Dr. Jason Marden
University of California, Santa Barbara
Seminar Information
How should one optimally allocate limited resources when facing strategic competitors with imperfect information? This fundamental question underlies many scenarios, from protecting critical infrastructure to deploying cybersecurity resources. We examine this through extensions of the Colonel Blotto game - a classical model where two players compete by distributing limited resources across multiple battlefields. While traditional analysis assumes perfect information and independent battlefield valuations, modern applications demand more nuanced models. This talk presents two key developments: First, we analyze how asymmetric information about opponent capabilities shapes optimal strategies and performance guarantees. Second, we introduce "networked Blotto games" where battlefield values are interdependent rather than isolated. Our results characterize optimal allocation strategies under these more realistic conditions and demonstrate how network structure and information jointly determine equilibrium behavior.
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 as an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason is an IEEE Fellow (2024) and recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), AFOSR Young Investigator Award (2012), American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015). Jason's research interests focus on game theoretic methods for the control of distributed multiagent systems.