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Public policies that supply public goods, especially those involvecollaboration by limiting individual liberty, always give rise to controversiesover governance legitimacy. Multi-Agent Reinforcement Learning (MARL) methodsare appropriate for supporting the legitimacy of the public policies thatsupply public goods at the cost of individual interests. Among these policies,the inter-regional collaborative pandemic control is a prominent example, whichhas become much more important for an increasingly inter-connected world facinga global pandemic like COVID-19. Different patterns of collaborative strategieshave been observed among different systems of regions, yet it lacks ananalytical process to reason for the legitimacy of those strategies. In thispaper, we use the inter-regional collaboration for pandemic control as anexample to demonstrate the necessity of MARL in reasoning, and therebylegitimizing policies enforcing such inter-regional collaboration. Experimentalresults in an exemplary environment show that our MARL approach is able todemonstrate the effectiveness and necessity of restrictions on individualliberty for collaborative supply of public goods. Different optimal policiesare learned by our MARL agents under different collaboration levels, whichchange in an interpretable pattern of collaboration that helps to balance thelosses suffered by regions of different types, and consequently promotes theoverall welfare. Meanwhile, policies learned with higher collaboration levelsyield higher global rewards, which illustrates the benefit of, and thusprovides a novel justification for the legitimacy of, promoting inter-regionalcollaboration. Therefore, our method shows the capability of MARL incomputationally modeling and supporting the theory of calculus of consent,developed by Nobel Prize winner J. M. Buchanan.

Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods