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47 articles matched your search for the keywords:
Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment

Social Order in Artificial Worlds

Michael Macy
Journal of Artificial Societies and Social Simulation 1 (1) 4

Kyeywords: Cooperation, Evolutionary Models, Artificial Agents, Altruism
Abstract: How does social order emerge among autonomous but interdependent agents? The expectation of future interaction may explain cooperation based on rational foresight, but the "shadow of the future" offers little leverage on the problem of social order in "everyday life" -- the habits of association that generate unthinking compliance with social norms. Everyday cooperation emerges not from the shadow of the future but from the lessons of the past. Rule-based evolutionary models are a promising way to formalize this process. These models may provide new insights into emergent social order -- not only prudent reciprocity, but also expressive and ritual self-sacrifice for the welfare of close cultural relatives.

Through the Minds of the Agents

Cristiano Castelfranchi
Journal of Artificial Societies and Social Simulation 1 (1) 5

Kyeywords: Cooperation, Altruism, Rationality, Cognition
Abstract: This is a response to Michael Macy's contribution to the JASSS Forum, Social Order in Artificial Worlds.

When One Decides for Many: the Effect of Delegation Methods on Cooperation in Simulated Inter-Group Conflicts

Ramzi Suleiman and Ilan Fischer
Journal of Artificial Societies and Social Simulation 3 (4) 1

Kyeywords: Prisoner's Dilemma, Intergroup Conflict, Evolution of Cooperation, Social Influence, Representation, Elections Frequency
Abstract: The study explores the evolution of decision strategies and the emergence of cooperation in simulated societies. In the context of an inter-group conflict, we simulate three different institutions for the aggregation of attitudes. We assume that: (a) the conflict can be modeled as an iterated Prisoner's Dilemma played by two decision makers, each representing her group for a fixed duration; (b) the performance of each group's representative influences her group members and, consequently, her prospects to be reelected. Our main objectives are: (1) to investigate the effects of three power-delegation mechanisms: Random Representation, Mean Representation, and Minimal Winning Coalition representation, on the emergence of representatives' decision strategies, (2) to investigate the effect of the frequency of elections on the evolving inter-group relations. Outcomes of 1080 simulations show that the emergence of cooperation is strongly influenced by the delegation mechanism, the election frequency, and the interaction between these two factors.

A Forgiving Strategy for the Iterated Prisoner's Dilemma

Colm O'Riordan
Journal of Artificial Societies and Social Simulation 3 (4) 3

Kyeywords: Game Theory, Classical Iterated Prisoner's Dilemma, Cooperation
Abstract: This paper reports results obtained with a strategy for the Iterated Prisoner's Dilemma. The paper describes a strategy that tries to incorporate a technique to forgive strategies that have defected or retaliated, in the hope of (re-)establishing cooperation. The strategy is compared to well-known strategies in the domain and results presented. The initial findings, as well as echoing past findings, provides evidence to suggest a higher degree of forgiveness can be beneficial and may result in greater rewards.

The Integration of Defectors in a Cooperative Setting

Marie-Edith Bissey and Guido Ortona
Journal of Artificial Societies and Social Simulation 5 (2) 2

Kyeywords: Cooperation, Conventions, Prisoner's Dilemma, Social Simulation, SWARM
Abstract: This paper describes a study of the robustness of cooperative conventions. We observe the effect of the invasion of non-cooperating subjects into a community adopting a cooperative convention. The convention is described by an indefinitely repeated prisoner-dilemma game. We check the effects on the robustness of the cooperating convention of two characteristics of the game, namely the size of the prisonner-dilemma groups and the "intelligence" of the players. The relevance for real-world problems is considered. We find that the "intelligence" of the players plays a crucial role in the way players learn to cooperate. The simulation program is written in SWARM (Java version).

Cooperation with Random Interactions and Without Memory or "tags"

Hugo Fort
Journal of Artificial Societies and Social Simulation 6 (2) 4

Kyeywords: Cooperation, complex adaptive agents, prisoner?s dilemma, game theory, evolutionary model
Abstract: The self-organization into cooperative regimes of a system of "selfish" agents playing the pairwise Prisoner's Dilemma game (PDG) is analyzed using a simple agent-based model. At each time step t, the agents divide into those who cooperate (C) and those who defect (D). The agents have no memory and use strategies not based on direct reciprocity nor 'tags'. Only one dynamical variable is assigned to each agent, namely his income at time t dC(t) obtained by playing the PDG with a partner chosen at random. A simple adapting strategy for the behavior of the agents (C or D) is shown to give rise, for a wide variety of PD payoff matrices, to a cooperative regime resistant to ?always D? strategy.

An Adaptive Toolbox Model: a Pluralistic Modelling Approach for Human Behaviour Based on Observation

Claudia Pahl-Wostl and Eva Ebenhöh
Journal of Artificial Societies and Social Simulation 7 (1) 3

Kyeywords: Social Simulation, Experimental Economics, Common Pool Resource Games, Adaptive Toolbox, Altruistic Punishment
Abstract: This article describes a social simulation model based on an economic experiment about altruistic behavior. The experiment by Fehr and Gächter showed that participants made frequent use of costly punishment in order to ensure continuing cooperation in a common pool resource game. The model reproduces not only the aggregated but also the individual data from the experiment. It was based on the data rather than theory. By this approach new insights about human behaviour and decision making may be found. The model was not designed as a stand-alone model, but as a starting point for a comprehensive Adaptive Toolbox Model. This may form a framework for modelling results from different economic experiments, comparing results and underlying assumptions, and exploring whether the insights thus gained also apply to more realistic situations.

