
Regular Potential Games
A fundamental problem with the Nash equilibrium concept is the existence...
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Learning and Selfconfirming Equilibria in Network Games
Consider a set of agents who play a network game repeatedly. Agents may ...
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Learning Nash Equilibria in Monotone Games
We consider multiagent decision making where each agent's cost function...
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Resolving Implicit Coordination in MultiAgent Deep Reinforcement Learning with Deep QNetworks Game Theory
We address two major challenges of implicit coordination in multiagent ...
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Rational Consensus
We provide a gametheoretic analysis of consensus, assuming that process...
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Competitive Safety Analysis: Robust DecisionMaking in MultiAgent Systems
Much work in AI deals with the selection of proper actions in a given (k...
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Imitation dynamics in population games on community networks
We study the asymptotic behavior of deterministic, continuoustime imita...
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The Nash Equilibrium with Inertia in Population Games
In the traditional gametheoretic set up, where agents select actions and experience corresponding utilities, an equilibrium is a configuration where no agent can improve their utility by unilaterally switching to a different action. In this work, we introduce the novel notion of inertial Nash equilibrium to account for the fact that, in many practical situations, action changes do not come for free. Specifically, we consider a population game and introduce the coefficients c_ij describing the cost an agent incurs by switching from action i to action j. We define an inertial Nash equilibrium as a distribution over the action space where no agent benefits in moving to a different action, while taking into account the cost of this change. First, we show that the set of inertial Nash equilibria contains all the Nash equilibria, but is in general not convex. Second, we argue that classical algorithms for computing Nash equilibria cannot be used in the presence of switching costs. We then propose a natural betterresponse dynamics and prove its convergence to an inertial Nash equilibrium. We apply our results to predict the drivers' distribution of an ondemand ridehailing platform.
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