1,224 research outputs found
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
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
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
Cheating for Problem Solving: A Genetic Algorithm with Social Interactions
We propose a variation of the standard genetic algorithm that incorporates
social interaction between the individuals in the population. Our goal is to
understand the evolutionary role of social systems and its possible application
as a non-genetic new step in evolutionary algorithms. In biological
populations, ie animals, even human beings and microorganisms, social
interactions often affect the fitness of individuals. It is conceivable that
the perturbation of the fitness via social interactions is an evolutionary
strategy to avoid trapping into local optimum, thus avoiding a fast convergence
of the population. We model the social interactions according to Game Theory.
The population is, therefore, composed by cooperator and defector individuals
whose interactions produce payoffs according to well known game models
(prisoner's dilemma, chicken game, and others). Our results on Knapsack
problems show, for some game models, a significant performance improvement as
compared to a standard genetic algorithm.Comment: 7 pages, 5 Figures, 5 Tables, Proceedings of Genetic and Evolutionary
Computation Conference (GECCO 2009), Montreal, Canad
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
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