A Computational Study of Rule Learning in “Do-It-Yourself Lottery” with Aggregate Information

Conference paper
Part of the Agent-Based Social Systems book series (ABSS, volume 11)

Abstract

This chapter computationally studies Barrow’s “do-it-yourself lottery” where players choose a positive integer that is expected to be the smallest one that is not chosen by anyone else. Here, we employ and modify the rule learning framework by Stahl (Games Econ Behav 32:105–138, 2000) based on the experimental findings by Östling et al. (Am Econ J Microecon 3:1–33, 2011), and incorporate them into our simulation model to see individual and collective behavior by changing the numbers of players and the upper limit. Our main conclusion is threefold: First, the game dynamics depends on both the number of players and the upper limit. Second, a lottery with a large sensitivity parameter divides the players into winner(s) and losers. Third, finding the “stick” rule immediately makes a player a winner and imitating behavior is not observed in four-player games.

Keywords

Agent-based computational economics Learning Multi-player and multi-strategy game 

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Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan

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