Case-Based Reasoning, Social Dilemmas, and a New Equilibrium Concept

Luis R. Izquierdo, Nicholas M. Gotts and Gary Polhill
Journal of Artificial Societies and Social Simulation 7 (3) 1

Kyeywords: Social Dilemmas, Case-Based Reasoning, Prisoner's Dilemma, Agent-Based Simulation, Analogy, Game Theory, Aspiration Thresholds, Equilibrium
Abstract: In this paper social dilemmas are modelled as n-player games. Orthodox game theorists have been able to provide several concepts that narrow the set of expected outcomes in these models. However, in their search for a reduced set of solutions, they had to pay a very high price: they had to make disturbing assumptions such as instrumental rationality or common knowledge of rationality, which are rarely observed in any real-world situation. We propose a complementary approach, assuming that people adapt their behaviour according to their experience and look for outcomes that have proved to be satisfactory in the past. These ideas are investigated by conducting several experiments with an agent-based simulation model in which agents use a simple form of case-based reasoning. It is shown that cooperation can emerge from the interaction of selfish case-based reasoners. In determining how often cooperation occurs, aspiration thresholds, the agents' representation of the world, and their memory all play an important and interdependent role. It is also argued that case-based reasoners with high enough aspiration thresholds are not systemically exploitable, and that if agents were sophisticated enough to infer that other players are not exploitable either, they would eventually cooperate.

Emerging communication and cooperation in evolving agent societies

Pieter Buzing, A.E. Eiben and Martijn C. Schut
Journal of Artificial Societies and Social Simulation 8 (1) 2

Kyeywords: Social Simulation, Communication, Cooperation, Artificial Societies
Abstract: The main contribution of this paper is threefold. First, it presents a new software system for empirical investigations of evolving agent societies in SugarScape like environments. Second, it introduces a conceptual framework for modeling cooperation in an artificial society. In this framework the environmental pressure to cooperate is controllable by a single parameter, thus allowing systematic investigations of system behavior under varying circumstances. Third, it reports upon results from experiments that implemented and tested environments based upon this new model of cooperation. The results show that the pressure to cooperate leads to the evolution of communication skills facilitating cooperation. Furthermore, higher levels of cooperation pressure lead to the emergence of increased communication.

The Fate of Spatial Dilemmas with Different Fuzzy Measures of Success

Hugo Fort and Nicolás Pérez
Journal of Artificial Societies and Social Simulation 8 (3) 1

Kyeywords: Complex Adaptive Agents, Cooperation, Artificial Societies, Spatial Game Theory
Abstract: Cooperation among self-interested individuals pervades nature and seems essential to explain several landmarks in the evolution of live organisms, from prebiotic chemistry through to the origins of human societies. The iterated Prisoner's Dilemma (IPD) has been widely used in different contexts, ranging from social sciences to biology, to elucidate the evolution of cooperation. In this work we approach the problem from a different angle. We consider a system of adaptive agents, in a two dimensional grid, playing the IPD governed by Pavlovian strategies. We investigate the effect of different possible measures of success (MSs) used by the players to assess their performance in the game. These MSs involve quantities such as: the utilities of a player in each round U, his cumulative score (or "capital" or \'wealth\') W, his neighbourhood "welfare" and combinations of them. The agents play sequentially with one of their neighbours and the two players update their "behaviour" (C or D) using fuzzy logic which seems more appropriate to evaluate an imprecise concept like "success" than binary logic. The steady states are characterised by different degrees of cooperation, "economic geographies" (population structure and maps of capital) and "efficiencies" which depend dramatically on the MS. In particular, some MSs produce patterns of "segregation" and "exploitation".

Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms'

José Manuel Galán and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 8 (3) 2

Kyeywords: Replication, Agent-Based Modelling, Evolutionary Game Theory, Social Dilemmas, Norms, Metanorms
Abstract: In this paper we try to replicate the simulation results reported by Axelrod (1986) in an influential paper on the evolution of social norms. Our study shows that Axelrod's results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times for many periods, exploring the parameter space adequately, complementing simulation with analytical work, and being aware of the scope of our simulation models.

Uncertainty and Cooperation: Analytical Results and a Simulated Agent Society

Peter Andras, John Lazarus, Gilbert Roberts and Steven J Lynden
Journal of Artificial Societies and Social Simulation 9 (1) 7

Kyeywords: Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty
Abstract: Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.

Metamimetic Games: Modeling Metadynamics in Social Cognition

David Chavalarias
Journal of Artificial Societies and Social Simulation 9 (2) 5

Kyeywords: Social Cognition, Imitation, Cultural Co-Evolution, Differentiation, Reflexivity, Metacognition, Stochastic Game Theory, Endogenous Distributions, Metamimetic Games, Counterfactual Equilibrium
Abstract: Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive capacities are the ground for a new class of mimetic rules, we propose a formal framework, metamimetic games, that enables to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium — counterfactually stable state — and attractor are introduced. Finally, we give an interpretation of social differenciation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to immutable criteria that exist prior to the activity of agents.

The Evolution of Altruism in Spatially Structured Populations

András Németh and Károly Takács
Journal of Artificial Societies and Social Simulation 10 (3) 4

Kyeywords: Altruism, Teaching, Knowledge Transfer, Spatially Structured Social Dilemmas
Abstract: The evolution of altruism in humans is still an unresolved puzzle. Helping other individuals is often kinship-based or reciprocal. Several examples show, however, that altruism goes beyond kinship and reciprocity and people are willing to support unrelated others even when this is at a cost and they receive nothing in exchange. Here we examine the evolution of this "pure" altruism with a focus on altruistic teaching. Teaching is modeled as a knowledge transfer which enhances the survival chances of the recipient, but reduces the reproductive efficiency of the provider. In an agent-based simulation we compare evolutionary success of genotypes that have willingness to teach with those who do not in two different scenarios: random matching of individuals and spatially structured populations. We show that if teaching ability is combined with an ability to learn and individuals encounter each other on a spatial proximity basis, altruistic teaching will attain evolutionary success in the population. Settlement of the population and accumulation of knowledge are emerging side-products of the evolution of altruism. In addition, in large populations our simple model also produces a counterintuitive result that increasing the value of knowledge keeps fewer altruists alive.

