Reasoning About Other Agents: a Plea for Logic-Based Methods

Formal logic has become an invaluable tool for research on multi-agent systems, but it plays a minor role in the more applied field of agent-based social simulation (ABSS). We argue that logical languages are particularly useful for representing social meta-reasoning, that is, agents' reasoning about the reasoning of other agents. After arguing that social meta-reasoning is a frequent and important social phenomenon, we present a set of general criteria (functional completeness, understandability, changeability, and implementability/executability) to compare logic to two alternative formal methods: black box techniques (e.g., neural networks) and decision-theoretical models (e.g., game theory). We then argue that in terms of functional completeness, understandability and changeability, logical representations of social meta-reasoning compare favorably to these two alternatives.