20 mai 2021   11h00 — 12h00

Vivek Borkar Département de génie électrique, Indian Institute of Technology Bombay, Inde

Vivek Borkar

Présentation sur 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 responsable
Can Baris Cetin responsable
Utsav Sadana responsable


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