Strategic Adaptation of Humans Playing Computer Algorithms in a Repeated Constant-Sum Game

Autonomous Agents and Multi-Agent Systems (2012); doi.org/10.1007/s10458-012-9203-z

35 Pages Posted: 16 Mar 2007 Last revised: 13 Aug 2012

See all articles by Leonidas Spiliopoulos

Leonidas Spiliopoulos

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development

Date Written: January 20, 2010

Abstract

This paper examines strategic adaptation in participants’ behavior conditional on the type of their opponent. Participants played a constant-sum game for 100 rounds against each of three pattern-detecting computer algorithms designed to exploit regularities in human behavior such as imperfections in randomizing and the use of simple heuristics. Significant evidence is presented that human participants not only change their marginal probabilities of choosing actions, but also their conditional probabilities dependent on the recent history of play. A cognitive model incorporating pattern recognition is proposed that capture the shifts in strategic behavior of the participants better than the standard non-pattern detecting model employed in the literature, the Experience Weighted Attraction model (and by extension its nested models, reinforcement learning and fictitious play belief learning).

Keywords: Learning, Pattern detection, Computer algorithms, Constant sum games, Experience weighted attraction, Repeated games

JEL Classification: C9, C63, C70, C72, C73, C91

Suggested Citation

Spiliopoulos, Leonidas, Strategic Adaptation of Humans Playing Computer Algorithms in a Repeated Constant-Sum Game (January 20, 2010). Autonomous Agents and Multi-Agent Systems (2012); doi.org/10.1007/s10458-012-9203-z , Available at SSRN: https://ssrn.com/abstract=970418 or http://dx.doi.org/10.2139/ssrn.970418

Leonidas Spiliopoulos (Contact Author)

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

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