Graph-constrained dynamic choice
Dynamic Games and Applications Seminar
Speaker: 聽鈥� Department of Electrical Engineering, IIT Bombay,聽India
Webinar ID: 881 6070 4034
Passcode: 095916
In this talk, I introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively聽伪-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聽伪>0, the asymptotic outcome concentrates around the optimum in a certain limiting sense when 'annealed' by letting聽伪鈫戔垶听蝉濒辞飞濒测.听
(Joint work with Konstantin Avrachenkov, Sharayu Moharir and Suhail Mohmad Shah.)
