20.500.12556/DKUM-91120 Local and global stimuli in reinforcement learning In efforts to resolve social dilemmas, reinforcement learning is an alternative to imitation and exploration in evolutionary game theory. While imitation and exploration rely on the performance of neighbors, in reinforcement learning individuals alter their strategies based on their own performance in the past. For example, according to the Bush-Mosteller model of reinforcement learning, an individual's strategy choice is driven by whether the received payoff satisfies a preset aspiration or not. Stimuli also play a key role in reinforcement learning in that they can determine whether a strategy should be kept or not. Here we use the Monte Carlo method to study pattern formation and phase transitions towards cooperation in social dilemmas that are driven by reinforcement learning. We distinguish local and global players according to the source of the stimulus they experience. While global players receive their stimuli from the whole neighborhood, local players focus solely on individual performance. We show that global players play a decisive role in ensuring cooperation, while local players fail in this regard, although both types of players show properties of "moody cooperators". In particular, global players evoke stronger conditional cooperation in their neighborhoods based on direct reciprocity, which is rooted in the emerging spatial patterns and stronger interfaces around cooperative clusters. evolutionary game theory cooperation learning social physics evolucijska teorija iger sodelovanje učenje fizika družbe true false true Angleški jezik Slovenski jezik Znanstveno delo 2024-11-08 03:07:26 2025-03-03 11:02:59 2025-03-04 03:11:39 0000-00-00 00:00:00 2021 0 0 str. 1-11 št. 8 Letn. 23 Avg. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-06-16 2021-07-22 2021-08-10 53 72946691 10.1088/1367-2630/ac170a 1367-2630 RAZ_Jia_Danyang_2021.pdf RAZ_Jia_Danyang_2021.pdf 1 82E810B122D2160F483816E364A4F9B5 3e78fbe16ac2693ae8f2e26040d21fc7f36d44d5a6a8a999e730c5269e63ceb7 9c794ddb-f816-11ef-aa8e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188938 https://iopscience.iop.org/article/10.1088/1367-2630/ac170a 0 a89e58f8-f816-11ef-aa8e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188939 Fakulteta za naravoslovje in matematiko 0 0 0