Imitation and Cooperation in Different Helping Games

Giangiacomo Bravo
Journal of Artificial Societies and Social Simulation 11 (1) 8

Kyeywords: Imitation, Evolution of Cooperation, Helping Game, Indirect Reciprocity
Abstract: The relation between imitation and cooperation in evolutionary settings presents complex aspects. From one hand, in any environment where egoists are favored over cooperators by selection processes, imitation should lead to a further spreading of the former ones due to the combined processes of individual selection and replication of successful behaviors. On the other hand, if cooperators succeed in forming clusters of mutual helping individuals, imitation may have a positive effect on cooperation by further reproducing this locally dominant behavior. This paper explores the relationship between imitation and cooperation by mean of a simulation model based on two different Helping games. Our model shows that different imitation mechanisms can favor the spreading of cooperation under a wide range of conditions. Moreover, the interplay of imitation and other factors — e.g. the possibility of performing “conditional associations” strategies — can further foster the success of cooperative agents.

Reinforcement Learning Dynamics in Social Dilemmas

Segismundo S. Izquierdo, Luis R. Izquierdo and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 11 (2) 1

Kyeywords: Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning
Abstract: In this paper we replicate and advance Macy and Flache\'s (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players\' decisions can change the dynamics of the model dramatically.

A Replication That Failed - on the Computational Model in 'Michael W. Macy and Yoshimichi Sato: Trust, Cooperation and Market Formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002'

Oliver Will and Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 11 (3) 3

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: The article describes how and why we failed to replicate main effects of a computational model that Michael Macy and Yoshimichi Sato published in the Proceedings of the National Academy of Sciences (May 2002). The model is meant to answer a fundamental question about social life: Why, when and how is it possible to build trust with distant people? Based on their model, Macy and Sato warn the US society about an imminent danger: the possible break down of trust caused by too much social mobility. But the computational evidence for exactly that result turned out not to be replicable.

Reply to Will and Hegselmann

Michael Macy and Yoshimichi Sato
Journal of Artificial Societies and Social Simulation 11 (4) 11

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: [No abstract]

A Proximate Mechanism for Communities of Agents to Commemorate Long Dead Ancestors

Bill Tomlinson
Journal of Artificial Societies and Social Simulation 12 (1) 7

Kyeywords: Agent Based Models, Ancestor Commemoration, Dominance Relationships, Communication, Cooperation, Memory
Abstract: Many human cultures engage in the collective commemoration of dead members of their community. Ancestor veneration and other forms of commemoration may help to reduce social distance within groups, thereby encouraging reciprocity and providing a significant survival advantage. Here we present a simulation in which a prototypical form of ancestor commemoration arises spontaneously among computational agents programmed to have a small number of established human capabilities. Specifically, ancestor commemoration arises among agents that: a) form relationships with each other, b) communicate those relationships to each other, and c) undergo cycles of life and death. By demonstrating that ancestor commemoration could have arisen from the interactions of a small number of simpler behavioural patterns, this simulation may provide insight into the workings of human cultural systems, and ideas about how to study ancestor commemoration among humans.

A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community

Conrad Power
Journal of Artificial Societies and Social Simulation 12 (1) 8

Kyeywords: Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems
Abstract: The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.

Punishment Deters Crime Because Humans Are Bounded in Their Strategic Decision-Making

Heiko Rauhut and Marcel Junker
Journal of Artificial Societies and Social Simulation 12 (3) 1

Kyeywords: Crime, Punishment, Control, Bounded Rationality, Agent-Based Simulation, Experiment, Game Theory
Abstract: Is it rational to reduce criminal activities if punishments are increased? While intuition might suggest so, game theory concludes differently. From the game theoretical perspective, inspectors anticipate the effect of increased punishments on criminal behavior and reduce their inspection activities accordingly. This implies that higher punishments reduce inspections and do not affect crime rates. We present two laboratory experiments, which challenge this perspective by demonstrating that both, criminals and inspectors, are affected by punishment levels. Thereupon, we investigate with agent-based simulations, whether models of bounded rationality can explain our empirical data. We differentiate between two kinds of bounded rationality; the first considers bounded learning from social interaction, the second bounded decision-making. Our results suggest that humans show both kinds of bounded rationality in the strategic situation of crime, control and punishment. We conclude that it is not the rationality but the bounded rationality in humans that makes punishment effective.

Resolving a Replication That Failed: News on the Macy & Sato Model

Oliver Will
Journal of Artificial Societies and Social Simulation 12 (4) 11

Kyeywords: Replication, Social Dilemma Situations, Trust, Simulation Methodology, Cooperation
Abstract: The paper at hand aimes at identifying the assumptions that lead to the results presented in an article by Michael Macy and Yoshimichi Sato published in PNAS. In answer to a failed replication, the authors provided the source code of their model and here the results of carefully studying that code are presented. The main finding is that the simulation program implements an assumption that is most probably an unwilling, unintended, and unwanted implication of the code. This implied assumption is never mentioned in Macy and Sato's article and if the authors wanted to program what they describe in their article then it is due to a programming error. After introducing the reader to the discussion, data that stem from a new replication based on the assumptions extracted from the source code is compared with the results published in Macy and Sato's original article. The replicated results are sufficiently similar to serve as a strong indicator that this new replication implements the same relevant assumptions as the original model. Afterwards it is shown that a removal of the dubious assumption leads to results that are dramatically different from those published in Macy and Sato's PNAS article.

