Stochastic evolution in populations of ideas

Robin Nicole*, Peter Sollich, Tobias Galla

*Corresponding author for this work

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4 Citations (Scopus)
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It is known that learning of players who interact in a repeated game can be interpreted as an evolutionary process in a population of ideas. These analogies have so far mostly been established in deterministic models, and memory loss in learning has been seen to act similarly to mutation in evolution. We here propose a representation of reinforcement learning as a stochastic process in finite â € populations of ideas'. The resulting birth-death dynamics has absorbing states and allows for the extinction or fixation of ideas, marking a key difference to mutation-selection processes in finite populations. We characterize the outcome of evolution in populations of ideas for several classes of symmetric and asymmetric games.

Original languageEnglish
Article number40580
Number of pages15
JournalScientific Reports
Issue number40580
Publication statusPublished - 18 Jan 2017


  • Evolutionary dynamics
  • Reinforcement learning

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