Emergence of cooperation in trust-driven coevolutionary networks

Understanding how cooperation emerges and persists among self-interested individuals remains a central challenge in evolutionary game theory. While previous studies demonstrate that adaptive networks can promote cooperation through dynamic link rewiring, most models rely on instantaneous payoff comparisons or external labels and often overlook the role of long-term trust dynamics. Here, we propose a trust-adaptive network (TAN) model that integrates strategy evolution, asymmetric trust dynamics, and adaptive network restructuring in an evolutionary prisoner’s dilemma. Pairwise trust evolves with asymmetric updating rates, building slowly but collapsing rapidly—mirroring negativity bias observed in empirical human social behavior. Social ties are severed only when accumulated trust falls below a tolerance threshold. Rewiring follows socially plausible triadic closure, while long-term isolated nodes can re-enter via conditional reintegration. Numerical simulations across diverse complex network topologies show that this mechanism promotes the recovery of high levels of cooperation across a broad range of parameter settings, including relatively strong temptation to defect. During evolution, networks undergo trust-driven structural reorganization, converging into highly resilient, sparse cooperative backbones composed of high-trust links. Further micro-level dynamics and ablation analyses reveal a dynamical sequence underlying cooperation recovery: trust collapse, structural isolation, strategy adjustment, and conditional reintegration. These findings highlight trust not merely as a byproduct of interactions, but as a dynamic, mediating force linking behavioral adaptation to topological restructuring. This provides a dynamical framework for understanding the resilience of cooperation in realistic social dilemmas.