Deception in Nash Equilibrium Seeking
Michael Tang, Umar Javed, Xudong Chen, Miroslav Krstic, Jorge I. Poveda

TL;DR
This paper introduces model-free algorithms for deception in Nash equilibrium seeking within multi-agent systems, demonstrating how a deceiver can manipulate victim actions to achieve advantageous outcomes while ensuring stability.
Contribution
It presents novel model-free deceptive algorithms with stability guarantees for Nash equilibrium seeking in asymmetric information games, including real-time management of deception strategies.
Findings
Deceptive Nash equilibria can be stabilized using integral feedback.
Deception can lead to increased profits for the deceiver in duopoly models.
Conditions for successful and mutual deception are established.
Abstract
In socio-technical multi-agent systems, deception exploits privileged information to induce false beliefs in "victims," keeping them oblivious and leading to outcomes detrimental to them or advantageous to the deceiver. We consider model-free Nash-equilibrium-seeking for non-cooperative games with asymmetric information and introduce model-free deceptive algorithms with stability guarantees. In the simplest algorithm, the deceiver includes in his action policy the victim's exploration signal, with an amplitude tuned by an integrator of the regulation error between the deceiver's actual and desired payoff. The integral feedback drives the deceiver's payoff to the payoff's reference value, while the victim is led to adopt a suboptimal action, at which the pseudogradient of the deceiver's payoff is zero. The deceiver's and victim's actions turn out to constitute a "deceptive" Nash…
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Taxonomy
TopicsAuction Theory and Applications · Game Theory and Applications