Cooperation in the Prisoner's Dilemma Game Based on the Second-Best Decision

Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 12 (4) 7

Kyeywords: Cooperation, Altruism, Agent-Based Simulation, Evolutionary Game Theory
Abstract: In the research addressing the prisoner's dilemma game, the effectiveness and accountableness of the method allowing for the emergence of cooperation is generally discussed. The most well-known solutions for this question are memory based iteration, the tag used to distinguish between defector and cooperator, the spatial structure of the game and the either direct or indirect reciprocity. We have also challenged to approach the topic from a different point of view namely that temperate acquisitiveness in decision making could be possible to achieve cooperation. It was already shown in our previous research that the exclusion of the best decision had a remarkable effect on the emergence of an almost cooperative state. In this paper, we advance the decision of our former research to become more explainable by introducing the second-best decision. If that decision is adopted, players also reach an extremely high level cooperative state in the prisoner's dilemma game and also in that of extended strategy expression. The cooperation of this extended game is facilitated only if the product of two parameters is under the criticality. In addition, the applicability of our model to the problem in the real world is discussed.

A Tag-Based Evolutionary Prisoner's Dilemma Game on Networks with Different Topologies

Jae-Woo Kim
Journal of Artificial Societies and Social Simulation 13 (3) 2

Kyeywords: Prisoner\'s Dilemma Game, Tags, Parochial Cooperation, Clustering, Small-World-Ness, NetLogo
Abstract: Researchers from many disciplines have been interested in the maintenance of cooperation in animal and human societies using the Prisoner\'s Dilemma game. Recent studies highlight the roles of cognitively simple agents in the evolution of cooperation who read tags to interact either discriminately or selectively with tolerably similar partners. In our study on a one-shot Prisoner\'s Dilemma game, artificial agents with tags and tolerance perceive dissimilarities to local neighbors to cooperate with in-group and otherwise defect. They imitate tags and learn tolerance from more successful neighbors. In terms of efficiency, society-wide cooperation can evolve even when the benefits of cooperation are relatively low. Meanwhile, tolerance however decreases as agents become homogenized. In terms of stability, parochial cooperators are gullible to the deviants defectors displaying tolerably similar tags. We find that as the benefits of cooperation increase and the dimensions of tag space become larger, emergent societies can be more tolerant towards heterogeneous others. We also identify the effects of clustering and small-world-ness on the dynamics of tag-based parochial cooperation in spite of its fundamental vulnerability to those deviants regardless of network topology. We discuss the issue of tag changeability in search for alternative societies in which tag-based parochial cooperation is not only efficient but also robust.

Co-Operative Punishment Cements Social Cohesion

Klaus Jaffe and Luis Zaballa
Journal of Artificial Societies and Social Simulation 13 (3) 4

Kyeywords: Altruism, Cooperation, Social, Prosocial, Cohesion, Evolution, Punishment, Retribution
Abstract: Most current attempts to explain the evolution - through individual selection - of pro-social behavior (i.e. behavior that favors the group) that allows for cohesive societies among non related individuals, focus on altruistic punishment as its evolutionary driving force. The main theoretical problem facing this line of research is that in the exercise of altruistic punishment the benefits of punishment are enjoyed collectively while its costs are borne individually. We propose that social cohesion might be achieved by a form of punishment, widely practiced among humans and animals forming bands and engaging in mob beatings, which we call co-operative punishment. This kind of punishment is contingent upon - not independent from - the concurrent participation of other actors. Its costs can be divided among group members in the same way as its benefits are, and it will be favoured by evolution as long as the benefits exceed the costs. We show with computer simulations that co-operative punishment is an evolutionary stable strategy that performs better in evolutionary terms than non-cooperative punishment, and demonstrate the evolvability and sustainability of pro-social behavior in an environment where not necessarily all individuals participate in co-operative punishment. Co-operative punishment together with pro-social behavior produces a self reinforcing system that allows the emergence of a 'Darwinian Leviathan' that strengthens social institutions.

Scale-Free Relationships Facilitate Cooperation in Spatial Games with Sequential Strategy

Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 14 (3) 3

Kyeywords: Cooperation, Second-Best Decision, Multi-Agent Simulation, Spatial Game, Collusive Tendering
Abstract: Recently, the area of study of spatial game continuously has extended, and researchers have especially presented a lot of works of coevolutionary mechanism. We have recognized coevolutionary mechanism as one of the factors for the promotion of cooperation like five rules by Nowak. However, those studies still deal with the optimal response (best decision). The best decision is persuasive in most cases, but does not apply to all situations in the real world. Contemplating that question, researchers have presented some works discussing not only the best decision but also the second-best decision. Those studies compare the results between the best and the second-best, and also state the applicability of the second-best decision. This study, considering that trend, has extended the match between two groups to spatial game with the second-best decision. This extended model expresses relationships of groups as a spatial network, and every group matches other groups of relationships. Then, we examine how mutual cooperation changes in each case where either we add probabilistic perturbation to relationships or ties form various types of the structure. As a result, unlike most results utilizing the best decision, probabilistic perturbation does not induce any change. On the other hand, when ties are the scale-free structure, mutual cooperation is enhanced like the case of the best decision. When we probe the evolution of strategies in that case, groups with many ties play a role for leading the direction of decision as a whole. This role appears without explicit assignment. In the discussion, we also state that the presented model has an analogy to the real situation, collusive tendering.

