Nash Equilibrium of Social-Learning Agents in a Restless Multiarmed Bandit Game

@inproceedings{Nakayama2017NashEO,
  title={Nash Equilibrium of Social-Learning Agents in a Restless Multiarmed Bandit Game},
  author={Kazuaki Nakayama and Masato Hisakado and Shintaro Mori},
  booktitle={Scientific Reports},
  year={2017}
}
We study a simple model for social-learning agents in a restless multiarmed bandit (rMAB). The bandit has one good arm that changes to a bad one with a certain probability. Each agent stochastically selects one of the two methods, random search (individual learning) or copying information from other agents (social learning), using which he/she seeks the good arm. Fitness of an agent is the probability to know the good arm in the steady state of the agent system. In this model, we explicitly… CONTINUE READING
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