Normative Agents for Real-World Scenarios

Norms are an important part of human social systems, governing many aspects of group decision-making. Yet many popularly used social models neglect to model normative effects on human behavior, relying on simple probabilistic and majority voting models of influence diffusion. Within the multi-agent research community, the study of norm emergence, compliance, and adoption has resulted in new architectures and standards for normative agents; however few of these models have been successfully applied to real-world public policy problems. During our research we introduced a new hybrid architecture, Cognitive Social Learners (CSL), that models bottomup norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. Our proposed cognitive architecture includes the interaction between rational thought, reward-based learning, and contagious social behaviors. The future plan is to employ this architecture for constructing normative agents to model human social systems; the aim of our research is to be able to study the effects of different public policy decisions on a community and studying the emergence of norms in real-world cases.