Proceedings of the Annual Meeting of the Cognitive Science Society

Social normative decision-making involves two evaluative axes: immediate gains from aligning with others (reputation, conformity, reduced friction) and delayed collective consequences accumulating through repeated actions (social loss, victimization). This study examines, via a multi-agent simulation with Instance-Based Learning Theory (IBLT) agents, how this tension shapes norm formation, bifurcation, and stabilization. Agents repeatedly choose between two actions (pull/keep) inspired by the trolley problem. In each round, they receive a short-term reward proportional to the degree of agreement with neighbors, while at fixed block intervals they receive a delayed penalty depending on the total number of victims. We compare dynamics on a lattice Grid with dynamics on networks generated by the Watts–Strogatz model and classify trajectories into three types (pull-dominant, intermediate, keep-dominant). As a result, the prevalence of these types differs by network structure, suggesting that network differences affect how the two rewards interact and thereby change transition dynamics.