback (< accept; reject >). An example are the models of Savarimuthu et al. (2007, 2009)

Segregation

Thomas Schelling’s research (1971) is about reasons for Segregation and its consequences20. The model concers segregation/separation/sorting, resulting from deliberately discrimi- native behavior based on visible parametres (race, social status,...). His model works with two different groups of people (starting by a line of text with binary symbols all the way to a 2D grid), who care about what kind of people live in their immediate neighborhood and so move themselves to the nearest positions suiting their (homogenous) preferences21.

An interesting feature of this approach is an authentic distinction of the agents’ actions and the results. The agents within the model are similarly to flocking birds (mentioned in classification of Sullivan and Haklay 2000) following only very simple rules which then surprisingly result in a structure observable from outside. As Thomas Schelling recalled 37 years after first model’s publication (2006), the first environment for running the model was a checkboard with agents in the form of a set of coins.

19. Original Ultimatum game has also a variation - Dictator game in which is the second player bound to accept whatever comes. Experiments with both of the games are serving as an argument supporting the socially aware homo sociologicus / homo reciprocans in her clash against the self-centered homo oeconomicus (Veblen & Commons in Vanberg 1993, Gintis in Vatn 2005). The average offer ranges between 40% and 50% for Ultimatum game (Oosterbeek et al. 2004, Gintis in Vatn 2005, De Jong et al. 2008) and around 30% for Dictator game (Forsythe et al. in Vatn 2005, Engel 2010). The difference between the cases can serve as an argument for how big is the share of altruism in comparison with the fear of rejection (Vatn, 2005). The idealism should however be not too much overrated, as not a negligible portion of agents still offer 0% (Engel, 2010). 20. Schelling, together with Robert Aumann, holds 1/2 share on 2005s Nobel prize „for having enhanced our understanding of conflict and cooperation through game-theory analysis“ (Nobel Media, 2005). 21. Result of a run in a world of 102 ∗ 102 fields and 4000 agents serves as a sample utilization of Cellular Automata environment in Figure 2.2c. The featured state of art shows how the world looks like after 1000 iterations when agents demand 60% of their neighborhood being of the same ethnicity.

18 2. Introducing ABMs with emergent Social Institutions

Sugarscape

Epstein and Axtell’s Sugarscape (1996) pictures a cellular automata world in which simple agents economize limited resources. Their natural behavior is to eat as much of the available sugar as possible, regardless of the count of agents they have to slaughter in order to do so. However, once they are able to change their own cultural inheritance and become allies with forthcoming competitors. Sugarscape has inspired many later contributions. Among those which are studied within this thesis is the one of Verhagen (2001), who comes with inclusion of agents’ mutual advisory and cooperation.

Adam Smith Problem

Trade mechanisms of Sugarscape have also served for checking a consistency of Adam Smith’s thoughts on free markets with those which he raised towards morality.

As evoked by Gavin (2018), Adam Smith, who is often being cited as a part of argument towards market-driven society, has apart from An Inquiry into the Nature and Causes of the Wealth of Nations authored a book called The Theory of Moral Sentiments. The latter presents morality as a driver of decisions and therefore can be understood as somehow contradictory to the opinions for which Smith is most known.

As Smith does not put much effort into explaining this ambivalence (Gavin, 2018), the Moral Markets model (as a part of a wider debate on this topic) aims to shine a new light on nature of the problem by simulating whether the ideas are indeed contradictory or they can work together.

Hirearchy

The question of whether it will turn out to be more effective for agents to face cooperative tasks by consensual agreement or by establishment of endogenous hirearchy is a theme for the model of Perret et al. (2016). Cost of having a hirearchy is symbolized by exploitation of the group by its leaders, contrasted by preferences of other agents towards being lead.

Treshold / Diffusion towards convention

Treshold models study the changes of majority’s behavior and aggregate it. Agents within them face „two distinct and mutually exclusive behavioral alternatives“ (Granovetter, 1978, 1422). The focus of the models is on transformation from one alternative to another, which includes e.g. (not) joining a riot or a strike or technological standards etc. (Shoham and Tennenholtz, 1992a,b). Agents in the model of (Shoham and Tennenholtz, 1992a,b)

19 2. Introducing ABMs with emergent Social Institutions

reason about inclining themselves to one of the sides within a binary problem (say adapting either the technological standard of QWERTY or DVORAK keyboard, often denoted as path dependency22), out of which (if everyone decides to do so) benefits the whole population. Agents therefore work on recognizing which of the conventions is more promising and reaggregate in between.

Burke et al. (2006) research convention23 in relation to locality, as they picture a set of neighborhoods where agents decide mostly on the basis of what their neighbours have done when facing a similar situation in the past. An example provided by Burke et al. (2006) himself is a physician, who can be visited by patients A or B to whom they can offer treatment α or β. With regard to that, she takes into considerations what have the fellow [nearby] physicians done in the similar cases.

A problem of path dependency with more than two choices is modeled by Haurand and Stummer (2018) on an example of a society which looks for the best weapon to fight vampires. The model aims to find out, what share of product’s success is accounted for by technological advantage in comparison to advertisement. Decisions of agents are therefore based on various factors including experience, observance, trainings and even marketing.

The most complex model of this type within the present selection (Kangur et al., 2017) is aimed at modeling the decision of the whole market dynamics towards hybrid and electric vehicles. The model is calibrated by an extensive cross-sectional research of Dutch population attitude towards the issue, each of the agents within the model shall therefore represent a real person. Apart from that, there are tens of other parameters, defining performance of cars, markets, taxation etc.

Rules of the road

A radical approach to the puzzle of path-dependency/treshold, Rules of the road model, (Hodgson and Knudsen, 2004) focuses on exclusivity of two conflicting conventions by illustrating a world where the driving side of the road is not decided and the agents are facing a risk of collision [=death] periodically until they decide for themselves a specific side to drive24 on .

22. The choice can actually make its undertaker dependent (locked-in) certain convention for considerable amount of time (Haurand and Stummer, 2018). 23. Convention, often associated with proceedings within Treshold models, could be seen (by the eyes of Savarimuthu and Cranefield 2011) for instance as the softest example of social institution, associated with no sanctions (Anderson & Taylor in Mahmoud et al. 2014, denote this stage as folkways). The next two steps in his scheme are then Social norms (informal sanctions) and Laws (formal sanctioning system. Althought this makes sense for convention as a technological standard, when it comes to joining a mob, it is not really easy to expect no sanctions to be connected with such behavior. 24. Speaking about the actual emergence of riding on the right, this institution did according to Peyton Young (1998) come out of the happenings related to the French Revolution. Until then, claims Young, it was usual to drive on the left side, as one jumps on a horse from the left (in this case from the outside of

20 2. Introducing ABMs with emergent Social Institutions

Epstein’s contribution to this puzzle (2001) is despite the branding closer to a simple convention (with the use of cognitive sight over the space), as the cars here are holding static positions and only observing which side did the other drivers choose. In the introductory article on the case, Epstein (2001, 10) promotes the thesis that „individual thought - or computing - is inversely related to the strength of a social norm“. The paper with a slightly provocative title Learning to be thoughtless emphasizes that we just do not have to think about what we usually do25. The model is run on a ring environment, in which the agents drive on both sides in both directions. Each of the agents (cars) has a specified sampling radius, within which they are able to reason about what side ofthe road is the right to drive on (the one on which the majority drives).

The (randomly) drawn agent scans his neighborhood and with fixed probability either adapts to the rule of majority or chooses randomly (noise). The general knowledge of the norm is represented by the noise, so while the noise is low the simulation produces zones of major conformity to each of the rules, which are said to represent punctuated equilibria as defined by Young in Individual Strategy and Social Structure: An Evolutionary Theory of Institutions (1998). Compared to that, while the randomity of decision making grows, the presented artificial world is similar to granulation.

Hodgson and Knudsen (2004) are revolving around the left/right convention26 on ring environment. Death by frontal impact (unlike Epstein’s model) works here as an indirect selection mechanism27, after which the agents are immediatelly replaced by new ones. The agents obtain more personal characteristics than in the case of Epstein, and also they are getting used to driving on one of these sides after some time of doing so. The so- called habituation later proves to be a key factor for the speed of convergence (therefore the norm gets promoted easily if the people are more used to it, which makes empirical sense).

the road). As the revolutionists aimed to protest against habits of the monarchic class (utilizers of horses and coaches), they made the right side (hitherto used by scum to walk on) the norm. 25. With which he attempts to criticize the game theoretic idea of institutions as equilibria in games the society plays. 26. Where convention, as they point out, is just a particular instance of an institutional rule (Sugden and Searle in Hodgson and Knudsen 2004, 3). 27. Evolutionary approach has been already mentioned in relation to a group of GT-based models as well as John Holland’s concept of Genetic algorithms. The phenomena of direct and indirect selection have been discussed in another nice contribution by (Martins, 2007). Direct selection stands for a selection of practices. It’s opposite, indirect selection covers the cases when the genes are selected by selecting the sucesfull agents who carry them (Weiner, 2015), which is the case of traditional application of Holland’s scheme as overview in section 3.2 .

21 2. Introducing ABMs with emergent Social Institutions

Minority game

In Minority game28, Arthur (1994) wonders about a real-life struggle of Santa Fe inhabi- tants29, who reason about how many people will come to hear Thursday’s night Irish music in El Farol bar in order to decide, whether to give it a go (less than 60 people should show up) or rather stay home (60 or more people make bar unpleasent to stay in). In order to find this out, they employ a series of prediction techniques, whichthey evaluate on a weekly basis (after finding out the actual attendance) and eventually accomodate in order to better fit the reality.

Cascade

Cascade models are related to Treshold models through the focus on agents who adopt certain behavior, by which they participate on its spread. The difference is, that Cascades concern specifically cases, where reaching certain level of general adoption dramatically increases its speed into fast adoption by the majority of agents. Spreading the cascade has often something to do with information.

Standing Ovation problem (Miller and Page, 2004) concerns the case of people on a stadium, which according to quality of the show and behavior of the others decide, whether to stand and applaud. Information cascades appear also in Emperor’s Dilemma (Centola et al., 2005) which in relation to Hans Christian Andersen’s fable contemplates over a question on whether a small group of fanatics is able to push a huge group of people into changing their beliefs regarding an unpopular formal institution (state-supported story saying that emperor is wearing clothes visible only to clever people while he is actually naked).

Opinion dynamics

The model of Duggins (2017) works with changes in (political) opinion within American society. The model combines two-dimensional environment with social networks, where agents (based on their initial properties) interact and mutually shape their attitude towards a problem (number on a continuous scale). The model works with subjectivity classes such as homophily, tolerance and willigness to misrepresent attitudes.

28. For those in the minority are the winners. 29. The selection of Santa Fe as a venue of model-supporting story is not purely coincidential. Santa Fe is a seat of Santa Fe Institute - an interdisciplinary centre for Complexity science (see also https://www.santafe.edu/). More names apart from Brian Arthur will sound throughout this paper, including John Miller, John Holland, Mitchell Waldrop, Robert Axtell and Steven Durlauf. The featured model, assesing a practical issue of institute fellows going out is not the only contribution addressing directly life around the institute - stories of the institutes’ founders are also captured in Mitchell Waldrop’s book Complexity: The Emerging Science at the Edge of Order and Chaos (1993).

22 2. Introducing ABMs with emergent Social Institutions

Pilditch (2017) develops Duggins’s model by combining it with a principle of cascade. Their model works with an idea of echo chambers, local clusters of strong edges with a potential of co-shaping the opinion before it turns public. Agents within this model are using Bayesian reasoning for belief evaluation.

Emergence of money

Karl Menger in his famous article On the Origin of Money (1892) proposed a theory of general agreement on a certain commodity (gradually turning into banknotes and coins) as a medium of exchange. He believed, that there are given properties of commodities which turn them into universally accepted good in barter exchanges and therefore technically turn them into money30.

Marimon et al. (1989) and Moran et al. (2013) are utilizing the model of Kiyotaki & Wright, which is concerned with looking for an optimal trading strategy within a society of three types of agents, who are differing in relations (producer, consumer) towards three goods on the market. Both models use genetic algorithms for optimal strategy selection, Moran et al. (2013) also use occasional mutations in order to test whether the outperformed strategies can be more successfull after the population develops in some direction.

Menger’s original thoughts, which do not concern relations towards the goods as relevant for the case of Kiyotaki & Wright has been made by Gangotena (2016).

2.3 Overview

The preceeding chapter has suggested some ways in which it is possible to think about ABMs with emergence and their position within the discourse.

Firstly, general context of the field has been depicted by introducing two modeling traditions. The first tradition is related to models within Game Theory, where the agents mostly act through interactions with the other agents. The second tradition relates to Arti- ficial Intelligence, within which agents are designed towards exploring the environment they happen to occupy.

The general design of a model has been presented in the categories concerning agents, relationships and environments.

The last section of the chapter focused on examples of models, which will be used for categorizations within following chapters of the thesis.

30. Among alternative views on money emergence is the one of Wray (1998), who believes that the monetary exchanges have developed out of evidence of debts.

3 Classifying Mind within emergent Social Institutions

„The most interesting research topics are simultaneously the most dangerous ones. Be there a case that you won’t find the debate, your only remaining chance is to makeit up yourself...’“

– Josef Menšík, 2016

The debate around causalities related to social institutions is concerned with several disputes, few of which have already been shown in the preceeding chapters. Another example related to institution-mind relation is the well known chicken-egg problem, in this case concerning the issue that institutions are expected (in order to represent a common base of practices) to grow out of minds, but at the same time to serve as constraints for minds. As such, institutions are macro level phenomena, standing in a dialectic relationship to minds.

If we assume the existence of institutions and their influence towards the minds, a solid theory of mind is indeed needed, both from the position of affecting macro level phenomena and being affected by macro phenomena.

This chapter is the beginning of author’s approach to classification of actual position of institutions within the models, which were presented in section 2.2. The consideration follows the logic of Transformational Model of Social Activity (section 1.2). Minds of the virtual agents are chosen as a starting point.

The classification is built inductively from observations of techniques utilised within the studied group of models. The chapter devoted to Mind also includes a few pages on predicative system of Christiano Castelfranchi and its properties, which serves here as a unifying language for defining borderlines of agents’ behavior.

The flow then continues through nature of emergence to the state in which the institutions are already emerged and create backward influence towards minds of agents. The final chapter then overviews the classes and stresses out some of their relations. Specific methods, which modelers use for reflecting certain patterns of reality (such as randomity or bounding factors) are at places accompanying the descriptions to which they mostly relate.