Sympathy and Punishment: Evolution of Cooperation in Public Goods Game

Hang Ye, Fei Tan, Mei Ding, Yongmin Jia and Yefeng Chen
Journal of Artificial Societies and Social Simulation 14 (4) 20

Kyeywords: Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment
Abstract: An important way to maintain human cooperation is punishing defection. However, since punishment is costly, how can it arise and evolve given that individuals who contribute but do not punish fare better than the punishers? This leads to a violation of causality, since the evolution of punishment is prior to the one of cooperation behaviour in evolutionary dynamics. Our public goods game computer simulations based on generalized Moran Process, show that, if there exists a \'behaviour-based sympathy\' that compensates those who punish at a personal cost, the way for the emergence and establishment of punishing behaviour is paved. In this way, the causality violation dissipates. Among humans sympathy can be expressed in many ways such as care, praise, solace, ethical support, admiration, and sometimes even adoration; in our computer simulations, we use a small amount of transfer payment to express \'behaviour-based sympathy\'. Our conclusions indicate that, there exists co-evolution of sympathy, punishment and cooperation. According to classical philosophy literature, sympathy is a key factor in morality and justice is embodied by punishment; in modern societies, both the moral norms and the judicial system, the representations of sympathy and punishment, play an essential role in stable social cooperation.

Are R&D Subsidies Provided Optimally? Evidence from a Simulated Agency-Firm Stochastic Dynamic Game

Giovanni Cerulli
Journal of Artificial Societies and Social Simulation 15 (1) 7

Kyeywords: R&D Subsidies, Rivalry Versus Cooperation, Dynamic-Stochastic Games, Simulations
Abstract: By means of a simulated funding-agency/supported-firm stochastic dynamic game, this paper shows that the level of the subsidy provided by a funding (public) agency, normally used to correct for firm R&D shortage, might be severely underprovided. This is due to the "externalities" generated by the agency-firm strategic relationship, as showed by comparing two versions of the model: one assuming "rival" behaviors between companies and agency (i.e., the current setting), and one associated to the "cooperative" strategy (i.e. the optimal Pareto-efficient benchmark). The paper looks also at what "welfare" implications are associated to different degrees of persistency in the funding effect on corporate R&D. Three main conclusions are thus drawn: (i) the relative quota of the subsidy to R&D is undersized in the rival compared to the cooperative model; (ii) the rivalry strategy generates distortions that favor the agency compared to firms; (iii) when passing from less persistent to more persistent R&D additionality/crowding-out effect, the lower the distortion the greater the variance is and vice versa. As for the management of R&D funding policies, we suggest that all the elements favouring greater collaboration between agency and firm objectives may help current R&D support to approach its social optimum.

Tag-Mediated Altruism is Contingent on How Cheaters Are Defined

Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 16 (1) 4

Kyeywords: Cooperation, Evolution, Green Beard, Social Parasitism, Chromodynamics
Abstract: Cooperation is essential for complex biological and social systems and explaining its evolutionary origins remains a central question in several disciplines. Tag systems are a class of models demonstrating the evolution of cooperation between selfish replicators. A number of previous models have been presented but they have not been widely explored. Though previous researchers have concentrated on the effects of one or several parameters of tag models, exploring exactly what is meant by cheating in a tag system has received little attention. Here we re-implement three previous models of tag-mediated altruism and introduce four definitions of cheaters. Previous models have used what we consider weaker versions of cheaters that may not exploit cooperators to the degree possible, or to the degree observed in natural systems. We find that the level of altruism that evolves in a population is highly contingent on how cheaters are defined. In particular when cheaters are defined as agents that display an appropriate tag but have no mechanism for participating in altruistic acts themselves, a population is quickly invaded by cheaters and all altruism collapses. Even in the intermediate case where cheaters may revert back to a tag-tolerance mode of interaction, only minimal levels of altruism evolve. Our results suggest that models of tag-mediated altruism using stronger types of cheaters may require additional mechanisms, such as punishment strategies or multi-level selection, to evolve meaningful levels of altruism.

Cooperation Could Evolve in Complex Networks when Activated Conditionally on Network Characteristics

Yen-Sheng Chiang
Journal of Artificial Societies and Social Simulation 16 (2) 6

Kyeywords: Evolution of Cooperation, Complex Network, Spatial Game, Conditional Cooperation
Abstract: The investigation of how cooperation is achieved on graphs in the field of spatial game or network reciprocity has received proliferating attention in the biological and sociological literature. In line of the research, this paper provides an new account of how cooperation could evolve in complex networks when actors use information of network characteristics to strategize whether to cooperate or not. Different from past work that focuses exclusively on the evolution of unconditional cooperation, we are proposing new strategies that are choosy in whom to cooperate with, conditional on the structural attributes of the nodes occupied by actors. In a series of evolutionary tournaments conducted by computer simulation, the model shows that a pair of simple strategies-cooperating respectively with higher and lower nodal-attribute neighbors-can be advantageous in adaptive fitness when competing against unconditional cooperation and defection. In particular, these strategies of conditional cooperation work well in random graphs-a network known for being unfavorable to the selection of cooperation. This paper contributes to the literature by showing how network characteristics can serve as a mechanism to sustain cooperation in some hostile network environments where unconditional cooperation is unable to evolve. The cognitive foundations of the mechanism and its implications are discussed.

The Evolutionary Dominance of Ethnocentric Cooperation

Max Hartshorn, Artem Kaznatcheev and Thomas Shultz
Journal of Artificial Societies and Social Simulation 16 (3) 7

Kyeywords: Ethnocentrism, Evolution of Cooperation, Evolutionary Game Theory, Minimal Cognition, Prisoner's Dilemma
Abstract: Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.

