Modeling Tipping Point Theory Using Normative Multi-Agent Systems

Tipping points occur when a large number of group members radically modify their behaviors in response to small but significant events; after a critical point is reached, the behavior of the entire social system changes irrevocably. This paper proposes that normative multi-agent systems (NorMAS) can serve as excellent computational models for modeling and predicting tipping points. We illustrate how tipping point theory can be modeled with a standard social learning approach and replicate some of the key findings.