Notes on method of classification

Considerations behind the classificatory approach were focused on balancing the ontological and the functional properties of the models. These views have, however, sometimes slightly clashed. An example of such occasions are reasonings about understanding the element of agent’s death in rules of the road and diffusion of weapons against vampires.

25 3. Classifying Mind within emergent Social Institutions

The sections, where death holds significant meaning for presented classificatory cases, provide discussion on this topic.

All models, which are used for classification are explicitely mentioned within the classes respective to categorization of Mind. The following parts are then based mainly on unifying patterns of the classes and mostly use models carrying meanings which either suit the argument well or (on the contrary) can be used to break it. Explicit sorting of all the models is presented within Table 5.1.

3.1 Towards framing of Mind within ABM context

Brian Arthur, the author of El Farol bar model, in 2000 published an article, addressing the neediness of Economics to deal with the notion of mind. There he notes, that agents who are merely following the rules of the system in which they find themselves are an unreal representation of what is actually human cognition about. He suggests an idea of an agent, who is able to cope with the environment she occupies by cognitive learning and reasoning. This includes her being able to critically asses the environment she occupies, including recognition of metaphores and thinking beyond the actions she takes. The fictional inhabitants of Santa Fe in his model of 1994 are in regard to cognitivite abilities already a bit further than what would the agents as defined within game theoretical context1 be able to do.

Thomas Nagel (1974, 439) in his famous article What is it like to be a bat? nicely describes how to understand actual flows of someone’s mind and its distinction from behavior;

„ Our own experience provides the basic material for our imagination, whose range is therefore limited. It will not help to try to imagine that one has webbing on one’s arms, which enables one to fly around at dusk and dawn catching insects in one’s mouth; that one has very poor vision, and perceives the surrounding world by a system of reflected high-frequency sound signals; and that one spends the day hanging upside down by one’s feet in an attic. In so far as I can imagine this (which is not very far), it tells me only what it would be like for me to behave as a bat behaves. But that is not the question. I want to know what it is like for a bat to be a bat. “

Over the course of defining his understanding of mind, Nagel argues for impossibility of actually putting oneself into someone else’s mind2 and therefore inability to understand someone else’s concepts of certain phenomena.

1. As well as within the basic ABMs of this tradition, which were introduced within the previous chapter. The abilities of agents within both Arthur’s model and the models of GT tradition will be shown throughout this section and chapter. 2. The actual achievement of mind exchange is a favourite topic in popular culture. One of them, Futurama’s episode The Prisoner of Benda features an interesting mathematical problem of reversing a

26 3. Classifying Mind within emergent Social Institutions

This limit, however, does not have to be as binding for the virtual minds - the agents in ABMs are designed by conciouss human modelers to resemble (mostly) human decision-making and they are defined as objects within computer program. A possible way of analyzing them can therefore lay in studying human-life-related parallels encoded within them and to compare their behavior to the one of their real counterparts.

Social scientists concerned with the problem of institutions’ functions, as mentioned in the context of Transformational model, understand institutions and agents as being positioned in a series of dialectic interrelations. Since our own experience (with influence of institutions) will provide basic substrate for any further interaction with institutions, any model starting without institutions would seem unrealistic. The reasoning about the dichotomy, which will last for the rest for the next three chapters, will for the sake of defining the position of mind start with a focus on agents as if they were isolated from the influences3. Focus of the argumentation will therefore be given to the properties of agents’ reasoning, eventually leading to emergence of generally agreed patterns.

Castelfranchi’s conceptualization of Mind processes

Italian psychologist Cristiano Castelfranchi (2000, 135)4, gives a similar opinion to the one of Arthur when calling for the institution-mind issue conceptualization;

„...we cannot build a correct theory of social functions without a good theory of mind and specifically of intentions discriminating intended from unintended (aware) effects, and without a good theory of associative and reinforcement learning on cognitive representations, and finally without top-down and not only a unilateral bottom-up (from micro to macro) view of the relationship between behavior and functions.“

Within the same article, Castelfranchi sketches an object-oriented structure of mind, based on values, beliefs and relations. In the presented system, agents are evaluating certain entity variables with respect to goals and on the basis of values. Values are resulting out of evaluation process, aimed to asses, whether certain means (entity variables) are suitable for achieving the goals. The degree of certainty on the given mean-goal relation (a belief that instrumental value of mean x for the goal y is this and that) is conceptualized in Beliefs and works as a bearer of subjectivity within the concept. mind swap without possibility of swapping within each pair of people more than once, which is according to Simon Singh (author of The Simpsons and their mathematical secrets and host to lectures, one of which serves as a basis for this note) the first case of a theory featured in popular culture before scientific publication. 3. However, as will be revealed later on, they can be seen as existing in some of the models when evaluated from the ontological standpoint. 4. Castelfranchi among other contributions co-authors a model of finder-keeper institution (the right over a piece of sugar is reserved to the agent who found it) being introduced to Sugarscape (Conte and Castelfranchi, 1995), which however does not concern spontaneous emergence.

27 3. Classifying Mind within emergent Social Institutions

Values

The role of values is essential for the whole concept. Values are seen either as context- relevant objects (value of mean x for the goal y), in which case evaluation is necessary. In the other case they are seen as ends in themselves and serve as a higher order/terminal goals, which contribute to the creation of new goals. Castelfranchi mentions the case of having friends as an example of such ultimate goal. Values are also playing the role of prescriptions for institutions. If there is an institution encouraging us not to steal, there is also a value ranking stealing as bad. Values have a set of properties;

1. Unfalsifiability: goodness or badness of something cannot be proved just with the value, as the values carry no instrumentality. Reference to an object is therefore needed.

2. Indefinity: values are relevant for goals within some limits, as the goals can be and usually are context relevant5

3. Normativity: by their nature, values create recommendations for doing something. These recommendations can in fact, as Castelfranchi mentions, be communicable easier than norms („X is good“ instead of „Do X!“)

4. Terminality: „While the goals generated by evaluations are always instrumental, in that they are relativized to the goals implied in the evaluations, the goals and norms generated by values are always terminal, i.e. ends in themselves“ (Castelfranchi, 2000, 118).

Castelfranchi’s system relates to two approaches towards interdependence. The first one, Mutual, covers a situation in which two agents need each other for achieving a common goal (resulting in common activity). The second, Reciprocal, relates to a case of social exchange, where each of the agents needs the other one to fulfill their own goals which can result in cooperation. The agents’ awareness of the emerging phenomena is here denoted as cognitive emergence. Part of this process is a feedback into the minds of the agents which changes them.

Institutions are viewed as prescription for values. Their position (not only in relation to the scheme) will be discussed mostly in chapter 4. Some relation towards their functions can be seen also in terminal goals, which are a theme for the next subsection.

5. Take for example Maslow’s order of needs, where everything but the lowest unsatisfied level is not considered as important in the current condition of the agent. Another example is the concept of Alternative uses of objects as developed by Friedrich von Hayek in The Use of Knowledge in Society (1945), stating that a new, innovative means, to approach certain problems could occur during unconvential situations. For instance, one would likely assign a low value to clothes as a mean for a goal of making a rope, however in the case of a goal of making a rope in a burning house could the value as well as the objective instrumentality differ.

28 3. Classifying Mind within emergent Social Institutions

Goals and Terminal goals

Castelfranchi recognizes two (mutually exclusive) types of systems: Goal oriented system and Goal governed system. The systems can take various forms from animals through humans to machines. The difference between them is, that while the former aimsat delivering a certain result without knowing it precisely and hence works on the basis of some rules or classifiers (the goal is external to the system), the latter workswith precise representation of the goal. In terms of the goals-systems relationship, Castelfranchi sees three ways of transliterating goal from one system to another. The first concerns precise representation of the goal by its copying (internalisation) into the target system (copy-goal). The second considers indirect representation (respondent goal), where a new internal goal is a function of the external goal. The last case are as-if/implicit goals, which are unexplicit parts of the system. Among those belong for example reflexes (jumping out from the potentially colliding car) or implicit utility-affectors (being loved).

The concept of the terminal goal within the present context relates to what kind of general atittude the model presumes agents to hold. For the scenarios, which employ some kind of profit-oriented behavior the concept of terminal goal is symptomatic for maximalization (goal G is to use any mean to maximize profit P) and is directly bound with the actions (agents know the instrumental means and given the constraints exercise them).

When it comes to more advanced scenarios, such as the rules-of-the-road of Hodgson and Knudsen (2004), agents’s terminal goals are to some extent different: the agents here aim to keep fit and for that they act (goal is to drive and live as long as possible). In the case of cascade models (Miller and Page, 2004; Centola et al., 2005), agents can have a terminal goal of not acting dramatically different than the rest of the crowd6.

Some of the models carry an implicit (design-based) valuation of certain behavior (when playing game S it’s socially desirable to select means of subset A from the total set B). The most prominent implementation of such prior belief is Metanorms game. Terminal goals implemented into design to spread desired behavior could have an effect on the scope of surprise which the model can provide. Since the agents in this very scenario are rational and have their own valuation of the possible conduct (defecting in the underlying scenario promises better outcomes), it might easily happen that they override the scenario. As will be shown later, this is the case of Axelrod’s Norms and Metanorms game. Another type of terminal goal is applicable to the case of convention/treshold where it does not have to matter which Mean is prevailent as far as both sides use the same one, therefore the terminal goal is of having a convention acceptable for at least majority of the population.

This functional relationship is within the original approach (Castelfranchi, 2000) coded as goal translation theory and determined as a rather conceptual function. According to

6. For Standing Ovation this carries a practical aspect of actually seeing what is going on at the stadium once people start to applaud.

29 3. Classifying Mind within emergent Social Institutions

Castelfranchi, there should be a recognized category of Socially Autonomous agent. An agent falls into this category if she has her own endogenous goals, is able to decide over a group of conflicting goals, can be influenced by the goals from outside, whichinthe case they make sense she adopts as her own and can not change her own goals without an (external) change of her beliefs.

Entity Variables / Means

Mean has been described as a tool, which can posses certain value of instrumentality towards helping its possesor (agent) pursue goals. The so-far mentioned examples considered Mean as a physical tool, such as clothes (instrumental for the Goal of not feeling cold in winter as well as many others). The undertakings of ABM agents are, however, in most cases not concerned with what kind of tools they use7.

Alternatively, the mean can be associated with a set of actions and their preferences for these actions within the mind of the agent. For instance, if the goal of the agents is to maximize their own gains, such as in traditional game theory setting, the plausible means are actions permissable by the existing strategy, which usage is (for the agent) governed by a degree of belief.

If one would be to imagine a codetable, which gives the agent an ideal guideline to any situation which she faces, the very table (otherwise identificable with strategy) would for this case play the role of a Mean. Once this prior expectation of suitability is challenged by suboptimal reality, changes of belief about the overall composition of the mean make the agent (if she is capable of doing so) choose different options.

Examples of such change can take at least two forms. First, agents can posses a set of scenarios which they utilize and rank them in order to find the most appropriate for the problem they aim to solve. Hence they work like processors which execute various inputs and evaluate which of them fits. In this case, agent playing a role of various scenarios processors. This is the case of Arthur’s El Farol bar model (1994).

The other option is to allow agents to intervene with their own properties; determine which of them fits the tasks they are dealing with and utilize them in the way which gives them greater power over the situation they are dealing with. An example of this type of model is prisonner’s dilemma with evaluation of agent-level information proposed by Nowak et al. (2000).

7. Althought they use some, such as goods (Marimon et al., 1989; Gangotena, 2016), weapons (Haurand and Stummer, 2018) or cars (Epstein, 2001; Hodgson and Knudsen, 2004; Kangur et al., 2017). The current state of the world could also offer a parallel of agents in the second case actually being [self-driven] cars.

30 3. Classifying Mind within emergent Social Institutions

Dimensions of classification

Some of the agents are able to utilize various means over the course of time. Such ability can (as the previous elaboration suggests) arise out of self-development (intervention to the mean) or simply replacing the mean with a different one out of an available set. The second dimension is the belief, which can likewise be dynamic. Once the agent does not apply self-criticism to the means she uses to address the goals (such as in the perfect- rationality setting), belief equals certainty. The opposite case is when interventions to the belief system are allowed.

Let’s consider the ways to intervene in Minds (which will be from now on understood as composites of Means and Beliefs). Agents within the following context all follow specific goals (with specific meaning across the models) by the use of means, whichare properties of the agent with some degrees of instrumentality towards the decision. Once the agent believes that the mean is instrumental for fulfillment of the goal, she keeps on using it.

The difference comes with dissatisfaction. In this case the final decision dependson whether the agent is able to evaluate. In the case that not (agent’s belief equals certainty), the mean remains in power, unless there is a stochastic feature changing it (such as noise, see below). In the case that yes, further decisions depend on how. The first option is, that the agent is able to take a different mean out of a finite set, the second that she can change properties of the mean in use which consequently implies that there is an unspecified range of possible means on a multidimensional continuous scale. The third option is, that agent has a single mean, use of which is in action being manipulated by beliefs - these agents are able to cease from action if the situation deserves that, but not implement it to their reasoning (for they have no memory).

This leaves us with five types of agents, which are shown in Table 3.1. These are agents with fixed means who are either unable to evaluate (and use the same mean for their whole lives consequently) or they are able to intervene into their own values by changing belief. The other group are agents with dynamic minds who can evaluate and change the means, which are either limited to a finite set or not. The last type of agents with dynamic mind can in the same time utilize a set of means, some of which are dynamic. Agents of this category are therefore able to interact with other agents in various ways such as playing the game and advising.

Each of these options is inspected in greater detail within the following sections.

31 3. Classifying Mind within emergent Social Institutions

Means at once Type of mean Beliefs Learning Memory Class One Fixed Fixed No No Fixed mean with certainty One Fixed Variable No No Fixed mean with variable belief One Set Variable Empirical Limited Dynamic mind with set of means One Continuum Variable Cognitive Yes Dynamically changing mind More Continuum Variable Cognitive Yes Dynamic mind with multiple means

Table 3.1: Categories of agents based on properties of their minds, the size of set of possible means which can one agent utilize is growing from top to down.

3.2 Fixed mean agents

Minds of agents within this category stay the same for the whole length of their virtual life. That, however, does not denote stability from researcher’s point of view, at least not in both of the cases. Agents of fixed mean with variable beliefs indeed stay within the model forever and only react to external stimuli. However, Fixed mean agents are being evolutionarily selected and therefore the overall distribution of minds (within the model) changes. Each of these two cases will be inspected within the following subsections.

Fixed mean with certainty

Agents within this category are certain about the instrumentality of means towards the goals, so the reasoning about the mean is not at stake. Consequently they apply a single set of means towards terminal goals they are facing. Therefore, the agents of this kind are having a single strategy which they utilize all over. This is the case for the triade of models based on Prisonner’s dilemma by Axelrod (1997), Tag-based cooperation by him and Riolo (2001) and the Trust game by Bicchieri et al. (2004).

