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

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

Cooperation in Symmetric and Asymmetric Prisoner's Dilemma Games

We experimentally study the effect of asymmetry on cooperation in a 40 period prisoner's dilemma game in fixed partner design. We distinguish between a high and low payoff symmetric prisoner's dilemma and an asymmetric game combined out of both symmetric ones. Asymmetry significantly decreases cooperation, as low-type players are more likely to defect after mutual cooperation while high-type players initiate cooperation more often than the former. Asymmetry also has a significant negative effect on the stability of cooperation rendering long sequences of mutual cooperation extremely rare.Symmetry, Asymmetry, Prisoner's Dilemma, Experiments

Conditions of Cooperation between Rats in the Prisoner\u27s Dilemma Model

Reciprocal cooperation is the act of working together with another individual to increase the likelihood that the other individual will continue to work together during future encounters. Reciprocal cooperation can be explained evolutionarily because it promotes the fitness of individuals in certain conditions. Cooperation is most commonly studied in humans. However less complex mammals such as rats display cooperative behaviors in certain conditions. This study examines the necessary conditions for cooperation in rats by testing the significance of housing conditions and prior interactions between cooperating rats. We found that rats did not cooperate at levels greater than chance

On Partially Controlled Multi-Agent Systems

Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms, what methods can be applied to influence the uncontrollable agents, the effectiveness of such methods, and whether similar methods work across different domains. Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other they are reinforcement learners. We suggest different techniques for controlling agents' behavior in each domain, assess their success, and examine their relationship.Comment: See http://www.jair.org/ for any accompanying file

An Experimental Investigation of Fairness and Reciprocal Behavior in a Triangular Principal'-Multiagent Relationship.

A laboratory investigation of a simple agency model that allow to study how the principal's fairness affects the attitude towards cooperation between two interdependent agents performing a simple production task.principal-agent theory; prisoner's dilemma; reciprocity; fairness; experimental economics

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

Analysis of game playing agents with fingerprints

Evolutionary computation (EC) can create a vast number of strategies for playing simple games in a short time. Analysis of these strategies is typically more time-consuming than their production. As a result, analysis of strategies produced by an EC system is often lacking or restricted to the extraction of superficial summary Statistics and Probability; This thesis presents a technique for extracting a functional signature from evolved agents that play games. This signature can be used as a visualization of agent behavior in games with two moves and also provides a numerical target for clustering and other forms of automatic analysis. The fingerprint can be used to induce a similarity measure on the space of game strategies. This thesis develops fingerprints in the context of the iterated prisoner\u27s dilemma; we note that they can be computed for any two player simultaneous game with a finite set of moves. When using a clustering algorithm, the results are strongly influenced by the choice of the measure used to find the distance between or to compare the similarity of the data being clustered. The Euclidean metric, for example, rates a convex polytope as the most compact type of object and builds clusters that are contained in compact polytopes. Presented here is a general method, called multi-clustering, that compensates for the intrinsic shape of a metric or similarity measure. The method is tested on synthetic data sets that are natural for the Euclidean metric and on data sets designed to defeat k-means clustering with the Euclidean metric. Multi-clustering successfully discovers the designed cluster structure of all the synthetic data sets used with a minimum of parameter tuning. We then use multi-clustering and filtration on fingerprint data. Cellular representation is the practice of evolving a set of instructions for constructing a desired structure. This thesis presents a cellular encoding for finite state machines and specializes it to play the iterated prisoner\u27s dilemma. The impact on the character and behavior of finite state agents of using the cellular representation is investigated. For the cellular representation resented a statistically significant drop in the level of cooperation is found. Other differences in the character of the automaton generated with a direct and cellular representation are reported

Simulation Models of the Evolution of Cooperation as Proofs of Logical Possibilities. How Useful Are They?

This paper discusses critically what simulation models of the evolution of cooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” (1984) and the modeling tradition it has inspired. Hardly any of the many simulation models in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this research design was fundamentally flawed. At best such simulations can claim to prove logical possibilities, i.e. they prove that certain phenomena are possible as the consequence of the modeling assumptions built into the simulation, but not that they are possible or can be expected to occur in reality. I suggest several requirements under which proofs of logical possibilities can nevertheless be considered useful. Sadly, most Axelrod-style simulations do not meet these requirements. It would be better not to use this kind of simulations at all

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