Decaying active probing enables exact finite-step recovery of the interaction matrix in stable controllable linear-quadratic network games, with a reweighted sparse estimator achieving almost-sure consistency and finite-time support recovery under perturbations.
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Bounded-rational agents on networks reach higher stationary coordination probability when connectivity is uniform, with the probability increasing in rationality parameter beta and the partition function approximable by a Gaussian moment-generating function to show regular networks are optimal.
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Revealing Strategic Interactions in Network Games Under Decaying Active Probing
Decaying active probing enables exact finite-step recovery of the interaction matrix in stable controllable linear-quadratic network games, with a reweighted sparse estimator achieving almost-sure consistency and finite-time support recovery under perturbations.
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Learning to Coordinate over Networks with Bounded Rationality
Bounded-rational agents on networks reach higher stationary coordination probability when connectivity is uniform, with the probability increasing in rationality parameter beta and the partition function approximable by a Gaussian moment-generating function to show regular networks are optimal.