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Recent Advances in Colonel Blotto Games and the Connection to Controls

Jason R. Marden – Professor, UCSB

Thu, 10-Nov-2022 / 4:00pm / Packard 202

Abstract

How should a system operator strategically allocate its assets in an adversarial environment? In this talk, we explore this question in the context of the well-studied Colonel Blotto game. Colonel Blotto games model strategic scenarios where two opposing entities are tasked with allocating a given number of assets over a collection of fronts. The classical setting has primarily focused on scenarios where each front is associated with an independent valuation and each entity seeks to maximize the cumulative performance over the fronts. This talk will focus on emerging variants of these Colonel Blotto games encompassing both informational asymmetries and alternative structures for the classic linear sum objectives. One such variant we will focus on is a “networked” Colonel Blotto game, where the valuation of a front now depends on the outcome associated with other fronts in the system.

Bio

Jason R. Marden is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason is a recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), the AFOSR Young Investigator Award (2012), the American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015). Furthermore, Jason is also an advisor for the students selected as finalists for the best student paper award at the IEEE Conference on Decision and Control (2011, 2016, 2017) and American Control Conference (2020). Jason’s research interests focus on game theoretic methods for the control of distributed multiagent systems.