May 20, 2021   11:00 AM — 12:00 PM

Vivek Borkar Department of Electrical Engineering, Indian Institute of Technology Bombay, India

Vivek Borkar

Presentation on YouTube

In this talk, I introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively \(\alpha\)-homogeneous rewards. Its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. Thus the limiting differential equation that it tracks coincides with the forward Kolmogorov equation for the latter, which in turn is a scaled version of a special instance of replicator dynamics with potential. This equivalence is exploited to show that for \(\alpha > 0\), the asymptotic outcome concentrates around the optimum in a certain limiting sense when 'annealed' by letting \( \alpha \uparrow \infty \) slowly. (Joint work with Konstantin Avrachenkov, Sharayu Moharir and Suhail Mohmad Shah.)

Georges Zaccour organizer
Can Baris Cetin organizer
Utsav Sadana organizer


Online meeting
Montréal Québec

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