Punishment Mechanisms and Their Effect on Cooperation: A Simulation Study

Mike Farjam, Marco Faillo, Ida Sprinkhuizen-Kuyper and Pim Haselager
Journal of Artificial Societies and Social Simulation 18 (1) 5

Kyeywords: Public Goods Games, Punishment, Cooperation, Reciprocity, Evolution of Cooperation
Abstract: In social dilemmas punishment costs resources, not just from the one who is punished but often also from the punisher and society. Reciprocity on the other side is known to lead to cooperation without the costs of punishment. The questions at hand are whether punishment brings advantages besides its costs, and how its negative side-effects can be reduced to a minimum in an environment populated by agents adopting a form of reciprocity. Various punishment mechanisms have been studied in the economic literature such as unrestricted punishment, legitimate punishment, cooperative punishment, and the hired gun mechanism. In this study all these mechanisms are implemented in a simulation where agents can share resources and may decide to punish other agents when the other agents do not share. Through evolutionary learning agents adapt their sharing/punishing policy. When the availability of resources was restricted, punishment mechanisms in general performed better than no-punishment, although unrestricted punishment was performing worse. When resource availability was high, performance was better in no-punishment conditions with indirect reciprocity. Unrestricted punishment was always the worst performing mechanism. Summarized, this paper shows that, in certain environments, some punishment mechanisms can improve the efficiency of cooperation even if the cooperating system is already based on indirect reciprocity.

Altruism Displays a Harmonic Signature in Structured Societies

Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 18 (3) 2

Kyeywords: Tags, Thresholds, Altruism, Evolution, Cooperation, Social Harmonics
Abstract: Several parameters combine to govern the nature of agent interactions in evolutionary social simulations. Previous work has suggested that these parameters may have complex interplay that is obscured when they are not analyzed separately. Here we focus specifically on how three population-level parameters, which govern agent interactions, affect levels of altruism in a population. Specifically we vary how frequently agents interact in a generation, how far along a network they may interact, and the size of the population. We show that the frequency with which an agent interacts with its neighbors during a generation has a strong effect on levels of evolved altruism – provided that those pairings are stochastic. When agents interact equally with all of their neighbors, regardless of how often, minimal levels of altruism evolve. We further report a curious harmonic signature in the level of altruism resulting from the interplay of the benefit-cost ratio of an altruistic act and the number of agent interactions per generation. While the level of altruism is generally an increasing function of the number of pairings per generation, at each instance where pairings equals a multiple of the benefit-cost ratio a sharp discontinuity occurs, precipitating a drop onto a lower-value function. We explore the nature of these discontinuities by examining the temporal dynamics and spatial configuration of agents. Finally, we show that rules for the evolution of cooperation that are based on network density may be inadvertently missing effects that are due to the frequency of interactions and whether those interactions are symmetrical among neighbors.

Degrees of Separation, Social Learning, and the Evolution of Cooperation in a Small-World Network

Nicholas Seltzer and Oleg Smirnov
Journal of Artificial Societies and Social Simulation 18 (4) 12

Kyeywords: Cooperation, Social Networks, Small-World, Modern Society, Simulation, Agent-Based
Abstract: We analyze a novel agent-based model of a social network in which agents make contributions to others conditional upon the social distance, which we measure in terms of the “degrees of separation” between the two players. On the basis of a simple imitation model, the emerging strategy profile is characterized by high levels of cooperation with those who are directly connected to the agent and lower but positive levels of cooperation with those who are indirectly connected to the agent. Increasing maximum interaction distance decreases cooperation with close neighbors but increases cooperation with distant neighbors for a net negative effect. On the other hand, allowing agents to learn and imitate socially distant neighbors increases cooperation for all types of interaction. Combining greater interaction distance with greater learning distance leads to a positive change in the total social welfare produced by the agents’ contributions.

The Effects of Network Structure on the Emergence of Norms in Adaptive Populations

Peter Revay
Journal of Artificial Societies and Social Simulation 18 (4) 14

Kyeywords: Social Norms, Agent-Based Modeling, Social Networks, Neighborhood Structure, Cooperation
Abstract: The different ways individuals socialize with others affect the conditions under which social norms are able to emerge. In this work an agent-based model of cooperation in a population of adaptive agents is presented. The model has the ability to implement a multitude of network topologies. The agents possess strategies represented by boldness and vengefulness values in the spirit of Axelrod's (1986) norms game. However, unlike in the norms game, the simulations abandon the evolutionary approach and only follow a single-generation of agents who are nevertheless able to adapt their strategies based on changes in their environment. The model is analyzed for potential emergence or collapse of norms under different network and neighborhood configurations as well as different vigilance levels in the agent population. In doing so the model is found able to exhibit interesting emergent behavior suggesting potential for norm establishment even without the use of so-called metanorms. Although the model shows that the success of the norm is dependent on the neighborhood size and the vigilance of the agent population, the likelihood of norm collapse is not monotonically related to decreases in vigilance.

Transitions Between Homophilic and Heterophilic Modes of Cooperation

Genki Ichinose, Masaya Saito, Hiroki Sayama and Hugues Bersini
Journal of Artificial Societies and Social Simulation 18 (4) 3

Kyeywords: Evolution of Cooperation, Tag, Spatial Structure, Migration, Segregation
Abstract: Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.

The Blessing of Sexuality: Evolution of Altruism with Mating Preference

Tanzhe Tang and Hang Ye
Journal of Artificial Societies and Social Simulation 19 (2) 2

Kyeywords: Altruistic Punishment, Mating Preference, Sexual Attractiveness, Social Dilemma
Abstract: Current simulation practices in artificial societies typically ignore the contribution of sexuality as a driving force for the evolution of prosocial behaviours. As recent researches in biology and genetics argued, sexual attractiveness, via the method of sexual selection, can explain many aspects of the second-order social dilemma. The basic hypothesis is that altruism is a sexually attractive virtue. To introduce the hypothesis into the analysis of human altruism, we employ the concepts of altruistic punishment and the behaviour-based sexual attractiveness to develop a gender-based evolutionary model where mating preference acts as the compensation to the male punishers from females in the given public goods game. In the model, the force of sexual selection is expressed as the effect of mating preference on altruism. The computer simulation indicates that social cohesion can be achieved by the existence of sexuality in an artificial society where the co-evolution of mating preference, altruistic punishment and cooperation exist. We then extend the model in two ways: (1) we employ the variable size population assumption to test the invasion capacity of cooperators, and (2) individual variation in altruistic investment is introduced to replace the average population payoff function in the baseline model. The variable size population and individual variation in investment are found to have amplifying effects on the evolution of altruism from different perspectives. Finally, we discuss the definition of altruism in dynamic evolutionary games, as well as the gender differences in the formation of altruism in primitive tribes.

