title: Exploration dynamics in evolutionary games.
abstract: Evolutionary game theory describes systems where individual success is based on the interaction with others. We consider a system in which players unconditionally imitate more successful strategies but sometimes also explore the available strategies at random. Most research has focused on how strategies spread via genetic reproduction or cultural imitation, but random exploration of the available set of strategies has received less attention so far. In genetic settings, the latter corresponds to mutations in the DNA, whereas in cultural evolution, it describes individuals experimenting with new behaviors. Genetic mutations typically occur with very small probabilities, but random exploration of available strategies in behavioral experiments is common. We term this phenomenon "exploration dynamics" to contrast it with the traditional focus on imitation. As an illustrative example of the emerging evolutionary dynamics, we consider a public goods game with cooperators and defectors and add punishers and the option to abstain from the enterprise in further scenarios. For small mutation rates, cooperation (and punishment) is possible only if interactions are voluntary, whereas moderate mutation rates can lead to high levels of cooperation even in compulsory public goods games. This phenomenon is investigated through numerical simulations and analytical approximations.
authors.affiliation: Max Planck Institute for Evolutionary Biology, D-24306 Plön, Germany. traulsen@evolbio.mpg.de
authors.forename: Arne
authors.initials: A
authors.lastname: Traulsen
authors.forename: Christoph
authors.initials: C
authors.lastname: Hauert
authors.forename: Hannelore
authors.initials: H
authors.lastname: De Silva
authors.forename: Martin A
authors.initials: MA
authors.lastname: Nowak
authors.forename: Karl
authors.initials: K
authors.lastname: Sigmund
mesh_terms: D005075:Biological Evolution; D003299:Cooperative Behavior; D005716:Game Theory; D006801:Humans; D009154:Mutation; D011336:Probability; D011678:Punishment