Having fixed mean however does not imply that the agents act in the same ways,for their actions can be affected by a random factor in the form of noise. Noise is a random variable, utilized in some models by randomly changing the strategy of players or intervening into the genes (when applicable). In its most simple meaning (Axelrod, 1997)8 it denotes a share of actions subjective to a stochastic change. For the models

8. In the case of ABMs with norms, noise is likely to be at first used by Axelrod. In the contribution of 1997, he writes about a public repeated PD duel with noise, which he organized at USA-USSR conference on interdepence during the Cold War. This event, organized partly in relation to Axelrod’s seminal book, was partly a research on how to deal with the echo effect - the real-life phenomena from within the Cold War, threatening that one of the sides will destroy the other purely on a basis of a false alarm. The tournament aimed to show what will the representatives of the two sides do when having to count with their action being misinterpreted. Throughout the game, the american representative, Prof. Catherine Kelleher benefited on being defective as she correctly read that the soviet representative, Prof. Sergei Blagovolin, will expect her to be cooperative and even forgive her the defects. Axelrod notes, that the noise can lead to forgiveness, but too much forgiveness consequently leads to exploitation.

32 3. Classifying Mind within emergent Social Institutions with fixed means, noise is the only external factor of variability within the model. Hence, despite the agent being certain about instrumentality of the mean she uses, the actual utilization of means into action is affected by this external feature. The function of noise could be seen as a discrepancy between the agent’s subjective perception of the utilized mean’s instrumentality and its objective functionings.

Minds of the agents are following the principle of maximalization based on a set of bits or continuous parameters. These are for example threshold, denoting to whom will the agent donate in Tag-based cooperation or boldness & vengefulness, identifying whether the agent is likely to punish others for defection, or a lack of punishment within Metanorms game.

However, the population undergoes a change observable from outside, as the agents are subjects to an indirect genetic selection9, which stands as a factor of variety in between generations.A typical approach for modeling indirect selection is through a scaling function. There the total individual score Ai of agent (strategy) i, gained from this behavior together with average generation score A¯ and standard deviation of this average (σ), defines the total headcount of agent’s offsprings Oi as:  0 A ≤ A¯ − σ  i Oi = 1 A¯ − σ < Ai < A¯ + σ (3.1)  2 A¯ + σ ≤ Ai Once the pre-defined number of iterations is over, reproduction of individual traits is allowed according to scaling function together with application of crossover and mu- tation10. The approach is used in all the models authored or co-authored by Axelrod (1997; 2001). Bicchieri et al. uses a special approach, where strategies are in the end of the round re-distributed with a technique of Foster & Young in regards to fitness. The role of this feature is to ensure that no strategy will go totally extinct. This is done by applying a replicator dynamic function, ensuring relative underrepresentation of weaker strategies in the new generation instead of their total extinction as would scaling function at some point do. Althought this technique is not denoted as GA, minds of agents are technically in the same position as in the case of other models within this category.

The base parameters of mindsets are therefore not changing from generation to another, as the genetic selection affects only their frequential distribution (except for random mutations).

A recent example using this type of agents is the model of emergent hirearchy (Perret et al., 2016), where the agents are selected on the basis of their ability to function as a group.Agents are endowed with parameters of influence and intolerance for dominance, which denote their ability to influence others within their patch and their existance under

9. As explained in footnote 27 within chapter 2. 10. An interesting point towards this case is made by Mahmoud et al. (2015), who notice, that Axelrod claims that the population within the model stays the same, which allows for multiple interpretations on how can that be achieved.

33 3. Classifying Mind within emergent Social Institutions

someone’s lead. They function by engaging themselves in collective task (obligatory), the particular execution of which they first have to agree on. Agents have various (random continuous) opinions on how to approach the task, about which they try to convince the others. Those who do well in persuasion are then rewarded by greater share of the outcome. The total gains are then used as weights for the scaling function.

Fixed mean with variable belief

Agents in these models posses still a single set of means, which execution towards the goals can be affected by momentual changes of beliefs, resulting out of external factors. There is, however, still no enduring impact into the mind (which is related to the fact that they have no memory). Agents stay within the environment forever, which implies that the frequential distribution of mindsets within the model stays constant unlike in the previous case.

Agents of this type are typically possesing a homogenous goal11. The goal of agents within rules of the road model by Epstein (2001) is to drive on the correct side of the road. The instrumentality of the current decision towards fulfilment of this goal is assesed through evaluation of agent’s vicinity, eventually leading to changing the distance which agent observes. Certainty of the current side’s instrumentality is therefore achieved endogenously.

The goal of agents within segregational model of Schelling (1971) concerns the structure of their von Neumann neighborhood, specifically regarding the share of the same eth- nical/cultural group within (originally equals 62.5%). Their mean for achieving the goal is of moving themselves into the nearest possible free spot allowing them to fulfill the goal, while the alternatives is for them to stay at the same spot once the goal is fulfilled. Instrumentality of the mean (and hence the belief) therefore changes with regard to current situation within the neighborhood in comparison with the goal. Using the optimal medium of exchange is the goal of agents within the application of Menger’s model on evolution of money by Gangotena (2016). Instrumentality of each of them is assesed through obtained information on liquidity of the goods and usability within the exchange system. Once the agents find out that it is not worth it for them to trade (no mutual coincidence of wants), they stay passive.

A feature facilitating the transmission of belief-changing information from without to the mind of the agent is often denoted as private signal. Its utilization can be seen for instance in cascade models, Standing Ovation problem (Miller and Page, 2004) and Emperor’s Dilemma (Centola et al., 2005) as well as the model of Burke et al. (2006), questioning the effect of locality on variance in decision-making. Agents within these models face contradictions in between their own beliefs and the social context. This particularly concerns prioritizing their own evaluation of what is going on (Shall I publicly disclaim

11. Their heterogenity is however ensured for from the terms of their positioning, as notes Gavin (2018).

34 3. Classifying Mind within emergent Social Institutions

(a) Situation with low search radius (b) Effect of a shock (p.15) (p.17)

Figure 3.1: Rules of the road model of statically-positioned agents adressing thougthlesness by Epstein (2001). Left sides of graphs shows the adapted institution, the right measures the degree of certainty the agents hold. There the black agents are those who are certain about the road side they drive on being the correct one (notice that agents who doubt are those on the borderlines). that emperor is naked? Was the show good? Is it usual to order care T for symptoms E?) or go with the crowd. Reasoning in these cases is affected by an individual threshold.

In Eshel’s (2000) model the private signal consists of an information on gains resulting from altruistic and egoistic behavior within the vicinity. Two meanings of neighborhoods are considered: the learning (to get stats from) and the action one (to interact in). In Eshel’s model the available information is limited to aggregates of the neighborhood, where agents should decide whether to act altruistically or egoistically12. This information serves for evaluation of the belief regarding the half of the society which is for the agent more profitable to be in. The mentioned approach is factically an example of bounding the reach (sight) of agents.

The model of Hodgson and Knudsen (2004), belonging to the rules of the road type is an interesting case of belief formation, affected by the length of agent’s succesfull functioning within the system (realising the goal of not crashing). Their study on simple traffic convention emergence considers agents predisposed in a variety of parameters. Together with the predisposition, they acquire a parameter of habituation, which is given by a habit update function and accounts for how sure the agent is about the road side she drives on. Agents as observed from within the model are unable to stay idle (they

12. The authors put an emphasis on the resemblance of this composition with another famous scenario; „Clearly, our individuals are not aware that they are, in fact, playing a version of the Prisoner’s Dilemma. If they understood it, then in a one-shot game they would all become egoists, since it is always better to be an egoist whoever your neighbors are“ (p.344).

35 3. Classifying Mind within emergent Social Institutions

always drive), however conceptually they are idle when considering the position of mind (mean of changing side is no longer instrumential to the goal of driving on the correct side).

The models of Social Dynamics (Duggins, 2017; Pilditch, 2017) are showing a case when the agents decide about their attitude towards some pieces of information. Within this process, belief actually fullfils its meaning, as the degree to which the agents believe in the piece of information determines their mean to encode it into their reasoning and potentially spread it as their own.

3.3 Dynamic mind agents

Dynamicity of the mind refers to the cases when the agents are able to permanently accomodate themselves to the environment which they occupy, should this regard spatial situation or behavior of other agents in vicinity. Minds of agents are more like a processors of a finite set of automata (Social base of means), as they only utilize various means and rank their suitability for adressing given situation. The agents with dynamically changing minds have the greatest possibility of mind-interventions out of all considered classes of agents, as they can accomodate themselves to the environment by permanently changing parameters of the mind. The case of Multiple means is a subclass of the latter, capturing the case of agents being able to undertake more than one type of action.

Social base of means

Minds of agents within this category are able to change the means they posses by replacing them for different ones out of a common set. This is done by evaluating the means in practice and ranking them, for example by comparing real and expected value of prediction or by ranking techniques such as the Highest Cummulative Reward13. Ranking technique here supplements the role of memory, as ranking of each of the means depends on what the agents remember.

HCR is applied for example to threshold model14 of Kittock (1994). Agents (who are in this case denoted to be robots) are endowed with a finite set of strategies, out of which they choose an appropriate one based on their own feedbacks from past interactions (althought their memory is limited - every new feedback deletes the oldest one).

13. HCR henceforward, brought by Shoham & Tennenholtz in 1993. Denotes the sum of total payout featured by given strategy. HCR can be applied together with the HCR update rule, defining that „an agent switches to a new action if the total payoff obtained from that action in the latest m iterations is greater than the payoff obtained from the currently-chosen action in the same time period“ (Shoham and Tennenholtz, 1997, 149). 14. The principal functioning of threshold type of problem is the same as within Epstein’s model overviewed within the previous section - agents’ goals are of reaching the correct convention, as their payoff is hurt whenever they are confronted with an agent utilizing the opposite choice.

36 3. Classifying Mind within emergent Social Institutions

Goal of agents within El Farol bar case (Arthur, 1994) is to enjoy the time spent in the eponymous bar once there (aka ideally go to a bar at the moment when it is not crowded15). In order to succeed they employ means in the form of predictors. Belief in the particular means is then assesed by a comparison of the predicted value with the real turnout in the bar regardless of whether they actually went there or not. Representation of the common set of means within Kiyotaki-Wright’s money-emergence model (Marimon et al., 1989) is a general set of classifiers, which the agents utilize in trade and based on its strength in trade possibly cast it off. Therefore they asses instrumentality of a classifier towards valuation of the goods.

Agents within model of Dutch alternatively-fueled-cars diffusion (Kangur et al., 2017) are chasing a variety of goals - they are interested in how sustainable is their lifestyle, what is their social position as well as personal preferences. The means which they utilize are various cars, which the agents are able to buy or lease. Influences on their belief that a certain car is suitable for the goals of the agent are made through various learning sources, including influence of other agents, media and test drives.

Dynamically changing mind

Agents of this class can operationally switch between means. The process of evaluation is done with utilization of memory and/or interactions with the others, in this case not limited to direct influence on the primary goal. In other words, agents within this model can address the goal by utilizing various combinations of means out of a k-dimensional space, where k denotes the total number of involved parameters in their permissable values.

Agents of this class can use the memories of past interactions based on what they recognize or what they have experienced in former undertakings. Behavior of agents typically comprehends changing pertinence to a group, strategy or both.

The simplest case of this sort is the general case of convention acception by Shoham and Tennenholtz (1992b), where the agents evaluate the ideal bit based on their own history of interactions. The concept of memory is also utilized in Prisoner’s Dilemma by Lindgren (1997). Agents within this model can remember a few rounds of the game and hence change the beliefs on how to play. Each agent posseses a gene, defining how she reacts to actions of the others in the game. Finally, genetic selection with an occasional mutations is exercised.

Agents of Macy and Skvoretz (1998) also play iterated Prisoner’s dilemma, in which they have a possibility of exiting the game16. This possibility can be executed by an act of (dis)trust from one agent to the other, as they can (under some conditions) observe

15. It is interesting that agents are not likewise afraid of not enough people showing up, althought listening to a band in totally empty place can be awkward. 16. As a reaction to an article of Orbell & Dawes, pointing out, that Prisoner’s dilemma is mostly played by people who are not real prisoners and therefore can stop playing whenever they want to. 37 3. Classifying Mind within emergent Social Institutions

which agent is defector/cooperator. Possible strategies (incl. decision not to play) are given fitness, based on which they propagate by the featured scaling function. This model includes direct selection, a special approach to genetic recombination feature (opened within section 2) - when agents approach a partner with fitter strategy, a part of their 15-string gene can get randomly replaced. This case is (as authors note) a special application of the genetic selection, as it is related to parts of agent’s behavior and not to agents (strategies) themselves. The agents therefore de-facto learn themselves on how to get better in a certain manner.

Another case is that there are information regarding a certain case of mean-outcome combination, with possible promising consequences if implemented into the means of the evaluating agent. This latter case of information which have somehow spread can be seen in rumours about alpha males or generally people who publicly disclaim success. Public visibility of evaluation-relevant information is present in the model of Nowak et al. (2000), concerning the role of reputation - agents here are given fractional information of forthcoming plays within Ultimatum Game. Their processing then allows them to construct competitor-level-means for further plays with the very agent (optimal means for approaching agent a are evaluated with the use of her minimal reservation price17.).

Multiple means

Agents within this category are utilizing more than a single mean at the same moment. They are therefore performing various types of actions towards the external environment. These means can take various forms, but at least one of them is dynamic. Models featured here typically concern more than one type of agents’ roles.

A novel feature from within the category is feedback provided by another agent. This can be connected with unequal positions of agents within the model, such as the presence of a society leader. The Sugarscape-context model of Verhagen (2001) features a multilateral relationship. Agents there act within a group with a leader and a common goal of optimizing the functioning of the group to collect resources. Agents within the group have different capabilities for realising the choice (objective instrumentality of means). Once they decide on what to do (actions include taking some or all the resources, moving to different spot or doing nothing), they share the information and get feedback consisting of what would the other members of the group do (alternative means), which the agent can evaluate and potentially update the decision tree, in case that it allows her to score better given the capabilities she has. This kind of consensual evaluation is present also in the models of Savarimuthu et. al. (2007; 2009). There the agents are able to compare their own past performance within Ultimatum game with the others in the neighborhood (network) and ask the best performing ones for advice. Autonomy, a

17. A parallel can be made with a labour or any other market, where a public disclaim of reservation price co-shapes the expectations of the other involved agents regarding the behavior of the agent in similar situations.

