Evolution of cooperation facilitated by reinforcement learning with adaptive aspiration levels

Shoma Tanabe, Naoki Masuda*


研究成果: Article査読

23 被引用数 (Scopus)


Repeated interaction between individuals is the main mechanism for maintaining cooperation in social dilemma situations. Variants of tit-for-tat (repeating the previous action of the opponent) and the win-stay lose-shift strategy are known as strong competitors in iterated social dilemma games. On the other hand, real repeated interaction generally allows plasticity (i.e., learning) of individuals based on the experience of the past. Although plasticity is relevant to various biological phenomena, its role in repeated social dilemma games is relatively unexplored. In particular, if experience-based learning plays a key role in promotion and maintenance of cooperation, learners should evolve in the contest with nonlearners under selection pressure. By modeling players using a simple reinforcement learning model, we numerically show that learning enables the evolution of cooperation. We also show that numerically estimated adaptive dynamics appositely predict the outcome of evolutionary simulations. The analysis of the adaptive dynamics enables us to capture the obtained results as an affirmative example of the Baldwin effect, where learning accelerates the evolution to optimality.

ジャーナルJournal of Theoretical Biology
出版ステータスPublished - 2012 1月 21

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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