An Empirical Game-Theoretic Analysis of the Dynamics of Cooperation in Small Groups

Steve Phelps
Journal of Artificial Societies and Social Simulation 19 (2) 4

Kyeywords: Evolution, Cooperation, Reciprocity
Abstract: Many models of the evolution of cooperation have shown the importance of direct reciprocity (for example “tit for tat” strategies) or alternatively indirect reciprocity (conspicuous altruism based on a reputation or “image score”). In the latter case many models make the implicit assumption that group sizes are large relative to the expected number of interactions, which makes their analysis more tractable in several ways, not least by allowing us to ignore any strategic interaction between the direct and indirect classes of reciprocation strategy. However, in smaller groups the possibility arises that both classes of strategy will play a role in determining the equilibrium behaviour. Therefore we introduce a replicator dynamics model which incorporates both direct and indirect reciprocity, and use simulation and numerical methods to quantitatively assess how the level of cooperation in equilibrium is affected by changes in the group size and the frequency with which other group members are encountered. Our analysis shows that, for intermediate group sizes, direct reciprocity persists in equilibrium alongside indirect reciprocity. In contrast to previous simulation studies, we provide a sound game-theoretic underpinning to our analysis, and examine the precise conditions which give rise to a mix of both forms of reciprocity.

Cooperation Via Intimidation: An Emergent System of Mutual Threats can Maintain Social Order

Piotr Mateusz Patrzyk and Martin Takáč
Journal of Artificial Societies and Social Simulation 20 (4) 5

Kyeywords: Cooperation, Punishment, Revenge, Conflict, Aggression, Morality
Abstract: Can human aggressiveness promote peaceful cooperation? Despite the seeming contradiction of these phenomena, our study suggests the answer is yes. We develop two agent-based models of cooperative interactions among aggressive agents threatening each other. In Model 1, we show that aggressive displays performed by dominance-seeking individuals create a system of mutual threats that effectively enforces cooperation and inhibits agents from escalating conflicts. This happens because agents observe each other fighting, which deters them from attacking each other due to aggressive reputations. In Model 2 we extend this effect to third-party interventions showing that forming alliances makes attacks more efficient and promotes the emergence of common rules determining whom to fight against. In such a state, social order is maintained by the existence of moral alliances – groups of agents willing to fight against norm violators. In summary, we argue that reputation for toughness and the aggressive predisposition of humans could have played an important role in the evolution of cooperation and moral systems.

The Role of Heterogeneity and the Dynamics of Voluntary Contributions to Public Goods: An Experimental and Agent-Based Simulation Analysis

Engi Amin, Mohamed Abouelela and Amal Soliman
Journal of Artificial Societies and Social Simulation 21 (1) 3

Kyeywords: Agent-Based Simulation, Cooperation, Public Goods Game, Laboratory Experiment, Social Preferences
Abstract: This paper examines the role of heterogeneous agents in the study of voluntary contributions to public goods. A human-subject experiment was conducted to classify agent types and determine their effects on contribution levels. Data from the experiment was used to build and calibrate an agent-based simulation model. The simulations display how different compositions of agent preference types affect the contribution levels. Findings indicate that the heterogeneity of cooperative preferences is an important determinant of a population’s contribution pattern.

An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members

Ismo T. Koponen and Maija Nousiainen
Journal of Artificial Societies and Social Simulation 21 (2) 1

Kyeywords: Discourse Patterns, Task Focused Groups, Agent-Based Model, Competition, Cooperation
Abstract: Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peer-to-peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity and cooperativity. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.

The Evolution of Tribalism: A Social-Ecological Model of Cooperation and Inter-Group Conflict Under Pastoralism

Nicholas Seltzer
Journal of Artificial Societies and Social Simulation 22 (2) 6

Kyeywords: Evolution, Cooperation, Inter-Group, Conflict, Warfare, Tribal
Abstract: This study investigates a possible nexus between inter-group competition and intra-group cooperation, which may be called "tribalism." Building upon previous studies demonstrating a relationship between the environment and social relations, the present research incorporates a social-ecological model as a mediating factor connecting both individuals and communities to the environment. Cyclical and non-cyclical fluctuation in a simple, two-resource ecology drive agents to adopt either "go-it-alone" or group-based survival strategies via evolutionary selection. Novelly, this simulation employs a multilevel selection model allowing group-level dynamics to exert downward selective pressures on individuals' propensity to cooperate within groups. Results suggest that cooperation and inter-group conflict are co-evolved in a triadic relationship with the environment. Resource scarcity increases inter-group competition, especially when resources are clustered as opposed to widely distributed. Moreover, the tactical advantage of cooperation in the securing of clustered resources enhanced selective pressure on cooperation, even if that implies increased individual mortality for the most altruistic warriors. Troubling, these results suggest that extreme weather, possibly as a result of climate change, could exacerbate conflict in sensitive, weather-dependent social-ecologies---especially places like the Horn of Africa where ecologically sensitive economic modalities overlap with high-levels of diversity and the wide-availability of small arms. As well, global development and foreign aid strategists should consider how plans may increase the value of particular locations where community resources are built or aid is distributed, potentially instigating tribal conflict. In sum, these factors, interacting with pre-existing social dynamics dynamics, may heighten inter-ethnic or tribal conflict in pluralistic but otherwise peaceful communities.

