7,146 research outputs found
Honest signaling in zero-sum games is hard, and lying is even harder
We prove that, assuming the exponential time hypothesis, finding an
\epsilon-approximately optimal symmetric signaling scheme in a two-player
zero-sum game requires quasi-polynomial time. This is tight by [Cheng et al.,
FOCS'15] and resolves an open question of [Dughmi, FOCS'14]. We also prove that
finding a multiplicative approximation is NP-hard.
We also introduce a new model where a dishonest signaler may publicly commit
to use one scheme, but post signals according to a different scheme. For this
model, we prove that even finding a (1-2^{-n})-approximately optimal scheme is
NP-hard
Imitative Follower Deception in Stackelberg Games
Information uncertainty is one of the major challenges facing applications of
game theory. In the context of Stackelberg games, various approaches have been
proposed to deal with the leader's incomplete knowledge about the follower's
payoffs, typically by gathering information from the leader's interaction with
the follower. Unfortunately, these approaches rely crucially on the assumption
that the follower will not strategically exploit this information asymmetry,
i.e., the follower behaves truthfully during the interaction according to their
actual payoffs. As we show in this paper, the follower may have strong
incentives to deceitfully imitate the behavior of a different follower type
and, in doing this, benefit significantly from inducing the leader into
choosing a highly suboptimal strategy. This raises a fundamental question: how
to design a leader strategy in the presence of a deceitful follower? To answer
this question, we put forward a basic model of Stackelberg games with
(imitative) follower deception and show that the leader is indeed able to
reduce the loss due to follower deception with carefully designed policies. We
then provide a systematic study of the problem of computing the optimal leader
policy and draw a relatively complete picture of the complexity landscape;
essentially matching positive and negative complexity results are provided for
natural variants of the model. Our intractability results are in sharp contrast
to the situation with no deception, where the leader's optimal strategy can be
computed in polynomial time, and thus illustrate the intrinsic difficulty of
handling follower deception. Through simulations we also examine the benefit of
considering follower deception in randomly generated games
Learning to Play Bayesian Games
This paper discusses the implications of learning theory for the analysis of Bayesian games. One goal is to illuminate the issues that arise when modeling situations where players are learning about the distribution of Nature's move as well as learning about the opponents' strategies. A second goal is to argue that quite restrictive assumptions are necessary to justify the concept of Nash equilibrium without a common prior as a steady state of a learning process.
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