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

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.Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems

Evolutionary Game Theory

Articl

Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal

The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined.Comment: 37 pages, uuencoded gzip'd Postscript (1.1Mb when gunzip'd) also available via WWW at http://www.cs.wisc.edu/~smucker/ipd-cr/ipd-cr.htm

Simulating Evolutionary Games: A Python-Based Introduction

This paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.Agent-Based Simulation, Python, Prisoner's Dilemma

Iterated Prisoner\u27s Dilemma for Species

The Iterated Prisoner\u27s Dilemma (IPD) is widely used to study the evolution of cooperation between self-interested agents. Existing work asks how genes that code for cooperation arise and spread through a single-species population of IPD playing agents. In this paper, we focus on competition between different species of agents. Making this distinction allows us to separate and examine macroevolutionary phenomena. We illustrate with some species-level simulation experiments with agents that use well-known strategies, and with species of agents that use team strategies

Cooperation in Networked Populations of Selfish Adaptive Agents: Sensitivity to Learning Speed

This paper investigates the evolution of cooperation in iterated Prisoner's Dilemma (IPD) games with individually learning agents, subject to the structure of the interaction network. In particular, we study how Tit-for-Tat or All-Defection comes to dominate the population on Watts-Strogatz networks, under varying learning speeds and average network path lengths. We find that the presence of a cooperative regime (where almost the entire population plays Tit-for-Tat) is dependent on the quickness of information spreading across the network. More precisely, cooperation hinges on the relation between individual adaptation speed and average path length in the interaction topology. Our results are in good agreement with previous works both on discrete choice dynamics on networks and in the evolution of cooperation literature

How Evolutionary Dynamics Affects Network Reciprocity in Prisoner's Dilemma

Cooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far—as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner's dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors' payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players' strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors' payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people's behaviorThis work was supported by the Swiss Natural Science Foundation through grant PBFRP2_145872 and by Ministerio de Economía y Competitividad (Spain) through grant PRODIEVO.Publicad

THE EFFECTS OF FEEDBACK ON COOPERATION IN THE PRISONER’S DILEMMA GAME SIMULATING A CLOSED MARKET SCENARIO

This study explores the effects of feedback on cooperation in the iterated prisoner’s dilemma game (PDG). Four sources of feedback were identified: peer, buyer, market and cultural feedback. Peer and buyer feedback were intrinsic to the PDG, for they were analyzed, but not manipulated. Market and cultural feedback comprised independent variables and their effects were measured on players’ and group cooperation (dependent variables). Twenty-seven participants played a PDG, divided in 9 groups of 3 players each. Cooperation was measured as rates of individual players’ cooperative X choices, and as aggregate products within groups. At the molecular (moment-to-moment) level, there was a significant within-subjects main effect of the market feedback F(1, 28) = 6.50, p = .02, ?p2 = .19. At the molar level, there was no significant effect of the market feedback, nor of the cultural feedback. It was not possible to establish a metacontingency between recurrent group cooperation and positive contingent group consequences. Players displayed sub-optimal choice behavior, seeking to maximize relative earnings within their group (defecting) over absolute earnings (cooperating). These results are discussed in light of how the source of feedback may sustain cooperation or defection in the PDG, and their implications in organizational settings. Reinforcing cooperative behaviors can be key to the maintenance and development of any organization, for informative performance feedback may not suffice. This study contributes to the understanding of economic decisional behavior in groups from a cultural selectionist perspective.Keywords: choice, cooperation, feedback, metacontingency, prisoner’s dilemma gameThis study explores the effects of feedback on cooperation in the iterated prisoner’s dilemma game (PDG). Four sources of feedback were identified: peer, buyer, market and cultural feedback. Peer and buyer feedback were intrinsic to the PDG, for they were analyzed, but not manipulated. Market and cultural feedback comprised independent variables and their effects were measured on players’ and group cooperation (dependent variables). Twenty-seven participants played a PDG, divided in 9 groups of 3 players each. Cooperation was measured as rates of individual players’ cooperative X choices, and as aggregate products within groups. At the molecular (moment-to-moment) level, there was a significant within-subjects main effect of the market feedback F(1, 28) = 6.50, p = .02, ?p2 = .19. At the molar level, there was no significant effect of the market feedback, nor of the cultural feedback. It was not possible to establish a metacontingency between recurrent group cooperation and positive contingent group consequences. Players displayed sub-optimal choice behavior, seeking to maximize relative earnings within their group (defecting) over absolute earnings (cooperating). These results are discussed in light of how the source of feedback may sustain cooperation or defection in the PDG, and their implications in organizational settings. Reinforcing cooperative behaviors can be key to the maintenance and development of any organization, for informative performance feedback may not suffice. This study contributes to the understanding of economic decisional behavior in groups from a cultural selectionist perspective.Keywords: choice, cooperation, feedback, metacontingency, prisoner’s dilemma gam

Resistance to learning and the evolution of cooperation

In many evolutionary algorithms, crossover is the main operator used in generating new individuals from old ones. However, the usual mechanism for generating offsprings in spatially structured evolutionary games has to date been clonation. Here we study the effect of incorporating crossover on these models. Our framework is the spatial Continuous Prisoner's Dilemma. For this evolutionary game, it has been reported that occasional errors (mutations) in the clonal process can explain the emergence of cooperation from a non-cooperative initial state. First, we show that this only occurs for particular regimes of low costs of cooperation. Then, we display how crossover gets greater the range of scenarios where cooperative mutants can invade selfish populations. In a social context, where crossover involves a general rule of gradual learning, our results show that the less that is learnt in a single step, the larger the degree of global cooperation finally attained. In general, the effect of step-by-step learning can be more efficient for the evolution of cooperation than a full blast one.Evolutionary games, Continuous prisoner's dilemma, Spatially structured, Crossover, Learning