ABOOMS: Overcoming the Hurdles of Continuous-Time Public Goods Games with a Simulation-Based Approach

Tuong Manh Vu, Christian Wagner and Peer-Olaf Siebers
Journal of Artificial Societies and Social Simulation 22 (2) 7

Kyeywords: Agent-Based Modelling and Simulation, Continuous-Time Public Goods Game, Software Engineering, Agent-Based Computational Economics, Object-Oriented Analysis and Design
Abstract: Public Goods Games (PGGs) are a standard experimental economic approach to studying cooperative behaviour. There are two types of games: discrete-time and continuous-time PGGs. While discrete-time PGGs (one-shot decisions about contributions to public goods) can be easily done as lab experiments, continuous-time PGGs (where participants can change contributions at any time) are much harder to realise within a lab environment. This is mainly because it is difficult to consider events happening in continuous time in lab experiments. Simulation offers an opportunity to support real-world lab experiments and is well suited to explore continuous-time PGGs. In this paper, we show how to apply our recently developed ABOOMS (Agent-Based Object-Oriented Modelling and Simulation) development framework to create models for simulation-supported continuous-time PGG studies. The ABOOMS framework utilizes Software Engineering techniques to support the development at the macro level (considering the overall study lifecycle) and at the micro level (considering individual steps related to simulation model development). Our case study shows that outputs from the simulation-supported continuous-time PGG generate dynamics that do not exist in discrete-time setting, highlighting the fact that it is important to study both, discrete and continuous-time PGGs.

How Group Cohesion Promotes the Emergence of Cooperation in Public Goods Game Under Conditional Dissociation

Xinglong Qu, Zhigang Cao, Xiaoguang Yang and The Anh Han
Journal of Artificial Societies and Social Simulation 22 (3) 5

Kyeywords: Group Cohesion, Public Goods Game, Cooperation Emergence, Conditional Dissociation, Positive Assortment
Abstract: Leaving is usually an option for individuals if they cannot tolerate their defective partners. In a two-player game, when a player chooses to leave, both she and her opponent become single players. However, in a multi-player game, the same decision may have different consequences depending on whether group cohesion exists. Players who choose not to leave would still be united together rather than be separated into singletons if there is cohesion among them. Considering this difference, we study two leaving mechanisms in public goods games. In the first mechanism, every player would be single once any of the group members leaves. In the second, we assume group cohesion exists that members who don't leave form a union. In our model, each player adopts a trigger strategy characterized by a threshold: she leaves if the number of defectors in her group exceeds the threshold. We find that under both mechanisms, when the expected lifespan of individuals is long enough, cooperators with zero tolerance toward defection succeed in the evolution. Moreover, when cohesion exists in groups, cooperation is better promoted because the cooperators have a higher chance to play together. That is, group cohesion facilitates positive assortment and therefore promotes cooperation.

An Agent-Based Model of Firm Size Distribution and Collaborative Innovation

Inyoung Hwang
Journal of Artificial Societies and Social Simulation 23 (1) 9

Kyeywords: Agent-Based Modelling, Prisoner’s Dilemma, Pavlovian Cooperation, Collaborative Innovation, Firm Size Distribution, ICT Industry
Abstract: ICT-based Collaborative innovation has a significant impact on the economy by facilitating technological convergence and promoting innovation in other industries. However, research on innovation suggests that polarization in firm size distribution, which has grown since the early 2000s, can interfere with collaborative innovation among firms. In this paper, I modelled firms’ decision-making processes that led to collaborative innovation as a spatial N-person iterated Prisoner’s dilemma (NIPD) game using collaborative innovation data from Korean ICT firms. Using an agent-based model, I experimented with the effects of firm size heterogeneity on collaborative innovation. The simulation experiment results reveal that collaborative innovation in the industry increases as the size heterogeneity decreases. Findings suggest that policies promoting collaborative innovation should focus on mitigating structural inequalities in the industry.

A Bad Barrel Spoils a Good Apple: How Uncertainty and Networks Affect Whether Matching Rules Can Foster Cooperation

Carlos A. de Matos Fernandes, Andreas Flache, Dieko M. Bakker and Jacob Dijkstra
Journal of Artificial Societies and Social Simulation 25 (1) 6

Kyeywords: Cooperation, Meritocratic Matching, Information, Homophily, Threshold Model, Learning
Abstract: Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less effective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks differentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other’s behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, non-cooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters.

Egalitarian Sharing Explains Food Distributions in a Small-Scale Society

Marcos Pinheiro
Journal of Artificial Societies and Social Simulation 25 (3) 5

Kyeywords: Hunter-Gatherers, Food Sharing, Evolution of Cooperation, Egalitarianism, Agent-Based Model
Abstract: Among social anthropologists, there is virtual consensus that the food-sharing practices of small-scale non-agricultural groups cannot be understood in isolation from the broader repertoire of leveling strategies that prevent would-be dominants from exercising power and influence over likely subordinates. In spite of that widespread view, quantitatively rigorous empirical studies of food sharing and cooperation in small-scale human groups have typically ignored the internal connection between leveling of income and political power, drawing inspiration instead from evolutionary models that are neutral about social role asymmetries. In this paper, I introduce a spatially explicit agent-based model of hunter-gatherer food sharing in which individuals are driven by the goal of maximizing their own income while minimizing income asymmetries among others. Model simulation results show that seven basic patterns of inter-household food transfers described in detail for the Hadza hunters of Tanzania can be simultaneously reproduced with striking accuracy under the assumption that agents selectively support and carry on sharing interactions in ways that maximize their income leveling potential.