Neural networks and Markov models for the iterated prisoner's dilemma

Authors:
John Seiffertt;Samuel Mulder;Rohit Dua;Donald C. Wunsch
Affiliations:
Applied Computational Intelligence Laboratory, Missouri University of Science and Technology, Rolla, MO;Sandia National Laboratories;Department of Computer Science, New York Institute of Technology;Applied Computational Intelligence Laboratory, Missouri University of Science and Technology, Rolla, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 6
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Abstract

The study of strategic interaction among a society of agents is often handled using the machinery of game theory. This research examines how a Markov Decision Process (MDP) model may be applied to an important element of repeated game theory: the iterated prisoner's dilemma. Our study uses a Markovian approach to the game to represent the problem of in a computer simulation environment. A pure Markov approach is used on a simplified version of the iterated game and then we formulate the general game as a partially observable Markov decision process (POMDP). Finally, we use a cellular structure as an environment for players to compete and adapt. We apply both a simple replacement strategy and a cellular neural network to the environment.