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Journal Articles IEEE Transactions on Control of Network Systems Year : 2022

Dynamic social learning under graph constraints

Abstract

We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards. We show that 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. We use this equivalence to show that for $\alpha > 0$, the asymptotic outcome concentrates around the optimum in a certain limiting sense when 'annealed' by letting $\alpha \to \infty$ slowly.
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Dates and versions

hal-03462479 , version 1 (01-12-2021)

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Cite

Konstantin Avrachenkov, Vivek S Borkar, Sharayu Moharir, Suhail Mohmad Shah. Dynamic social learning under graph constraints. IEEE Transactions on Control of Network Systems, 2022, 9 (3), pp.1435-1446. ⟨10.1109/TCNS.2021.3114377⟩. ⟨hal-03462479⟩
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