Abstract
Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems (MAS). Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to learn the norms existing in human society.
This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events mentioned in news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.