Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated Prisoner's dilemma

Naoki Masuda*, Mitsuhiro Nakamura

*この研究の対応する著者

研究成果: Article査読

19 被引用数 (Scopus)

抄録

Humans and other animals can adapt their social behavior in response to environmental cues including the feedback obtained through experience. Nevertheless, the effects of the experience-based learning of players in evolution and maintenance of cooperation in social dilemma games remain relatively unclear. Some previous literature showed that mutual cooperation of learning players is difficult or requires a sophisticated learning model. In the context of the iterated Prisoner's dilemma, we numerically examine the performance of a reinforcement learning model. Our model modifies those of Karandikar et al. (1998), Posch et al. (1999), and Macy and Flache (2002) in which players satisfice if the obtained payoff is larger than a dynamic threshold. We show that players obeying the modified learning mutually cooperate with high probability if the dynamics of threshold is not too fast and the association between the reinforcement signal and the action in the next round is sufficiently strong. The learning players also perform efficiently against the reactive strategy. In evolutionary dynamics, they can invade a population of players adopting simpler but competitive strategies. Our version of the reinforcement learning model does not complicate the previous model and is sufficiently simple yet flexible. It may serve to explore the relationships between learning and evolution in social dilemma situations.

本文言語English
ページ(範囲)55-62
ページ数8
ジャーナルJournal of Theoretical Biology
278
1
DOI
出版ステータスPublished - 2011 6月 7
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • モデリングとシミュレーション
  • 生化学、遺伝学、分子生物学(全般)
  • 免疫学および微生物学(全般)
  • 農業および生物科学(全般)
  • 応用数学

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