Enhancing Democratic Mediation through Norm-Awareness in Generative Agent Societies

Democratic mediation serves as a vital mechanism for resolving social conflicts; however, current practices encounter three critical limitations: (1) inefficient operations, wherein traditional laborintensive mediation processes are both time-consuming and inefficient; (2) theoretical gaps, as prevailing mediation theories fail to explore the underlying causes of conflicts; and (3) inadequate analysis, with existing digital tools lacking comprehensive conflict mediation capabilities and primarily focusing on singular data types. To address these limitations, we introduce the Normative Social Simulator for Democratic Mediation, referred to as Norm Mediat. This framework is specifically designed to simulate democratic mediation, incorporating social norms. Central to this framework is the integration of normative reasoning into the mediation process, which enhances the ability to understand individuals' intrinsic needs and identify the root causes of conflicts. The framework comprises two essential components: (1) Dynamic Multimodal Conflict Modeling (DMCM), which generates the initial dataset of conflict interactions; and (2) Norm-Aware Iterative Mediation (NAIM), which implements an iterative democratic mediation process through norm awareness. The results of our human evaluation underscore the effectiveness of our norm-driven mediation strategies. This research significantly contributes to computational social science by providing a comprehensive methodological framework for simulating democratic processes and offering a benchmark dataset for conflict resolution studies.