The combined effects of conformity and reinforcement learning on the evolution of cooperation in public goods games

The human social instinct makes people susceptible to social influences, notably the urge to conform. Conformity enables individuals to attain psychological balance, thereby facilitating the smooth functioning of society. Individuals can also adjust their behavioral strategies based on their personal experiences. Individuals who learn by reinforcement can acquire optimal behaviors through feedback from their social environment. Reinforcement learning allows individuals to adapt their behavioral strategy based on their experiences to maximize payoffs. In this paper, we investigate how cooperation evolves in a population by constructing public goods games that integrate the conformity mechanism and the Bush–Mosteller reinforcement-learning rule. In particular, we use the “free rider” characteristic of noise traders in economics to introduce the conformity mechanism. The population is divided into two types of individuals: noise traders and noise-contrarian traders. We introduce a weighting parameter λ to represent the level of an individual’s conformity. The results indicate that increasing the density of noise traders promotes cooperation. Furthermore, an optimal conformity weighting maximizes the cooperation level. Finally, dynamic evolution indicators, including time series, snapshots, and the expected probability distribution for cooperation, are provided to explain the results.