Computational Resilience in Human Reciprocity: An Asymmetric Intrinsic Prosocial Bias for Sustaining Cooperation under Exogenous Uncertainty

Abstract

In direct reciprocity, environmentally imposed exogenous uncertainty frequently decouples benefactors altruistic intentions from final outcomes. How do cooperative bonds remain resilient against this inevitable "noise"? Adopting a comprehensive levels-of-analysis framework across eight experiments, we integrate interpersonal paradigms, computational modeling, multivariate fMRI decoding, and third-party social evaluations to uncover a robust "asymmetric adjustment" in beneficiarys affective evaluation and reciprocity under exogenous uncertainty, systematically delineating its cognitive algorithmic rules, neural representations, and adaptive functions in sustaining human cooperation. Specifically, beneficiarys reciprocity amplifies for positive deviations from expectations while buffering against reciprocal reductions from unintended shortfalls. Modeling reveals this computational resilience is governed by an asymmetric intrinsic prosocial bias, extending beyond traditional frameworks of outcome/intention-based evaluation, prediction-error-based social learning, or loss aversion. Supported by theory-of-mind system, this "asymmetric adjustment" is perceived as morally superior to other calculations, thereby serving a critical adaptive function to sustain cooperation amidst the volatility of an uncertain world.