TY - JOUR
T1 - Deception in Nash Equilibrium Seeking
AU - Tang, Michael
AU - Javed, Umar
AU - Chen, Xudong
AU - Krstic, Miroslav
AU - Poveda, Jorge I.
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - In socio-technical multiagent 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 noncooperative 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 equilibrium of a different game, whose structure is managed—in real time—by the deceiver. We examine quadratic, aggregative, and more general games and provide conditions for a successful deception, mutual, and benevolent deception, and immunity to deception (for a “nongeneric” set of payoff functions). Stability results are established using techniques based on averaging and singular perturbations. Among the examples in this article is a microeconomic duopoly in which the deceiver induces in the victim a belief that the buyers disfavor the deceiver more than they actually do, leading the victim to increase the price above the Nash price, and resulting in an increased profit for the deceiver and a decreased profit for the victim. A study of the deceiver’s integral feedback for the desired profit reveals that, in duopolies with equal marginal costs, a deceiver that is greedy for very high profit can attain any such profit, and pursue this with arbitrarily high integral gain (impatiently), irrespective of the market preference for the victim.
AB - In socio-technical multiagent 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 noncooperative 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 equilibrium of a different game, whose structure is managed—in real time—by the deceiver. We examine quadratic, aggregative, and more general games and provide conditions for a successful deception, mutual, and benevolent deception, and immunity to deception (for a “nongeneric” set of payoff functions). Stability results are established using techniques based on averaging and singular perturbations. Among the examples in this article is a microeconomic duopoly in which the deceiver induces in the victim a belief that the buyers disfavor the deceiver more than they actually do, leading the victim to increase the price above the Nash price, and resulting in an increased profit for the deceiver and a decreased profit for the victim. A study of the deceiver’s integral feedback for the desired profit reveals that, in duopolies with equal marginal costs, a deceiver that is greedy for very high profit can attain any such profit, and pursue this with arbitrarily high integral gain (impatiently), irrespective of the market preference for the victim.
KW - Extremum seeking
KW - Nash equilibria
KW - deception
KW - learning
KW - noncooperative games
UR - https://www.scopus.com/pages/publications/105009371566
U2 - 10.1109/TAC.2025.3582524
DO - 10.1109/TAC.2025.3582524
M3 - Article
AN - SCOPUS:105009371566
SN - 0018-9286
VL - 70
SP - 7984
EP - 7999
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 12
ER -