title: Stochastic evolution in populations of ideas.
abstract: 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 '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.
authors.affiliation: Department of Mathematics, King's College London, Strand, London, WC2R 2LS, United Kingdom.
authors.forename: Robin
authors.initials: R
authors.lastname: Nicole
authors.affiliation: Department of Mathematics, King's College London, Strand, London, WC2R 2LS, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Sollich
authors.affiliation: Theoretical Physics, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, United Kingdom.
authors.forename: Tobias
authors.initials: T
authors.lastname: Galla