38 3. Classifying Mind within emergent Social Institutions parameter defining how much a certain agent is willing to give/accept advice, isthen responsible for whether advisor-advised relationship can be established.

A more sophisticated learning algorithm is utilized by Mahmoud et al. (2012, 2017) who relates to metanorms scenario of Axelrod with a Boldness-Vengefulness learning algorithm. Agents take into account their past performance decomposed by each of the actions in which one can (under)score18. The agents therefore selectively evaluate each of the means they utilize.

Both models are innovative in means which the agents posses towards learning. The first of the models (Mahmoud et al., 2012) gives agents a property of exploration rate, which predisposes them a rate at which they adapt random strategies from within the environment. The newer model (Mahmoud et al., 2017) features an application of independent learning, in which the agents improve their own strategies regardless of forthcoming outcomes, which was found to lead to a sharp growth of boldness. Authors explain this phenomena by causation from outliers within the utilized environment (small world network). These find out, that they can easily fulfill the goal of maximalization bybeing bold towards the neighbours, for there is a rather low chance that they get punished. For hubs it is then not worth it to be vengefull, as there are way too many outliers to punish and vengefulness is costly. Therefore they also adapt the belief of boldness being the best stake to hold19.

The vampire economy model of Haurand and Stummer (2018)20 includes three types of agents - people, vampires and marketeers. People aim to survive attacks of vampires. For the sake of that they can use various means in the form of weapon production (garlic, stakes, bullets, ...), attending trainings or producing bread (in order not to starve). People are endowed with a prior talent for using certain weapons (they are able to carry one at time) and are also able to interact with each other and co-shape social attitude on each of the weapons. The overall attitude towards the actual choice of weapon is being shaped on the basis of each agents’ experience (starting with the talent and developing through succesfull and unsuccesfull fights as well as observed fights), which then agents(in communication) send to the general discourse. Vampires are aiming toward sucking people’s blood, which they can (in limited amount) carry and with a fixed probability also turn people in vampires. They move through the environment, but are not able to develop their abilities in any way. Marketeers are only important for spreading news about weapons. They along with vampires do not age (unlike people).

Moral Markets aka Adam Smith problem (Gavin, 2018) are a special case, where agents pursue antagonistic goals. Agents are functioning within Sugarscape-like environment in which they are affected by codes of behavior relevant for their specific jobs. Similarly to

18. Defection score (DS, total temptation rewards - total punishments from defections), punishment score (PS, gained punishments from the others) and punishment omission score (POS, gained metapunishments for not punishing someone). 19. Norms and Metanorms games are being repeatedly examined for these types of scenarios. Some of the findings will be discussed in section 4.1. 20. Which is based on a simpler model on similar topic by Farhat (Haurand and Stummer, 2018).

39 3. Classifying Mind within emergent Social Institutions

Figure 3.2: Complexity of routines within distinct simulation periods of vampire economics (Haurand and Stummer, 2018, 378).

Sugarscape, their means are of producing and trading goods. Additional element allows them to specialize - make a decision to employ a change in their behavior, which allows them to increase their levels of productions. However, once they are too different from the production of the other good (being it sugar or spice), the trade usually does not happen as the agents within the model care about who the counterparty is21. The agents are, however, unable to weight the changes in the behavior and hence they have to choose whether to behave in accordance with market or morals.

3.4 Overview

The chapter has started with an overview of basic concepts within a defaulting approach to mind conceptualization by Christiano Castelfranchi.

Mean has been understood as a function which translates the inputs to outputs. Utiliza- tion of means serves the agent on the way towards fulfilment of the goals. The goals can take two forms. First of the posibilities is a terminal goal, which is a synonyme for overall definition of the basis on which the agent is ranked, is embedded within

21. As explained by the author on page 328: „These two principles, specialization and discrimination, are meant to correspond analogically to Smith’s theory of natural inequalities in the profits of labor and stock, whereby economic specialization provokes moral demands that many people are unwilling to make. Just as Adam Smith’s modest ladies refuse to debase themselves by singing in public, so too agents refuse to do business when their sensibilities are too greatly offended.“

40 3. Classifying Mind within emergent Social Institutions the model and is the same for all the agents. The non-terminal goals are important for sub-tasks on the way towards the terminal goals and for most of the agents they are united with terminal goals (as agents always directly approach their desired final-state). However, an exception lays within the class of agents with multiple means, where the agents perform multiple types of actions and therefore are not concerned only with the final state but take side-actions which help them by partially improving their other means on the way towards goal fulfilment.

Flexibility of agent’s means is an important criterion for discussing the type of mind which she posseses. Understanding of mind fixation is, that the agent’s mind is closed to external influences. Chances of changing the behavior can then be null, which is the case of agents with fixed mind and certainty, who only use their means without considering what it brings them. Alternatively, agents have a chance to decide whether to use the mean or stay idle, which is what agents of fixed mind and variable beliefs do.

The function of belief is important for mean utilization through the whole context. Belief is considered as agent-level metric of subjective mean instrumentality towards the goal of the agent. Belief can be changed by agents’ interactions with the environment, which can include both interacting with other agents and deriving knowledge from the environment through tools of cognition.

In the cases of agents with dynamic minds beliefs are an important feature which helps agents to evaluate the means they use and potentially change their minds towards alternative means. Chances of having a different than default mean are dependent on the way of agents’ minds dynamics. Agents with social base of means have limited memories, which help them to carry records on beliefs for each of the possible means within a publicly available set through which they can choose. Agents with dynamically changing minds build their own instance of means from any information and beliefs they collect and memorize. Agents with multiple means have different means (agendas), which allow them to (also dynamically) shape the mean aimed towards their terminal goal.

There are also numerous techniques for manipulation with agent-level emergent properties from the level of design. These include application of noise (affects choice no matter the means), direct or indirect genetic selection (affects frequential distribution of means) and internal information flows, which can dynamically intervene with the scope of knowledge agent posseses.

4 Classifying emergent Social Institutions

„If you didn’t grow it, you didn’t explain it.“ Marchionni and Ylikoski (2013, 329)

– Joshua Epstein, 2006

The forthcoming classification of mind processes has been concerned with the agents’ capacities for decision-making and the way how they evaluate instrumentality of possesed means towards goals.

In the case of presented scheme of Castelfranchi (2000) institutions are a special type of goals with prescriptive function. As such, they posses implicit valuation - agents do not have to evaluate them in order for them to have a value. Another provided consideration explains institutions as terminal goals created out of evaluation. Hence, be there a particular goal (wearing white-blue by seaside) with high consensual valuation (white-blue colours are generally believed to be a good outfit for seaside pavements), institution is emerging directly from the minds. His consideration of emergence is closely connected to consciousness, for he believes that we can only understand agents as behaving accordingly with the institution from the moment when they realize what the institution is1.

Consciousness is according to Castelfranchi (2000) a key factor for identifying whether the institution have emerged. Therefore, within the utilized taxonomical concept the institution should become an internal part of the mind. The opposite case is a mere imitation, which does not affect the values and according to Castelfranchi also does not allow the agent to fully understand the nature of the phenomena. Some relation can be seen within the approach of Macy and Willer (2002), who have divided the models with emergence into those with emergent structure and with emergent social order. Emergent structure stands for the case where the agents (driven by response to influences and pressures) eventually reach a convention (explanandum), whereas Emergent social order describes a case where the social ties serve as explanans of succesfull collective actions, following initial situation in which the agents are dominantly pursuing egoistic goals.

Emergence within some of the models is connected to macro-level phenomena observable from above the agents. Elaboration over them will be used as a bridge between sections on mechanisms of emergence and those on backward influence of emerged institutions to the system along with arguing towards irreducibility of some of them to the level of agents.

1. Which is a point of friction in between individualistic and holistic understanding of institutions, as will be noted later on.

43 4. Classifying emergent Social Institutions

Figure 4.1: A very small groups of isolated radicals are not enough to spread a cascade (Naked emperor model on Voronoi’s diagram, Centola et al. 2005, 1032)

4.1 Emergence

The question for evaluation within this section is through which mind-related processes do the institutions emerge?

The idea of agents, who are in their minds clearly aware of their code of behavior being a part of something bigger is close to the ideas of individualism2. The opposing, holistic approach, would on the contrary argue for the state of oblique following, in which the agents result to similar code of behavior, however with a presumption of not only being unaware of the institution, but perhaps even thinking that their agenda focuses sometwhere else.

Being part of a crowd which is coordinated in doing something does not have to be exactly the same as having the intention towards doing it. The proposed idea of coupling emergence with processes of the mind will therefore consists of arguing towards the difference in between incorporating institutional pattern within the mindset and showing a behavioral unity.

The cases of micro emergence, carrying implicit relations towards the concept of mind as utilized so far are presented in section 4.1. Section 4.2 considers the cases of emerging macro phenomena, standing beside the so far utilized concept. These cases are discussed from the positions of dispensability and reductionism and classified as weak emergence. Selected cases of design-level imposements are discussed together with attributes they impact.

2. See the initial distinction in chapter 1.

44 4. Classifying emergent Social Institutions

Table 4.1: Categories of emergent institutions

Unity of minds Unity of behavior Class Yes Yes Function of mind No Yes Function of behavior

Micro-level phenomena (Functions of agent properties)

Institution in the form of a function of agent properties is derived directly out of agents’ minds and therefore is observable or identifiable by the agents’ minds as defined in the previous section. The distinction between the two subsets relates to the character of coordination: institutions internalized into the mindset cover the case when agents have the same values: their minds are predetermined to approach the same types of situations with the same means. The eventuality of coordination lies on causes instead of consequences in the case of the latter category of institutions as behavioral patterns. In this case the agents have a different mindset, but in the end act in accordance.

As have been mentioned earlier, the behavioral class is considered as superior to the one concerning institutions residing in the mind.

Institution internalized into the mindset

This category concerns institutions emerging into the minds of the agents and hence acting as a prescription for values within them. Impact to the distinct categories from the forthcoming scheme will be inspected together with relevant context.

Genetic selection (through the scaling function) leads to implicit prevailence of those mindsets which are succesfull in the interaction (experiencing high outcomes). Conse- quently, if the genetic selection of mindsets (having a form of selecting the agents within the model) is undertaken, then the agents fulfill the demands of having the institution prescripted in mind.

The meaning of the institution here, however, does not necessarily equal with what the model design prescribes as desirable behavior. An example is the case of norms and metanorms game, where Axelrod (1997) himself identifies ambivalence in results. His simulations have shown, that in norms game scenario (where the agents are not considering being punished or punishing for defection), results lead either to a combination of mindsets with high vengefulness and low boldness (institution prescribes high value of good means, which is the desired outcome of the model design) or the other way round (in which case the emerging institution prescribes high value to the act of defection).

Metanorms scenario, where the mindsets contain a belief towards punishing those agents who defect, deals with randomity and limits collapses to those which are dependent on

45 4. Classifying emergent Social Institutions

the initial conditions. According to Axelrod’s results, collapse is at stake once the initial level of vengefulness is low (and hence there is already a norm prescribing the act of boldness as permissible).

Galan and Izquierdo (2005); Mahmoud et al. (2010) have run a series of sensitivity tests to show that the results of Axelrod are not as stable as he believed3. The findings of Galan and Izquierdo include that given the cost of vengeance the society can easily flip back to boldness. Mahmoud et al. then (2012; 2015; 2017) attempted to improve weaknesses of the model. In the first contribution of 2012 they introduce a model with limited rationality and ability to learn by adopting a strategy of the best performing agent.

However. the computational powers which Axelrod had at the moment of model publication, did not allow him to foresee that even though the institutions initially emerge, the bad institution of boldness still overrides the system in majority of cases within very long runs sooner than within 200th generation of 106 modeled by Galan and Izquierdo (2005), who made this observation. The process which they identified starts with a notion that enforcing the norm is for agents with maximalization as a goal costly. Then once the society reaches the initial state of emergent institution (with defection and non-vengefulness being punished), boldness is very rare in the society so those agents whose mindset contains high vengefulness (no valuation is considered here, as in the case of this type of models we operate with certainty) are carrying high costs of applying punishment in comparison with losses they would face from bold agents’ defections. As a conseuqence of that, the vengefull mindsets are selected out, which leads to a backward loop of boldness as boldness is in such constellation very competetive (and rewarding).

Such situation is not hard to imagine within the real world, where cheating on people who believe in other’s timidity is always easier than if the counterparty is suspicious. Axelrod (1997) features results of Timur Kuran, who associates the fall of Soviet Union with belief of regime opposition that acts of protest uprising would not be violently suppressed.

A possible finding from the observations made by Axelrod and Galan and Izquierdo is that the institution in the studied cases actually emerges, althought they contain (probably undesired) prescription of high value to being bold.

Both classes of agents with variable means are also within this category. For the latter case that means, that once agents choose out of a social base of means, they take the mean as their own. Once such instance of mindset becomes prevailent, agents who use them as their own are fulfilling the case of having the institution inside their minds. The caseof dynamically changing minds offers way more possible combination of mindsets within a context of a single agent and scenario.

3. Together with an acknowledgement of limited computational capacities he could have used back in 1986.

46 4. Classifying emergent Social Institutions

Beliefs into instrumentality of the current mean are in this case formed by a constant process. The process of reaching the optimal mean is similar to the one utilized by agents who take their means out of the social base with a difference in the scope of possible combinations. Definition of the specific cases is dependent on the formation of beliefs. For example in the model of Verhagen (2001) belief is determined by feedbacks provided by other agents within the cluster. Once the feedbacks are good, agent ceases to make any further changes, as her belief is confirmed by external evaluation. At this point the institution is internalized into the mind and emerges, if all agents within the group manage to agree on optimal setting of the means.

Institution as behavioral pattern

The category of prevailent behavior is a superset to the previous one. It consists of a prevailent strategy, which implies that the case of agents who take the norm as a mindset, features also unity in behavior as featured within this subsection.

The difference between the case of behavioral prevailence in comparison with theformer class is in the difference between possesing belief and possesing means. Models, denoted within this context are those of fixed means with variable beliefs. The agents of this type can follow certain institution without actually understanding them.

Approach of these agents can be denoted as opportunistic (without any negative con- notations), for their actual obediance to the institution is context-dependent. Agents with fixed means have a special characteristic of staying idle once the mean is nolonger instrumental to the goal. In other words, once there is nobody driving in opposite direction on the same side of the road, there is no reason to think about changing their own direction as having any sense towards the goal of coallition in convention. The institution in this case is, as noted by Epstein (2001), thoughtlessly accepted.

Gangotena’s model of Menger’s monetary emergence (2016) is a nice finding relating to general theory of post-Brettonwood monetary systems being supported merely by beliefs of people. Insistence on a certain mean of exchange is in this case unrelated to monetary unit U, but is only related to the number of people who share the same belief, here serving as a mean of attributing values to certain units, as the goal is only concerned with size of the belief. Shocks to belief can consequently lead to quick and surprising transitions in between the choices.

