How to turn an MAS into a graphical causal model

摘要

This paper proposes that an appropriately configured multi-agent system (MAS) is formally equivalent to a graphical causal model (GCM, a broad category that includes many formalisms expressed as directed graphs), and offers benefits over other GCMs in modeling a social scenario. MASs often use GCMs to support their operation, but are not usually viewed as tools for enhancing their execution. We argue that the definition of a GCM should include its update mechanism, an often-overlooked component. We review a wide range of GCMs to validate this definition and point out limitations that they face when applied to the social and psychological dimensions of causality. Then we describe Social Causality using Agents with Multiple Perspectives (SCAMP), a causal language and multi-agent simulator that satisfies our definition and overcomes the limitations of other GCMs for social simulation.