LIDS Seminar: Weina Wang

"A Local-to-Global Approach for Decision-Making in Large Stochastic Systems"

Large-scale decision-making problems arise in many domains, including communication networks, modern computing clusters, and ride-hailing systems. These systems involve tens of thousands of interacting components or agents, making policy design challenging as the complexity can grow exponentially with system size. In this talk, I will present a local-to-global framework that addresses this challenge. This framework first solves local single-component problems, and then carefully converts them into a global policy. I will illustrate this framework on weakly-coupled Markov decision processes, which include the classical restless bandit problem as a special case. The policies obtained through this framework break away from the traditionally dominant design of index or priority policies, and they are efficiently computable with provable order guarantees of asymptotic optimality. Notably, this local-to-global framework has allowed us to overcome a long-standing barrier in establishing such guarantees, as well as to address strong heterogeneity among agents, a particularly difficult challenge in modern large stochastic systems.