We present a framework for a class of sequential decision-making problems in the context of max-min bi-level programming, where a leader and a follower repeatedly interact. At each period, the leader allocates resources to disrupt the performance of the follower (e.g., as in defender-attacker or interdiction problems), who in turn minimizes some cost function over a set of activities that depends on the leader’s decision.
Dr. Prokopyev will be discussing ‘Sequential Interdiction with Incomplete Information and Learning’ on the June 19th. Graduate students have an opportunity to meet with our distinguished speaker before the seminar.
We will discuss a few examples from healthcare, where quantitative models embedded in decision-support tools can improve the quality of decision-making (for patients, physicians, or caregivers) and patient outcomes.