4.2 Macro-level phenomena

Generally, spontaneous emergence of a macro pattern is being presented in models where local perceptions of the agents are considered at the point of goal fulfilment (low belief towards instrumentality of any other use of means) among a considerable group

47 4. Classifying emergent Social Institutions

Table 4.2: Irreductibility and novelty interpreted by Dessalles et al. (2008, 6) from Achim Stephan. The table involves four categories of emergent states. Starting with columns, Synchronicity stands for the possibility of emergent phenomena existing right in the time of emergence, where in Diachronic emergence they are merely a consequence of a process. The difference in between rows is given by (ir)reductibility. While the Weak emergent properties are attributable only to the structure (and hence cannot be reduced to the agents), the Strong ones are observable both from within (reducibility of the norm to properties of the agents) and from without.

Synchronic determination Diachronic determination Weak (reducible) Weak Emergentism Weak Diachronic Emergentism Strong (irreducible) Strong Synchronic Emergentism Strong Diachronic Emergentism

of the agents. Hence the agents think about whether it is better to behave egoistically or alruistically (Eshel et al., 2000), on which side of the road is it better to drive (Kittock, 1994; Epstein, 2001; Hodgson and Knudsen, 2004), what is being considered a good mean for a certain goal (Burke et al., 2006) or what does the general society believes in (Miller and Page, 2004; Centola et al., 2005).

From an initial observance it could seem that the models connected with macroscopic phenomena are those which show emergence out of behavior but not out of mind, specifically those which are locally bounded in vision.

As has been showed by Mahmoud et al. (2014)4, application of spatial constraints on the one hand helps emergence to occur but on the other it turns the system into constellation in which no universal convention emerges. These results were later proved by Mahmoud et al. (2017) and also studied by Lindgren (1997, 362). The latter author notes that „the possibility of spatiotemporal phenomena may give rise to a stable coexistence between strategies that would otherwise be outcompeted.“

This, however, does not have to mean, that weakly emergent structures can not be observed within the models of soup environment (fully connected network) or unconstrained vision on 2D cellular automata. The difference is, that for instance a scenario of convention diffusion would make them unicoloured (not far from what Epstein pictures in a scheme reprinted as Figure 3.1b). However, the motivation of modeling local clusters of the same behavior or property is likely not to set parameters which would allow us to obtain a nice picture but to research what happens if the conditions change, which is exactly what is done by Epstein (2001) and many others among the mentioned authors.

4. And at first featured within section 2.1.

48 4. Classifying emergent Social Institutions

(a) t = 0.2, unbinding parameter (b) t = 0.3, structures begin to form

Figure 4.2: Segregation as the art of possible and desirable (reaching the goal given treshold). Utilized script is a slightly edited version of the one featured within the tutorial of Davies (2016) which is based on the model of Schelling (1971).

49 4. Classifying emergent Social Institutions

Ontological properties of the phenomena

When the happenings within the model are emerging into a structure observable from above, agents are likely unable to observe how the overall map looks like unless they can view the structures from above (such as flags or other signs on houses of people of certain property5). If the visibility would be possible not only within the computer but also in reality, it would be enough for agents to take a plane or climb a mountain to observe the overall situation as we do6. Agents can in this case only be aware of their own momentual state of mind (such as the goal of living in a neighborhood of certain properties being fulfilled) which can make them stay idle. As they are having no means to step out of the scheme and observe it from above, they are in the same time unaware of phenomena emerging on the macro level. Even if the agents would be able to see the constellation, there is still one more property to evaluate, which is whether the constellation (structure) could posses some backward causal powers towards the agents.

Achim Stephan (featured in Dessalles et al. 2008 with a goal of directly serving emergence within ABMs) conceptualizes emergence together with a consideration of time (Table 4.2). The approach is a part of micro-macro debate, which targets interdependence between phenomena behind the emergence (for which in this case stand agents and their minds) and the wholes (aggregations) which happen to show some patterns. The specific case of emergence depends on whether the wholes are directly dependent onthe units (Dessalles et al., 2008). The diachronic phenomena are within Stephan’s concept those, which are observable only after a sequence of events (they are a consequence), while the synchronic ones are in some shape observable at any time.

According to the theory there are four possible cases of relation between happenings within the model and the phenomena emerging out of it. Emergent phenomena in this meaning are those which are independent on the macro level (functioning as a composite of micro parts) and as such belong to the system itself.

The difference between the weak and the strong type of emergentism concerns the role of mutual causality, for the phenomena concerning the strong emergence are also supervenient (possesing causal powers) towards the system (and hence can be observed both from without and from within), the weakly emergent ones are observable only from the outside. Idea of strong emergent phenomena is closer to the position of individualism, which presumes any macro phenomena being decomposable into individuals and their relations without losing any meanings (Weiner, 2015). Holists would question such presumption by saying that such object is attributable only to the system itself and as such is irreducible (Dessalles et al., 2008; Weiner, 2015).

5. For the example of road direction would this actually be quite easy. 6. The approach of defining an agent possesing power of macro-level view (and therefore alsoof understanding the nature of the phenomena) is actually used as an instrument for bridging macro and micro level in these types of cases. Among those who use it are for instance Dessalles et al. (2008), who along with this contribution also classify the featured type of emergence.

50 4. Classifying emergent Social Institutions

Weak emergence

The initially mentioned models (Schelling, 1971; Epstein, 2001) are cases of weak emergence, for they represent consequences of actions. Once the pattern emerges, the agents stay idle within the system (their goal has been fulfilled and hence the mean of moving to different neighborhood/changing road side is not highly valuated). The fulfilment of the goal is in this case instrumental to the emergence of the pattern - if the agents are unable to reach the goal (for example by having too high demands, as can be seen in Figure 4.2d), outsiders can observe nothing else than grain. It, however, does not have to apply, that a model with macroscopic emergent structure is in a stable state - some agents can always be moving (both in spatial point of view and the state of their minds).

Presuming this case of emergence to be strong would create a demand for ascribing the system more properties than it actually has. The actual state of the agent (her property of goal fullfilment) here relates only to the stability of the system, not the resulting pattern; any changes to the pattern, which would respect the distances between each of the coloured sections are irrelevant to properties of the agents. Hypothetical flags which the agents would put on their houses to express pertinence to the group they belong to would only help the pattern to be seen from above, but not the macro-micro influence (agents still do not act according to the design of the shape but only according to situation within their vicinity).

Cases of weak emergence are hard to reason about from the position of marginalized as the institution is only an aggregate and hence there is no macro-micro impact. Agents within the system in such cases do not have to be affected enough in order to actually ostracize the strangers. Not participating in models such as Sugarscape or Segregation is not really a matter of free intention, but rather a matter of being able to fulfill the goals. The imprint of institutions towards the agents is therefore mainly related to whatever happens to them for not fulfilling the goal without any necessity of pressure from the outside. The agents in Sugarscape (Epstein and Axtell, 1996) have to move and chase the resources (suger or sugar and spice depending on exact case) in order to survive. Their deaths due to not suceeding to do so are, however, caused not by existence of some institutions (such as trade patterns which we can see from above, but from them being unable to fulfill their goals. The cases of whether the institutions will propagate in downwards direction are therefore dependent on whether their existence will become a limitation towards the agents who are not taking part.

Being a part of a group which has resulted into certain behavior can be particularly beneficial in situations when the goals are connected with being among the (local) majority. The agents, who only aggregate themselves in behavior but do not include the institution into their minds7 are chasing a goal of being within an aggregate of some behavior, which does not concern what kind of behavior they do actually express. However, the fact that they are a part of a group with unified behavior gives them an implicit valuation of

7. Which is the case of models within the complement of institution as a behavioral pattern set to its subset in the form of institutions internalized into mindset.

51 4. Classifying emergent Social Institutions

behavior which they express without letting them worry about the other choices (which are out of question as the goal of agents is to behave the same as the others and means of changing behavior are activated only once they are different).

Conceptual dealing with irreducible phenomena

It can be argued, that once we consider the state of a computational model, there should always be a (computational) way how to deduce the observation to the level of agents. One could for instance extract sizes of the zones and reason about positions of agents and their properties. The possibility of actually using such exercise for extracting beneficial knowledge unobtainable from agents local positioning within the environment, stays anyway doubtful.

The agents at this point have acted on the basis of their goal. Following that, an institution has on the basis of agents’ cognitive functions emerged either into their minds or only into their behavior. Agents at that point are undertaking two situations - first, their mind and/or behavior have been affected in the way which makes them behave accordingly with the (now) common mindset. Second, their actions have resulted into a pattern, which they are not aware of. More precisely, agents are located somewhere within a space, which could look for example as the world in the case of Schelling’s segregations depicted on Figure 2.2c. If they succeed to arrange themselves (their demands are not those regarding too much of their neighbours coming out of the same group as shown in Figure 4.2d), what they perceive is a fulfiled individual goal of placing themselves into a neighborhood with desired properties. Therefore the ordering which they might perceive from below does not have to be in relation to what is happening above. In the case of Emperor’s Dilemma (Centola et al., 2005), agents are aware of happenings around them (the share of believers, supporters etc.), however, they do not have to be aware neither of how the behavior spreads over the map nor of what is their contribution to that.

A special meaning of institutions can be seen within the class of agents with fixed means and variable beliefs, which are being reasoned about here. Ontologically described, agents within this type of model are carrying a predisposition towards a certain order. In the case of Hodgson and Knudsen (2004) this could denote a goal of living in an ordered world with unity on traffic direction. Schelling (1971) works with an order constrained by primary attributes such as culture or race. This homogenous predetermination behind the goals of these agents can be seen as a type of structure (institution) imposed within the design of the model.

There is however an important difference between the types of emergence in question. While not taking part in segregation is mostly about goals, not taking a part in driving on the correct side of the road can turn agents into dead agents. The decision about driving direction in Rules of the road is still done on the micro level, but while we (from above) watch the emerging institution of left-hand or righ-hand-drive, the agents who

52 4. Classifying emergent Social Institutions are taking the minority’s direction are getting more and more limited and if that does not push them to change the sides, the whole situation turns into exclusion of themselves as well as those who happen to drive towards them at that moment.

Weakly diachronic emergence can therefore be understood as an aggregation of choices. Its nature is not far from macro-level phenomena, together with missing line between the nature of the aggregate and behavior of the agents (who do not care whether they are a part of big or a small group as far as the behavior is the same within their vicinity).

Weak emergence as a tool of categorization

The property of institutions within the model being only a macro property could be serving as a bottleneck for determining which models shall be considered within the following section about top-down influence. The definition of such criterion relies on whether the weakly emergent pattern is the only time of emergence occuring within the model or if something happens at the places where agents reside. Such consideration shall be a matter of deciding on to which extent do the emergent phenomena affect those agents which were not a part of the emergence.

An agent who is segregated from his homogenous group does not necessarily have to know that he is in fact isolated. The author therefore believes that the phenomena with only macro emergence can not be considered and categorized from the point of view of top-down influence. Cases of strong emergence where macro phenomena are incorporated by micro phenomena will be considered furthermore.

4.3 Backward influence of Institutions towards Minds

So far it has been acknowledged that there are two types of emerging phenomena. First, institutions emergent into the minds of agents, which is the case of genetically selected agents with fixed minds and the dynamic agents. Second, emergent codes of behavior, which are subjective to volatility and result out of agent’s adaptation to the environment without inclusion into the mind. Emergence within some of the models goes along with macro features (spontaneous constellations of agents), which are observable from outside but hardly reducible to properties of the agents. This side of the interpretation accounts for the strong emergence, where the emergent properties can be assigned to the minds of their agents through a functional relationship. Different case is the weak emergence at above the level of agents, where the exact relation between minds and emergent phenomena becomes doubtful, especially when concerning such phenomena working as carriers of instutional powers in top-down relation.

The remaining part of the dichotomy between agents and institutions is the backward influence of institutions into the mind and behavior of the agent as well as her abilityto

53 4. Classifying emergent Social Institutions

Table 4.3: Categories of top-down influence

Risks Special feature Class (a matter of...) Death Existence Low outcomes Effectivity No Voluntarism Voluntaristic acceptance No Voluntarism, Embedded belief Opinion cognitivelly reason about what the institution is. What seem to matter from this point of view is whether (and how) can the emerged institution affect minds of agents within the environment. In the words of the concept; how do the institutions affect the reality of agents as it is decomposed into goals, means and valuation (beliefs).

Second, one might be wondering about how stable the adaptation actually is (aka what can the agent do apart from obeying). This last question tackles the topic of agents’ ability for self-realisation8. Reasoning for the topic of self-realisation aka voluntarism is related to the initially opened topic of institutions imprinted within minds, as the free realization of agents can be seen as an inverse of the extent to which the agent is determined by exogenous forces. The mentioned forces can, within the present context, be associated with two influences. First, there are terminal goals embedded by the programmer who made the model. Second, there are emergent features such as institutions which can also impose some influence over the agents (which is the topic of this section).

The outlined topics also relate to the level of agent’s consciousness about the institution and their ability to critically reason about it respectively.

The question of what happens to the agents who have not internalized the institution (into their behavior or also their minds) is used as a proxy for finding out how restrictive is the emerged institution towards the agents and perhaps even also how they could understand it. The sub-topic for exploration is whether there is a backward path from internalization (aka whether there are some features of agents’ revolt together with the institution).

A matter of existence

Some of the agents face a long-run risk of extinction. This mechanism is concerned with the function of genetic algorithms, who account for a continuous existential threat towards those on the lower outcome levels. The whole mechanism of evolution is connected by premise that the strong are those who survive. In the case of models with GA this means, that there is certain attitude (mean) which is concerned to be a prerequisite for existence within the certain society. A view on what was happening to people holding some extreme positions throughout the history does not offer only a parallel of death.

8. As far as an object within virtual simulation can be designated as being capable of independent action.

54 4. Classifying emergent Social Institutions

People who were convicted of major crimes or expressed psychological disorders could had also been banished or socially understood as crazies or fools. Societies of the current times are (especially in the western world) a bit more respectful towards mental disorders, however it is still possible to identify some existential needs in terms of behavior. Among such needs can belong a respect (and identification) with basic values on which the societies stand and which can basically supplement some of the formal institutions.

Models of rules of the road picture a parallel of a social institution, respect towards which is backed up by death of marginalized agents much sooner than at the moment of being genetically selected out. The model picturing humanity being basically in war with vampires is, along with rules of the road, another case of close-to-diffusion scenario, where a speed of adaptation can be a line between life and death (unless one wants to become a vampire).

