How to Reach Pareto Optimal Equilibrium in Double-bind Prisoner's Dilemma Game

Shihomi Wada; Keiji Suzuki

doi:10.14864/softscis.2006.0.174.0

SCIS & ISIS 2006

Session ID : TH-H2-2

DOI https://doi.org/10.14864/softscis.2006.0.174.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

TH-H2 Decision Making and Discrete Event System

How to Reach Pareto Optimal Equilibrium in Double-bind Prisoner's Dilemma Game

*Shihomi Wada, Keiji Suzuki

Author information

Keywords: prisoner's dilemma game, multi-agent simulation, reinforcement-learning

CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

In this paper, we examine how to reach Pareto optimal equilibrium in Double-bind Prisoner's Dilemma game (hereinafter referred to as DbPD game). DbPD game are made by adding dominated strategy to 2 by 2 prisoner's dilemma game (Wada and Suzuki (2006)), and the problem of playing DbPD game or not also becomes a dilemma. Though our agents are all set as rational, selfish, reinforcement-learning based model agents, we discover that they learn to avoid using Nash equilibrium in early round. After finishing to learn that, then they start to learn cooparating. Through these process, our agents are possible to contribute each other in DbPD game.

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