摘要：This talk considers a system with multiple devices that make repeated decisions based on their own observed events. The events and decisions at each time step determine the values of a utility function and a collection of penalty functions. In the first part of the talk, the goal is to make distributed decisions over time to maximize time average utility subject to time average constraints on the penalties. An example is a collection of power constrained sensors that repeatedly report their own observations to a fusion center. Maximum utility is fundamentally reduced because devices do not know the events observed by others. Optimality is characterized for this distributed context. It is shown that optimality is achieved by correlating device decisions through a commonly known pseudorandom sequence. An optimal algorithm is developed that chooses pure strategies at each time step based on a set of time-varying weights. 

In the second part of the talk, a related problem is cast in a dynamic game setting.  Devices decide whether or not to share information, and will only do so if such sharing does not sacrifice their competitive advantage. Standard Nash equilibrium concepts are inadequate in this scenario. Instead, a new "no regret" goal is introduced and solved for arbitrary event sequences and arbitrary human decision sequences.

Papers on these topics are found here:
http://ee.usc.edu/stochastic-nets/docs/distributed-optimization-ton.pdf
http://arxiv.org/abs/1412.8736

个人简介：Michael J. Neely received B.S. degrees in both Electrical Engineering and Mathematics from the University of Maryland, College Park, in 1997. He was then awarded a 3 year Department of Defense NDSEG Fellowship for graduate study at the Massachusetts Institute of Technology, where he received an M.S. degree in 1999 and a Ph.D. in 2003, both in Electrical Engineering. He joined the faculty of Electrical Engineering at the University of Southern California in 2004, where he is currently an Associate Professor. His research interests are in the areas of stochastic network optimization and queueing theory, with applications to wireless networks, mobile ad-hoc networks, and switching systems. Michael received the NSF Career award in 2008, the Viterbi School of Engineering Junior Research Award in 2009, and the Okawa Foundation Research Grant Award in 2012. He is a member of Tau Beta Pi and Phi Beta Kappa.