The symbolic inclusion of death, through which the situations within these models can be understood as a matter of existence, is particularly interesting when being inspected for whether all motives behind the featured class of models can actually be understood as a matter of life and death. An example for such consideration can be for instance the model of hirearchy emergence. Within this model the agents can be outperformed on the basis such as unability to be lead or unability to convince others. A plausible interpretation is, that one shall be able to work in a group in order to survive. The context of the model, motivated by a society on a lonely island, can indeed allow for admitting, that once a group happen to become (for instance) castaways of boat-sinking, it is useful if everyone reasonably participates on their attempts to build infrastructure of their new homeland. Furthermore, the lack of respect to the lead can in this case also be understood as a proxy for despair of ever being saved from the island. On the other hand, it is questionable whether other plausible approaches of picturing the birth of a group would not picture the reality of the island better. Among those could have been for instance some of the dynamic mind techniques, such as observance of levels of cooperation expressed by the others.

The ways of agents’ interventions inside the institution are somehow limited. Agents with fixed means with certainty, who create a majority of types of agents mentioned in this section, are being born with means, which turned out to be efficient within the previous generation9. This is being taken care of by the embedded scaling function, which makes a division of traits according to the measures of success. There is no chance for changes in their minds apart from evolutionary selection and they do not think about using different mean than the one they were born with. For instance, once there is a selection of practices resulting into agents being selected out based on the means they use (such as levels of boldness and vengefulness in metanorms game), their successors are faced with world where a certain code of behavior is underrepresented or missing. The agents within the new setting are therefore predisposed to having some behavioral pattern.

9. Possibly slightly differentiated by the effect of noise.

55 4. Classifying emergent Social Institutions

The parameter of habituation within the model of Hodgson and Knudsen (2004) is actually making the agents even more embedded within the trait they are a part of. Once an agent happens to be driving on one of the sides for some time, the assurance she gets with suceeding to drive on the very side for some time makes her not refrain even though there would be a situation in which it would (objectively) be an appropriate thing to do.

A matter of effectivity

The moment when agent loses wealth by getting very low payoff can be in the real world also pictured as an existential risk. The model of Adam Smith problem pictures this type of situation while pointing out a belief, that holding moral positions (such as not trading with people of way too different behavior such as rednecks) can be connected with threat to one’s outcomes if being exaggerated. In fact, majority of the studied models deal with situations, where unacceptance of institution is (symbolically) not a matter of existence, but only a matter of effectivity. Not living together with people of the same race can for some people be frustrating, but usually (especially in lawful states) does not create a risk towards one’s existence. A similar parallel can be made towards curing patients by certain medicaments or making a good name of oneself by being generous.

There are some situations within this class which are allowing for institution-independent actions of the agents.

An interesting input of this sort is the application of independent learning (Mahmoud et al., 2017). This feature gives the agents a possibility of improving their performance (in Metanorms Game) regardless of the instrumentality of so-far utilized means, and therefore to test borders of their own possible behavior10. Such scheme keeps both the agents’ terminal goal (maximization of profit) and derived goal (use the means which have scored well), but gives them a chance to manipulate beliefs (impose critical reasoning over the mean they happen to utilize) without an external impulse of experience from interaction or cognitivelly-obtained finding from their vicinity (such as information about certain parameters of the means used by others).

A matter of voluntaristic acceptance

Attempts to work with agents being able to advice each other (Verhagen, 2001; Savarimuthu et al., 2007) are bringing some kind of endogenous supervised learning. The possibility of associating this feature with the topic of voluntarism (and not for instance with information spread, which is more related to emergence or properties of the environment),

10. As is also discussed within section 3.3, the consequential flow of events for a network with low connectivity is that the outliers start to profit from defecting and when the hubs start to punish them, the score of the hubs decreases by the price of punishment. In the end, boldness overrides the norm as the agents are not vengefull anymore.

56 4. Classifying emergent Social Institutions however, depends on whether the advised information is taken no matter what (and the advisor serves just as a new edge with a special type of relationship regarding information exchange) or the agent takes it through some kind of evaluation. Such evaluation can be seen for instance within Ultimatum game of Savarimuthu et al. (2007), where the agents are carrying a parameter of Autonomy, denoting how social they are (both in terms of providing the advice and taking it).

Ceasing to play no matter the goal fulfilment can be seen as another possibly voluntaristic feature. The form, in which this ability is given to agents with fixed mean and variable belief is relevant to the fact that this type of models is oriented on binary state of overall goal (un)fulfilment, so ceasing action is just a consequence of deterministic actions’ sequence. The situation within the model of Macy and Skvoretz (1998) (PD with dynamic mean) is different - agents within the model actually decide whether to interact with othersor not. Their reasoning about whether to interact or not depends on various trust-related information about both themselves and the counterparty (the decision tree is shown and described in Figure 4.3). This model shows an interesting innovative feature for both mind profiling and voluntaristic action. Nowak et al. (2000) take the idea of considering the nature of others even a bit further, as they work with a feature of agent’s general reputation being spread within the environment. This information, however, has no influence towards decision of the others whether to play, but only helps them tochange their means (Ultimatum Game division suggestions) to target the specific player (as described within section 3.3).

A matter of opinion

Within some of the models the effectivity does not really have to matter. Not seeing football players shaking hands at the end of the game as everyone around is standing despite the performance being poor, not believing into emperor actually being naked or driving a petrol car once everyone around believes in sustainable effects of electric cars, are situations in which one can feel unpleasent, but they normally do not express a risk towards one’s existence nor effectivity as they are mainly a matter of opinion.

An interesting fact about models of situations which can be understood as opinion- relevant is, that within minds of agents belonging to this group are parameters, which denote the level of their confidence towards the stance they hold. In the basic setting, this parameter works only as a bearer of subjectivity towards the subject matter and contrasts with the strength of social influence towards the agent. In its simple form it is symbolized by a constant, however a development over the time is also permissible. An example of the latter are the models of social opinion dynamics.

Out of the three this class is the least connected with negative impacts on the agents, who can carry on with their existence within the society without risks other than various kinds of opinion conflicts. The borderline between what shall be taken as an opinion and what is already a matter of effectivity, stays however thin. Situations within some of

57 4. Classifying emergent Social Institutions

Figure 4.3: Decision tree of agents within Prisonner’s Dilemma of Macy and Skvoretz (1998, 643), where the agents are considering trustworthiness of counterparts before playing with them. Agents are endowed with a genome (a string of 15 bits) predisposing them for certain actions. These range from general attitude towards trusting others (Unconditional rule) through external indicators of trustworthiness (greeting others, openess, responsiveness to actions of others etc.) to actual strategy of (not) defecting. Depending on agent’s general trustfulness towards others, they can start to evaluate the partner based on predicting her own intentions into them (Projecting own intentions) or by taking into account externally-provided signs of their trustworthiness as mentioned (Reading Telltale Signs) and based on that decide whether to play or not.

58 4. Classifying emergent Social Institutions the models can actually be pictured as those where the problem matter is originally a matter of opinion, which eventually turns into the matter of effectivity. This could be seen on an example of diffusion/threshold models unconcerned with the topic of death, where the agents hold some initial stance (belief) towards the problem matter (usage of certain technology), which in path-dependence type of problems turns into the problem of effectivity once a significant portion of agents decides to adoptit.

4.4 Overview

Two dimensions for classification have been mentioned over the course of this section. While the first one covers the objective side of institution-mind influence, the secondone steps aside and looks at chances of agents for self-realisation outside of being influenced.

The first of the topics was concerned with the ways how social institutions emergeout of agents’ actions. Two classes have been outlined in this direction. The first one targets emergence of institutions out of minds, which are subsequently spread once embeded in the minds of other agents and expressed in behavior.

The second of the considered options was emergence out of behavior, which emphasized that not thinking the same does not necessarily mean not doing the same together with mentioning this type of emergence to be connected with agents of flexible beliefs and fixed minds. This category of agents is concerned with binary choices for which their single mean constitutes a tool of change. External influences can give these agents belief that instrumentality of the mean of change is high, after which they switch to different option. They, however, do not really have an opinion towards what shall they be doing apart from whether it is better to change it and so are vulnerable to current happenings around them.

The middle section of the chapter has gone through various classes of emergence with a main argument of certain macroscopic phenomena not being decomposable back to minds of the agents. This property is relevant for models where the overall situation of institution emergence throughout the environment does not make influence towards situations of the local agents.

The last topic was top-down influence, concerned with the cases of agents influenced by the (in-environment) emergent institution. Reasoning was revolving around position of a hypothetical marginalized agent, who does not internalize the institution (either in behavior or mind) but still is functioning within the environment where the emergence is a case for majority of the agents. By contrasting the position of this agent with such situation it has been identified, that not internalizing the institution can for that agent be considered a threat to existence, lack of effectivity or a matter of opinion clashes.

59 4. Classifying emergent Social Institutions

Some of the agents are also able to reason about employment of alternative means even though they have internalized an institution. The meaning of institutions for this group is a matter of voluntaristic acceptance.

60 5 Dichotomy of Mind and Institutions in ABM

„If you don’t know how you grew it, you didn’t explain it.“ Marchionni and Ylikoski (2013, 330)

– Michael W. Macy & Andreas Flache, 2009

This section puts together the whole concept as has been discussed and shaped on preceeding pages of this thesis. The concept of transformational nature of social reality is utilized as a scheme for conceptualizing the flow ongoing within these concepts. As will be shown, the ordering of events by Transformational models differs throughout the concepts.

Through the assesment it has been found that there are five types of initial agent’s positions involving two possible cases of emergence and four cases of relationship from above. As the agents served as a unit of analysis throughout most of the paper, definitions of the social reality they occupy is now made from their points of view.

5.1 TMSA and ABMs with emergence

The idea of understanding the social reality from the position of Transformational models of Social Activity has been mentioned repeatedly throughout the paper. The present section aims to provide an overview of the studied cases from the perspective of some of the positions from within this debate.

Participants of the debate have raised a number of discussion points towards how exactly can one think about properties and functions of social institutions. This idea as well as the circular nature of the model were used for describing relations within the models. Identified cases of agent-institution dichotomies are therefore portrayed in a sequence denoting all parts of TMSA (as shown in Figure 1.1) in order to discuss potential matching of their thoughts with specific approaches to model emergence within ABMs.

Positions within the debate

As noted in Weiner (2015), the ontological background of structure-agency flow within TMSA debate starts from three positions. The most deterministic one (Berger and Luck- mann, 1999) expects the structure to preexist the agents. The middle case concerns a flow of influence throughout agent’s socialization. The most voluntaristic position of Harré (2009, 140) emphasizes the situation of agent by denoting practice as „an activity that is

61 5. Dichotomy of Mind and Institutions in ABM

directed to an end that is meaningful to the actors, and managed by symbolic means and unfold according to certain local standards of correctness. The most important practices constitutive of the social world are symbolic interactions, in which the intentions of one actor are matched by the interpretations of the interactor(s).“ This argument of Harré is a confrontation between the previous positions and an individualistic point of view. Understanding the role of practice and its relation with understanding of agents in Harré’s concept is close to the case of prevailent behavior emergence, as presented in the previous chapter.

Participants of the debate offer a series of opinions on what exactly emergent structure means to agents (Weiner, 2015). Among the sounding ideas are those of institutions functioning as a guide for behavior, which is used voluntarily (Harré, 2009) or promoted by general requirement of the environment one occupies (Berger and Luckmann, 1999). Another meaning points at it as a genetic constant, which stands outside of agents and serves as a building blocks for their actions(Bhaskar, 1998). Bhaskar (1998) also uses a parallel with a generally existing collection of behavioral codes, which can by interpretations of some authors (Lawson, 2003; Martins, 2007, 2011) be a subject of evolutionary selection. Lawson (2003) also uses an interpretation of institution as a preexistent feature, which can condition the actions while simultaneously depending on them.

All of the concepts can be somehow integrated into the transformational structure. What could differ is the completeness of the whole circle of influences (when speaking both about agents and practices). The current reasoning will therefore at some points step out of a condition of institutions with influential powers actually being emerged within the model in question.

Evolutionary selection

The theme of the evolutionary selection has been mentioned repeatedly in relation towards agents with fixed means and certainty which are actually being selected by evolutionary-like principles.

The evolutionary-related contributions within Transformational Model debate include those of Lawson (2003) and Martins (2007). The darwinistic version of the model, developed by Martins (2007), is denoted as PVRS.

The full name of this concept standsfor its limits towards the genetic selection process. Specifically for the considered case, this means (Weiner, 2015) the existence of aspecific Population (agents facing a terminal goal of maximalization) which possesses a Variety (different means to asses the goal) on which behalf they are Selected (scaling function) for Reproduction after which the observed variety changes. The last condition is a causation from the level of environment towards the finally observed variety, which can be assesed by the relation between initial conditions of the system and the results it

62 5. Dichotomy of Mind and Institutions in ABM produces. According to Martins (2007) the fullfilment of these conditions is instrumental for the model being usable within particular cases.

To wrap the idea, agents with fixed mean and certainty possess a single mean towards the goal of payoff maximalization. Their possible actions include only repeated utilization of the mean in interactions with other agents, final choice of their actions can however be influenced by the effect of noise. Success of the agents towards fulfilment ofthegoal is measured by cummulative payoff, serving as an input into scaling function - a method of evolutionary selection1. Succeeding generations of agents are influenced by in-mind institution in the form of mean.

Ex-ante predetermination

Agents with fixed mean and variable beliefs are born with institutional predetermination towards desirable state of the world, which serves as their goal. Goals are in this case concerned with parameters of the environment which the agent occupies, be it defined spatially or through reaching of goal-related general agreement. Their belief is shaped by current state of goal fulfilment. These agents do not internalize institution into their minds, but only into their behavior, represented as staying idle as a consequence of state-changing mean being no longer instrumential once agent’s goal is fulfiled.

At this point the model is in an equilibrium. Imposure of big enough group of radicals is able to turn some of the equilibriums into a cascade effect, which is a spread of revolutionary influence from local cluster throughout the environment. If society reaches the equilibrium macro-level phenomena might emerge. This type of phenomena are examples of weak emergence, which is unobservable from within the system. Agents are able to recognize this effect on the Micro level. Macro-level emergent phenomena in their completeness are however particularly difficult to deduce.

A relation of this type of transformation can be made towards Bhaskar’s (1998) idea of agents predetermined by structure from the time of their birth, which is connected with impossibility of agents creating a bottom-up influence towards the structure. This finding is consistent with the one made within chapter 4 on emergence, wherethe coalitions of this type of agents became purely a behavioral pattern.

