Jia F, Mate A, Li Z, Jabbari S, Chakraborty M, Tambe M, Wellman M, Vorobeychik Y. A Game-Theoretic Approach for Hierarchical Policy-Making. nd International (Virtual) Workshop on Autonomous Agents for Social Good (AASG 2021). 2021. Publisher's VersionAbstract
We present the design and analysis of a multi-level game-theoretic model of hierarchical policy-making, inspired by policy responses to the COVID-19 pandemic. Our model captures the potentially mismatched priorities among a hierarchy of policy-makers (e.g., federal, state, and local governments) with respect to two main cost components that have opposite dependence on the policy strength, such as post-intervention infection rates and the cost of policy implementation. Our model further includes a crucial third fac- tor in decisions: a cost of non-compliance with the policy-maker immediately above in the hierarchy, such as non-compliance of state with federal policies. Our first contribution is a closed-form approximation of a recently published agent-based model to com- pute the number of infections for any implemented policy. Second, we present a novel equilibrium selection criterion that addresses common issues with equilibrium multiplicity in our setting. Third, we propose a hierarchical algorithm based on best response dynamics for computing an approximate equilibrium of the hierarchical policy-making game consistent with our solution concept. Finally, we present an empirical investigation of equilibrium policy strategies in this game as a function of game parameters, such as the degree of centralization and disagreements about policy priorities among the agents, the extent of free riding as well as fairness in the distribution of costs.
Biswas A, Mukherjee S. Ensuring Fairness under Prior Probability Shifts, in AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021). ; 2021. arXiv
Saksono H. Transformative-fair AI for Addressing the Societal Origins of Marginalization, in ; 2021. saksono-2021-transformative_fair_ai_for_addressing_the_societal_origins_of_marginalization.pdf
Perrault A, Fang F, Sinha A, Tambe M. AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline. AI Magazine. 2020.Abstract
With the maturing of AI and multiagent systems research, we have a tremendous
opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important
role in fighting social injustice and improving society.