Collective performance induced by social and individual learning in any population structure: An evolutionary game approach

Li, Zhifang and Zhang, Jingwei and Chen, Xiaojie and Szolnoki, Attila (2025) Collective performance induced by social and individual learning in any population structure: An evolutionary game approach. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE. ISSN 2691-4581 (In Press)

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Abstract

Collective decision-making is vital and widespread in human and artificial societies. Individuals often choose the option by assessing the intrinsic values of options in decisionmaking through individual learning. But they are also influenced by peer pressure and select the option by conformity-based social learning. A central question is whether the population can settle on the most beneficial option when social learning is involved. Previous studies concerning social learning focused on well-mixed populations where individuals are equally likely to interact with each other. But real social interactions are often more subtle that are modeled by a graph. Therefore it is challenging to theoretically analyze the effect of social learning on collective decision-making in structured populations. To address this issue, using evolutionary game theory we propose an evolutionary model of binary options jointly integrating individual and social learning in any population structure. We first derive the average fraction of the option with higher merit by means of coalescing random walks and find that the introduction of conformitybased social learning is detrimental to collective performance of decision-making. Interestingly, however, our theoretical analysis reveals that the majority of the population always favors the option with higher merit regardless of the preference of social learning. Importantly, these theoretical predictions are valid for any population structure and they are verified by intensive numerical simulations made in three representative static interaction structures. We further show that they hold in dynamic networks via computer simulations. We also demonstrate the robustness of our findings to different conformity-based social learning procedures.

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