Independent recreation

Agents with dynamically changing minds are shaping a reality of practices defined within multi-dimensional space. Their interrelations with the other agents include ability to reason out of information obtained from various sources and to understand their own position within the system. Institution emerges out of their minds once there is a coalition

1. Understandable either as direct (selected are those agents which are succesfull) or indirect (what the models actually do is selecting the succesfull means for further replication within the society. 63 5. Dichotomy of Mind and Institutions in ABM on the prevailent mindset. Backward influence of the institution reaches the information base in which the agents shape their beliefs and consequently affects the cognitively- identified scope of possibilities.

The dynamic agents can face pressure out of various directions. The powers affecting their actions can come from their peers (such as leader of a group in Verhagen’s model providing a feedback or advisor in the models of Savarimuthu) as well as from general space they occupy. Furthermore, institutions within this concept can take various shapes given mutlidimensional space where some of the means reside. This type of interpretation can be found in a definition of institution as made by Lawson (2003).

The classes of dynamic minds are symptomatic by being able to work with the mean they utilize.

Interaction

Agents with social base of means utilize a pre-existing structure in a form of a social base of meanings which they are aware of. This type of structure symbolizes a toolbox, components of which the agent can try to solve the tasks she faces. Possibly, emerging institution take a form of coalition of agents on the most instrumental mean towards the goal and hence reside inside the minds of agents. Agents have no power to influence the institutions other than shaping common beliefs about instrumentality.

Agents are in this case aware of the fact that such a base exists and what they can choose. Structure can therefore be denoted as some kind of set, including permissible behavioral practices. As they can utilize and rank all of the means, we can assume that they also know about their existence. The individualistic concept of Harré (2009) utilizes the idea of this type of structure.

5.2 An overview of the classification

This section wraps up the whole classification from the point of technical conditions. This wrap-up is (in comparison with the preceeding one) build from mostly technical standpoint in order to reflect which techniques can the researchers use to reflect some types of institutions.

A particular aim of the section is to outline some yet unresolved issues for detailed future elaboration.

As the first point within the conceptualization, a system of predicative analysis ofmind was presented and consequently adapted to specific cases of phenomena emerging out of minds of (more or less) independently acting agents within Agent Based Models. The distinct cases of models with emerging norms were translated into language of

64 5. Dichotomy of Mind and Institutions in ABM the scheme and accordingly grouped. A key precaution for emerging institutions as defined by Castelfranchi is the one of taking it as a mindset. The chapter concerned with Institutions revealed, that this type of reasoning is compactible with agents of fixed mindsets (to which the institutions are encoded by direct genetical selection) or those which can dynamically change means. In the latter case, the institution becomes a mere belief, as with agents with variable beliefs.

This type of setting is, however, sometimes connected with emergence of macro phenomena, explainable from holistic point of view. Equilibria of these models or their parts are moments with low beliefs into instrumentality of means to change position within the system or a state of mind. A special case of phenomena is a cascade, which is a spreading change of states determined by a group of agents with radical belief and considerable headcount (critical value).

The chapter on Institutions has also gone through top-down influence of institutions towards the minds, which can impose various limitations on the agents as well as give them some level of freedom.

Ability of agents to recognize the emerging phenomena at the macro level is conceptually hard to solve, as is the neediness of agents to be aware of institution being implemented into their minds (cognitive emergence).

The overall results of model categorization can be seen within Table 5.1.

65 5. Dichotomy of Mind and Institutions in ABM

Family Citkey Agent_typea Unity_in Macro_considerationb Topdown_type_matter Cascade Miller and Page (2004) FM-VB Behavior Yes Opinion Cascade Centola et al. (2005) FM-VB Behavior Yes Opinion Establishment of hirearchy Perret et al. (2016) FM-C Mind Existence Metanorms game Axelrod (1997) FM-C Mind Existence Metanorms game Mahmoud et al. (2012) VM-MM Mind Voluntaristic acceptance Metanorms game Mahmoud et al. (2017) VM-MM Mind Voluntaristic acceptance Minority Game Arthur (1994) VM-SB Mind Effectivity Money Marimon et al. (1989) VM-SB Mind Effectivity Money Moran et al. (2013) VM-SB Mind Effectivity Money Gangotena (2016) FM-VB Behavior Opinion Opinion dynamics Duggins (2017) FM-VB Behavior Opinion Opinion dynamics Pilditch (2017) FM-VB Behavior Opinion PD Lindgren (1997) VM-DCM Mind Effectivity PD Macy and Skvoretz (1998) VM-DCM Mind Voluntaristic acceptance Segregation Schelling (1971) FM-VB Behavior Yes X Social Evolution Model Eshel et al. (2000) FM-VB Behavior Yes X Sugarscape Epstein and Axtell (1996) FM-VB Behavior Yes X Sugarscape Verhagen (2001) VM-MM Mind Effectivity Sugarscape-ASP Gavin (2018) VM-MM Mind Effectivity Tag-based cooperation Riolo et al. (2001) FM-C Mind Existence Treshold-Diffusion Shoham and Tennenholtz (1992b) VM-DCM Mind Effectivity Treshold-Diffusion Kittock (1994) VM-SB Mind Yes Effectivity Treshold-Diffusion Burke et al. (2006) FM-VB Behavior Yes Effectivity Treshold-Diffusion Kangur et al. (2017) VM-SB Mind Effectivity Treshold-Diffusion Haurand and Stummer (2018) VM-MM Mind Effectivity Treshold-Road Epstein (2001) FM-VB Behavior Yes Effectivityc Treshold-Road Hodgson and Knudsen (2004) FM-VB Behavior Yes Existence Trust game Bicchieri et al. (2004) FM-C Mind Existence Ultimatum Game Nowak et al. (2000) VM-DCM Mind Voluntaristic acceptance Ultimatum Game Savarimuthu et al. (2007) VM-MM Mind Yes Voluntaristic acceptance Ultimatum Game Savarimuthu et al. (2009) VM-MM Mind Yes Voluntaristic acceptance

Table 5.1: Overview of discussed models and their respective classes a. FM: Fixed mean, VM: Variable mind, C: Certainty, VB: Variable belief, DCM: Dynamically changing mind, MM: Multiple means b. Stands for actual consideration of macro emergence within the original paper. c. Althought this is a case of rules of the road model. agents are actually not dying.

Edges between some of the classes are quite loose - for instance the category of agent mind who utilize the social base of means differs from those with dynamically changing mind mostly in the nature of approaching the means. While the former class works with a given set, the latter changes their means in a continuum (and consequently carries some enhancements, such as improved learning).

While this difference might seem not to carry too much importance, some use mightbe seen at least in teleology. While the former class is good for testing out a set of means, the latter is interesting for changing their parameters by endowing the agents with advanced techniques of learning.

Genetic Algorithms seem to work as a technique, which carries a lot of institutionally- related meaning. The sign of this evolutionary technique’s utilisation have been therefore able to group all models which utilized this technique on a single pile. Interesting

66 5. Dichotomy of Mind and Institutions in ABM parallels come up when the class of agents selected genetically combined with the cases of agents who are in their models facing the risk of momentual death into the reasoning on institution serving as a matter of existence when assesed within top-down relationship.

Agent in the contrast of Social Institution

Within the attempt to frame the concept of Mind (section 3.1) Castelfranchi (2000) introduces a concept of Socially Autonomous Agent, who creatively works with a number of goals and is able to prioritize them.

The idea of conflicting goals has been introduced within the model of Adam Smith problem (Gavin, 2018). Agents within the model are unfortunately not possesing enough advanced mental powers to be capable of actually undertaking the decision2 and as a result of that end up behaving accordingly to which extreme is momentarilly permissable by the modeler.

The basic (non-terminal) type of goals then stand for agenda, towards which the agents aim their means and which are in a functional relationship with the terminal goals. Giving agents a power to intervene with them can be identified as allowing them to decide what do they want to do, allowing them to have some free will or act voluntarilly.

The closest position to the one of conflicting goals is the concept of sub-goals (obtain advice) functioning as a sub-stages towards the main goal (earn as much as possible) which can be seen on the case of the group of agents who utilize multiple means. A good specific example of voluntarism-supporting property within this category is independent learning, which symbolically keeps agents’ eyes open towards prospectual undertakings outside of what they normally believe in (institution).

Such exploration is a part of evaluation also within the group of social base of means. However, despite these agents being able to choose any of these means during the process of changing means and their evaluation, selection of other mean than the best performing one is not allowed.

Generally, the idea of giving the agents power to look at a variety of goals is interesting way of how to move the models a bit more towards the reality (being it desirable or not, see reasoning within the following chapter). But does that brings us closer to voluntarism?

Within the particular example of models by Savarimuthu et al. (2007; 2009) the sub-goals are in fact serving for even greater convergence. Savarimuthu et al. presume, that there is an optimal value which the institution of fair division should hold and therefore the

2. The reason is that as the model is designed for just and only (dis)prooving compactibility of Smith’s thoughts, agents behavior has been pretty much similar to what have Smith defined in the 18th century.

67 5. Dichotomy of Mind and Institutions in ABM function of a succesfull advice in fact only pushes the agents incrementally towards the target value.

Another way of understanding the dichotomy between the agency and the structure within the studied models can be targeted towards influence of emerged institutions towards the goals.

Such reasoning can be related to effectivity as a possible terminal goal of the agents. An example of agents with institution being a matter of effectivity can be those which in path-dependence-like type of situations face expensiveness of not being a part of the majority.

However, the problem with the way how terminal goals are functioning, is that as the models (including the one on Moral Markets) are aiming towards benefiting those agents who outcompete the others through objectively measurable outcome, not adapting such a convention is expensive towards the fitness and therefore objectively a point for being selected out.

Naturally, the so-called voluntarism from within the previous chapters shall not be taken too seriously. The agents are (as well as any other objects within the field of Artificial Intelligence) still capable only of doing what they were programmed for, which mostly consists of comparing values and making a decision on their basis.

Other possibilities on further improvement

The next step after introduction of inductively built concept can apart from the mentioned widening naturally consists of its deductive critical validation by testing the observed rules against various models from outside of the sample.

Tabular overview of the classification shows a trend of models from within similar themes to fall within similar groups at various points within the concept. In the end, for the work on conceptualizing the phenomena have been mostly useful not baseline models of approaches, but those which carry some interesting specifics or a catchy story around the studied phenomena. If the next steps shall not necessarily be of deductive testing but rather of further specifying the observed patterns, then it might be useful to restrict the sample only to models of the mentioned types and look at them at the level of detail appropriate for observing even more overlaps between computational and ontological properties of the models.

68 5. Dichotomy of Mind and Institutions in ABM

Note on alternative classificatory approaches

To the best of author’s knowledge there is not an alternative classificatory scheme putting together Transformational Models of Social Activity and Agent-Based Models or using the symbolic system of Castelfranchi to build a classificatory system and use it.

However, it shall be noted that there are alternative concepts attempting to analyse mutual relationships of the agents and the institutions within ABMs with both emergent and a priori imposed institutions.

Althought the presented approach aims to be identifiable as original in terms of going through the whole process of agent-institution dichotomy together with considerations about socially-ontological relations behind the presented meanings, there are certain touchpoints between what has been described within the thesis and classifications made from variety of other views.

As there is already a huge and still growing range of models and modeling approaches incorporating the emergence of norms, secondary literature classifying them has grown accordingly. Following the points of view, which the classifications employ, there is a set of categories they fall in.

Existence of classifications which focus on the technical properties of the models ormeta approaches (Neumann, 2008; Beheshti, 2015), their origin (Sullivan and Haklay, 2000) or general discourse (Saam and Harrer, 1999; Neumann, 2010; Balke and Pereira, 2013; Gräbner and Elsner, 2015; Gräbner, 2016) have already been opened within chapter 2. The study of technical properties by Macy and Willer (2002) has been mentioned as an example of model’s technical decomposition (Figure 2.1) as well as a source of reasoning similar to the one presented within the section on emergence.

The most prominent example of possibly competing approach is a line of authors (Gilbert, 2004; Savarimuthu et al., 2007; Savarimuthu and Cranefield, 2009, 2011; Hollander and Wu, 2011a; Mahmoud et al., 2014), who attempt to depict the described phenomena as series of technically-described processes. Perhaps the most advanced version of the scheme proposed Mahmoud et al. (2014) is shown on Figure 5.1.

Savarimuthu and Cranefield (2009), who put a lot of effort into developing the concept, claim at it originally coming from Finnemore & Sikking. Savarimuthu and Cranefield (2011, 22) understand institutions as „expectations of an agent about the behaviour of other agents in the society.“ In their base version they contemplate the general use of norms in MAS and sort the methods of their uses to four groups (creation, spreading, enforcement, emergence). The circle starts with Creation, which denotes a moment when agents find out about the institution’s existence, e.g. by own cognition. Spreading covers the ways how agents learn about the institution, such as by evolutionary mechanisms or machine learning. Phase of Enforcement relates to punishment of non-compliant agents. This does not have to be driven by the external forces, but for instance also by the agent’s

69 5. Dichotomy of Mind and Institutions in ABM

Figure 5.1: Model process perception by Mahmoud et al. (2014, 18). The black arrows denote the order in which each of the phases follow each other, the blue ones distinct options for each of the phases. In the case of spontaneously emerging institutions the process is growing out of two branches under Norms adoption. The three types of spreading (originated by Savarimuthu and Hollander & Wu as noted by Mahmoud et al. 2014), denote spreading from parents to offspring (vertical), society leader to the followers (oblique) or from peer to peer (horizontal). When the norm is adopted, agents can decide whether to assimilate it (yes/no) which potentially leads to internalization.

emotional setting. The actual Emergence is for the purpose of the scheme only a flag, stating that expansion of the institution has been reached.

The mentioned alternative approach has a base in Computer Science and so its categorization is more focused on technical properties of utilized methods instead of ontological meanings as in the case of this thesis.

The current complexity of both this scheme and the mentioned sources of the potential comparisons creates a potential for building a solid comparation of differences between the two points of view3 outside the scope of this thesis.

3. Or many more, as is usual in Social Sciences.

70 5. Dichotomy of Mind and Institutions in ABM 5.3 Overview

This chapter went through Mind-Institution dichotomy as suggested by Transformational Model of Social Activity to see to which extent do the contributions fit its processes. Within this the whole scheme was reviewed and re-described through the whole mind- institution-mind relationship. It has been shown that the meanings of structures within the featured models to some way resemble stances within the debate.

The overview of models’ pertinance within the specified classes was then presented together with denoting a few general findings.

The classification has been built inductively on a sample of 31 models. It is reasonable to believe, that widening it by extending the sample or applying techniques of deeper functional analysis would lead to extended precision of classes’ definition. Likewise it would create opportunities for critique of the way in which the current classification is built. Such initiative is by all means welcome.

