Evolutionary Dynamics of Population Games With an Aspiration-Based Learning Rule

Wang, Qiang and Chen, Xiaojie and He, Nanrong and Szolnoki, Attila (2025) Evolutionary Dynamics of Population Games With an Aspiration-Based Learning Rule. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 36 (5). pp. 8387-8400. ISSN 2162-237X

Text 2024IEEE-TNNLS-Evolutionarydynamics.pdf - Published Version Restricted to Registered users only Download (850kB) | Request a copy

Abstract

Agents usually adjust their strategic behaviors based on their own payoff and aspiration in gaming environments. Hence, aspiration-based learning rules play an important role in the evolutionary dynamics in a population of competing agents. However, there exist different options for how to use the aspiration information for specifying the microscopic learning rules. It is also interesting to investigate under what conditions the aspiration-based learning rules can favor the emergence of cooperative behavior in population games. A new learning rule, called as “Satisfied-Cooperate, Unsatisfied-Defect,” is proposed here, which is based on aspiration. Under this learning rule, agents prefer to cooperate when their income is satisfied; otherwise, they prefer the strategy of defection. We introduce this learning rule to a population of agents playing a generalized two-person game. We, respectively, obtain the mathematical conditions in which cooperation is more abundant in finite wellmixed, infinite well-mixed, and structured populations under weak selection. Interestingly, we find that these conditions are identical, no matter whether the aspiration levels for cooperators and defectors are the same or not. Furthermore, we consider the prisoner’s dilemma game (PDG) as an example and perform numerical calculations and computer simulations. Our numerical and simulation results agree well and both support our theoretical predictions in the three different types of populations. We further find that our aspiration-based learning rule can promote cooperation more effectively than alternative aspiration-based learning rules in the PDG.

Actions (login required)

Edit Item