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Learning enables adaptation in cooperation for multi-player stochastic games
Authors:Feng Huang  Ming Cao  Long Wang
Affiliation:1.Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People’s Republic of China;2.Center for Data Science and System Complexity, Faculty of Science and Engineering, University of Groningen, Groningen 9747 AG, The Netherlands
Abstract:
Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g. the ‘tragedy of the commons’), modulate their behaviours to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Using evolutionary game theory, we develop a framework of stochastic games that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviours can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favoured over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social–ecological dilemmas, learning enables the adaptation of individuals to varying environments.
Keywords:reinforcement learning   evolutionary game theory   stochastic game   adaptive behaviour   social dilemma
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