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- W4382936253 abstract "We consider network games where a large number of agents interact according to a network sampled from a random network model, represented by a graphon. By exploiting previous results on convergence of such large network games to graphon games, we examine a procedure for estimating unknown payoff parameters, from observations of equilibrium actions, without the need for exact network information. We prove smoothness and local convexity of the optimization problem involved in computing the proposed estimator. Additionally, under a notion of graphon parameter identifiability, we show that the optimal estimator is globally unique. We present several examples of identifiable homogeneous and heterogeneous parameters in different classes of linear quadratic network games with numerical simulations to validate the proposed estimator." @default.
- W4382936253 created "2023-07-04" @default.
- W4382936253 creator A5033257687 @default.
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- W4382936253 date "2023-05-31" @default.
- W4382936253 modified "2023-10-14" @default.
- W4382936253 title "Estimation of Unknown Payoff Parameters in Large Network Games" @default.
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- W4382936253 doi "https://doi.org/10.23919/acc55779.2023.10155917" @default.
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