6 Assesing relevance of ABMs with emergent Social Insti- tutions

„The purpose of models is not to fit the data but to sharpen the questions.“

– Samuel Karlin, 1983

The preceeding three chapters have used a symbolic system to inductively derive meanings and categories from observed properties of agents within Agent-Based Models. It has been shown, that some patterns inside them indeed resemble phenomena within societies with endogenous social institutions, which was among the aims of this thesis. The yet unevaluated point of view targets a valid question of with what exactly can existence of such models contribute to social sciences.

The majority of models presented within the previous chapters seem to be developed mostly for the purpose of science - they aim at uncovering fundamental relations behind initial parameters, techniques used and the result. That does not mean that they are aimed to stay only within academic discussions - they capture essential parts of relationships together with associated reasoning and ontology and as such can be inserted into wider structures and function as its parts. This section leaves the focus of methodology and attempts to look at the phenomena from the practical standpoint in order to find out, whether the discovered classes have promising use in specific cases or their potential lays only in theoretical disputations.

The usual way of evaluating whether certain model does well in describing the phenomena it is supposed to describe is comparing the actually observed data with what the model is predicting. This approach at first does not sound very realistic for the ABMs presented so far, as there is overall quite some uncertainty about how the socials institutions evolve as well as about any good measurements to compare it with (as points out also Gavin 2018). It is therefore a bit unrealistic to believe that the featured models would serve as an ultimate simulation of society with emerging institutions. Firstly, each person within the society is likely involved in way more institution-related connections that what any model can capture (including Dutch cars by Kangur et al. 2017). Secondly, it is unlikely that human behavior will be perfectly predictable within the foreseeable future.

However, it is questionable whether precision of comparison with real data is the main purpose of model design.

73 6. Assesing relevance of ABMs with emergent Social Institutions

Shall models be simple or complex?

The idea of overall social reality as a difficult-to-predict phenomenon is somehow colliding with the presented suggetion of ABMs with emerging social institutions as a heuristic improving what we know about the world out of purely-NCE considerations. The question is, in which direction shall one actually head in terms of model simplicity vs. its complexity.

When speaking about the axiomatic relations suggested by models within NCEs microeconomics, it is not hard to say that (for instance) not many markets work under such isolation as the market of tea which Alfred Marshall studied in 19th century when working on partial equilibrium theory. However, as claims Finnish methodologist Uskali Mäki (2009), althought there is a wide set of reasons for criticizing the way how the mainstream theory is build (such as attempts for turning all phenomena into unified dimensions, serving simple answers to simple audience or flooding the discourse with too many stylized facts), its criticism should not be based on picturing its proponents as distracted from the reality or even blinded:

Economists build simple models because they believe the world is complex. They don’t build simple models because they believe the world is simple. They build models based on theoretical isolation because they believe this is the only or the best way to get access to the deeper causes of the phenomena in complex reality. [....] Assumptions serving to exclude factors that are irrelevant or negligible from the point of view of the purpose of inquiry are often formulated as deliberately false idealizations. So formulated, they appear to make false claims about the absence, constancy, or zero strength of a variable. But they can often also be transformed into claims about peoperties such as the negligibility of a factor, and as such claims, they are given a chance of being true (Musgrave 1981). (Mäki, 2009, 18-19)

So, in other words, the Economists, here specifically those who use the traditional equili- biral techniques, are only trying to simplify the research, which would otherwise [when treating Complexity by Complex means] be too complex to capture. Complexity occuring in the results, rather than in assumptions, as claimed by Axelrod (1997).

Numerous approaches towards model’s (and agents) complexity have been shown throughout the research. The model of Netherlands moving to market of electric cars (Kangur et al., 2017) shows a great degree of precision and according to authors can even fit general market trends. Agents within simple models such as rules of theroad of Epstein (2001) are, on the other hand, facing a very simple and also hypothetical setting, as rules of the road are usually decided on the level of formal institutions rather than social ones. Epstein and Axtell (1996) claim to be tempted to include very specific rules regarding e.g. rules for mating in original Sugarscape, however in the end they

74 6. Assesing relevance of ABMs with emergent Social Institutions purposefully decided to keep the rules as simple as possible, so in the end the agents can for instance mate with close relatives. Hodgson and Knudsen (2004, 5), the authors of extended rules of the road, say:

„Initially our objective was to make the drivers as “intelligent” as possible, subject to the constraint of a limited number of cognitive and behavioral variables. After numerous runs with additional cognitive parameters, we found that a highly parsimonious model was very effective. Given the relative simplicity of the decision environment, and the effectiveness of our “parsimonious” decision algorithm, it seemed neither necessary, appropriate nor fruitful in this model to incorporate more complex learning procedures such as the “elaboration likelihood model” of Petty and Ca- cioppo (1986) and the non-linear models of attitude change by Eiser et al. (2001). However, more complex learning algorithms would clearly be appropriate in decision environments involving more learning parameters and behavioral choices than are present in our model.“

The simple models, however, still give interesting results. We are able to find out, what kind of system properties can allow for emergence of leadership (Perret et al., 2016) or which indicators to watch if one happens to be interested in the spread of (fake) news (Centola et al., 2005; Pilditch, 2017). Perhaps the actual question shall be not whether to build simple or complex models but with which parameters shall one economize.

Raising the number of parameters and features within the model is from some point of view associable with taking responsibility for the development of the studied phenomena as well as the results it can evoke. Each definition of parameter, its development and the distribution from which it is sampled gives greater chances for design of the model to be questioned. Comments on designs and developments of models towards greater precision are surely a valid and welcome part of the academic debate. For some reason most of the credit is however in the end paid to models such as Segregation (Schelling, 1971) which has so few parameters that it can be created (and at first actually was) with just a few pennies and a checkboard.

It can be argued, that success of some models is partially due to a good sale potential developed through interesting pictures on Cellular Automata or by framing the whole model with some cool topic such as vampires or Anderson’s fables. What matters within the science are still the facts. One would likely have a hard time to find a settlement exactly described by Schelling’s Segregation, to visit a stadium with people standing right according to Miller’s SOP or to enter El Farol Bar and enjoy the evening together with no more than 60 other people even though there is a National Holiday on a following day.

The main asset of these models lays in their focus on a small portion of reality which still holds enough information to help us understand how some processes work in general without overrating the role of precision. Generalization of such findings can in

75 6. Assesing relevance of ABMs with emergent Social Institutions

fact take us closer to the reality than many of the advanced ones. Even the follow-up reasoning needed for applying the uncovered regularities to a specific case then does not necessarily have to be a reason for increasing complexity, but perhaps for some alternative explanations including linguistical models of OIE.

On potentials for OIE

The ongoing agency-structure debate between NCE and OIE can offer antagonistic positions in which people either factically reason about everything with an unreal ability to oversee the consequences and do not hesitate to commit crimes when not being seen or the one of a total determinism, in which a freedom of decision is out of question, as we all are just passengers in a tow of social institutions without a chance for ever affecting our own choices or the institutions itselves.

The forthcoming text has featured numerous real-world situations within which reasoning of the individuals (regardless of whether determined or voluntaristic) and social institutions together share an explanatory power towards what happens. Virtual agents who discuss politics-related topics, perform barter exchange or look for locally-based practices are not hard to be explained to anyone and can be used as a theme for an experiment. The same agents and situations can likewise serve as an explanation for the ways in which the agent’s minds and behaviors (together with consideration of their displacement, social powers etc.) help contribute to emergence of the wholes which then impose a backward restriction towards their constituents.

What kind of better means of publicity can OIE actually aim for?

Motivations of Old Institutional Economists behind diffusion of ACE with emergent Social Institutions in fact does not have to stop by modeling the institutions themselves. It would be by far enough if the general patterns of emerging and restricting institutions would spread throughout the overall discourse of modeling (to some extent) intelligent systems.

The featured approaches to emergence can in fact help not only with creation of social institutions resembling simulation, but also with keeping logical consistency within the modeled system. Thus, if overseeing what is happening within certain heterodox system is needed, the presented heuristic keeps control of system development in between iterations, which is largely not possible by just generating data from given distributions. The major value of the approach is therefore not in precision of the result, but in keeping control over the rules of its dynamics.

76 6. Assesing relevance of ABMs with emergent Social Institutions

Focus on parallels

Some models are looking at very up-to-date societal problems. For instance looking for how many radicals are enough critical mass for turning own radical beliefs into a cascade (Eshel et al., 2000; Miller and Page, 2004; Centola et al., 2005) is among nice parallels to cases in which prevailing institutions within the society (disbelief to fake news) are questioned by one or more stakeholders of radically opposing belief.

Challenges connected with these types of phenomena are much beyond ABMs. Once current societies face consequences of many people’s vulnerability towards becoming potential sounding boards for fake news, it is desirable to look at appropriate reactions towards such trend.

ABMs alone are not solutions to problems. They however, seem to be an interesting heuristic for testing various scenarios within complex environment and debating about influences between changes in the design and the final output.

The inclusion of emergent social institutions gives the model ability to flexibly react to what has happened in the previous runs by keeping in mind, that actual people can be partly or entirely shaped by what they have gone through (both in terms of environment and relations). Additionally, using techniques of emergence to build institutions within the model can in fact be easier than imposing them externally, as endogenous features within the models take care of mind-institution relationships within the models.

Human-Machine interactions

Use of such features can go beyond simulations of societies done for the purpose of humans. Leaving the premise of model’s use as a research tool (being it in academia or in business) can allow us to think about deploying models of human-machine interaction, where a well-informed learning algorithm within the computer can also have some of the mentioned principles in mind.

Author’s particular experience relates to functioning of Instagram, where accounts of some of the influencers (such as states, as is shown within printscreen - Figure 6.1) follow random users1 and unfollow them soon after. A possible motivation interpreted through principles shown within this thesis can be looking for reciprocial agents, who based on being followed act accordingly to tit-for-tat and begin to follow the commencing party. The initiator then unfollows which makes this approach (mean) instrumental towards possible goal of raising her followers to followings ratio (apart from spreading her cool pictures to greater group of people in the future and growing a chance to spread a cascade once some of the contributions turn viral).

1. Or ask for being allowed to follow if one’s account is private.

77 6. Assesing relevance of ABMs with emergent Social Institutions

Figure 6.1: Utilization of Tit for tat in practice?

Some of this thesis’ sources (Kittock, 1994) have already been considering ABMs (or more likely MAS) to actually consist of robots. Such examples do not have to be concerned only with algorithmic trading, but also with much more tangible features such as cars. Such consideration is not new, for instance Stone and Veloso (2000, 1) were discussing it in relation to rules of the road models almost 20 years ago, „if there is one self-steering car, there will surely be more. And although each may be able to drive individually, if several autonomous vehicles meet on the highway, we must know how their behaviors interact.“

Althought thinking about sitting in a car which actually aims to decide on which side of the road to drive sounds rather brutal, it is yet another example of multi-agent interactions likely having certain place both in the contemporary and future technologies.

Overview

The chapter featured author’s essay on relevance of ABM within the program of Eco- nomics. The topics which were discussed have by some proportion overlapped with some of the opened discussion points within chapter 1.

The first part of the considerations was revolving mostly about Methodological topics. Importance of complexity has been mentioned as a potentially important factor for model’s overall usability together with noting that some of the most famous models are actually utilizing very simple designs. The attention was then moved towards possibilities of using ABMs with emergent social institutions within the discourse of Old Institutional Economics.

Focus on OIE was then used as a point of departure for more teleologically-grounded ideas related to usage of emergent features within both theoretical social sciences and its embedding in technologies.

78 7 Conclusion

„Everything will be okay in the end. If it is not okay, it is not the end.“

– John Lennon

ABMs with emergent social institutions as presented on the pages of the thesis seem, according to the observed matching with Transformational Models of Social Activity, as not only an interesting playground of situations dealings with vampires, colliding cars and naked emperors, but also as a plausible tool for actually simulating the interrelations between agents and social institutions.

Voluntarism was in the beginning noted as one of the motivations towards exploring ways to widen holistic explanations of Transformational Models. Techniques which indeed allow the agents to act in other ways than what institutions suggest (even without not being outcompeted) have been identified as either cases of institution being only an opinion which do not necessarily limit agents confident within their belief, or cases of empowering agents with a possibility to explore means beyond the prescribed limits.

Mechanisms connecting ontological meanings and their computational expressions as defined within the presented schemes can apart from being used for classifying wider ranges of models serve also as prescriptions for designing environments functioning accordingly with types of institutional influence similar to those which were described.

The aim of the thesis was to classify the different ways institutions are modeled using the agent- based computational economics approach, while focusing on the Mind-Institution dichotomy. Moreover, mutual relations of the different approaches will be studied, and their relevance will be assessed.

Mind-Institution dichotomy was covered within three chapters, which went through the topic from the moment of picturing the positions of agents’ minds towards dealing with phenomena going on around them through emergence of institutions to their backward influence on the agents. The mechanism of the flow has been inspiredby Transformational Models of Social Activity, to which the findings have been compared within the final chapter of the classifications. Mutual relations of the approaches have been shown at various stages throughout the classification. Likewise relevance of the approach was discussed in many contexts throughout the whole text. Some of the notable findings have been explored in more depth within the essay in chapter 6 together with considerations of their empirical applicability. Author thus considers the aim of the thesis to be fulfilled.

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List of Tables

2.1 Santa Fe institute’s fellow Brian Arthur thinking about a new, complex, attitude on economics (Waldrop, 1993, 37-38). 10 2.2 Comparison of approaches by Miller and Page (2007, 79). 11 3.1 Categories of agents based on properties of their minds, the size of set of possible means which can one agent utilize is growing from top to down. 32 4.1 Categories of emergent institutions 45 4.2 Irreductibility and novelty 48 4.3 Categories of top-down influence 54 5.1 Overview of discussed models and their respective classes 66

List of Figures

1.1 Bhaskar’s Transformational Model of Social Activity 4 2.1 First, second and third level of conceptualization by Macy and Willer (2002). 12 2.2 Some of the environments used within ABMs with emerging social institutions 14 2.3 Metanorms game (Axelrod, 1997, 53) 16 3.1 Rules of the road - the case of thoughtlessness 35 3.2 Complexity of routines within a simulation period of vampire economics of Haurand and Stummer. 40 4.1 A very small groups of isolated radicals are not enough to spread a cascade (Naked emperor model on Voronoi’s diagram, Centola et al. 2005, 1032) 44 4.2 Schelling’s segregational model with various tresholds 49 4.3 Decision tree of agents possible to stop playing within Prissoner’s Dilemma of Macy and Skvoretz. 58 5.1 Model process scheme by Mahmoud 70 6.1 Utilization of Tit for tat in practice? 78