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

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.Cooperation, Altruism, Agent-Based Simulation, Evolutionary Game Theory

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

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

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

ACE Models of Endogenous Interactions

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).Endogenous interaction, Agent-based Computational Economics (ACE)

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

Individual variation evades the Prisoner's Dilemma

BACKGROUND: The Prisoner's Dilemma (PD) is a widely used paradigm to study cooperation in evolutionary biology, as well as in fields as diverse as moral philosophy, sociology, economics and politics. Players are typically assumed to have fixed payoffs for adopting certain strategies, which depend only on the strategy played by the opponent. However, fixed payoffs are not realistic in nature. Utility functions and the associated payoffs from pursuing certain strategies vary among members of a population with numerous factors. In biology such factors include size, age, social status and expected life span; in economics they include socio-economic status, personal preference and past experience; and in politics they include ideology, political interests and public support. Thus, no outcome is identical for any two different players. RESULTS: We show that relaxing the assumption of fixed payoffs leads to frequent violations of the payoff structure required for a Prisoner's Dilemma. With variance twice the payoff interval in a linear PD matrix, for example, only 16% of matrices are valid. CONCLUSIONS: A single player lacking a valid PD matrix destroys the conditions for a Prisoner's Dilemma, so between any two players, PD games themselves are fewer still (3% in this case). This may explain why the Prisoner's Dilemma has hardly been found in nature, despite the fact that it has served as a ubiquitous (and still instructive) model in studies of the evolution of cooperation

Metabolism of Social System: N-Person Iterated Prisoner’s Dilemma Analysis In Random Boolean Network

Random Boolean Network has been used to find out regulation patterns of genes in organism. This approach is very interesting to use in a game such as N-Person Prisoner’s Dilemma. Here we assume that agent’s action is influenced by input in the form of choices of cooperate or defect she accepted from other agent or group of agents in the system. Number of cooperators, pay-off value received by each agent, and average value of the group, are observed in every state, from initial state chosen until it reaches its state-cycle attractor. In simulation performed here, we gain information that a system with large number agents based on action on input K equals to two, will reach equilibrium and stable condition over strategies taken out by its agents faster than higher input, that is K equals to three. Equilibrium reached in longer interval, yet it is stable over strategies carried out by agents