Interactive Restless Multiarmed Bandit Game and S

Authors: Shunsuke Yoshida Masato Hisakado Shintaro Mori

Publish Date: 2016/08/10

Volume: 34, Issue: 3, Pages: 291-306

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Abstract

We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multiarmed bandit rMAB A player competes with multiple agents Each bandit has a payoff that changes with a probability p c per round The agents and player choose one of three options 1 Exploit a good bandit 2 Innovate asocial learning for a good bandit among n I randomly chosen bandits and 3 Observe social learning for a good bandit Each agent has two parameters c p obs to specify the decision i c the threshold value for Exploit and ii p obs the probability for Observe in learning The parameters c p obs are uniformly distributed We determine the optimal strategies for the player using complete knowledge about the rMAB We show whether or not social or asocial learning is more optimal in the p c n I space and define the swarm intelligence effect We conduct a laboratory experiment 67 subjects and observe the swarm intelligence effect only if p c n I are chosen so that social learning is far more optimal than asocial learning

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