
Optimal Price of Anarchy in CostSharing Games
The design of distributed algorithms is central to the study of multiage...
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Distributed Submodular Maximization with Parallel Execution
The submodular maximization problem is widely applicable in many enginee...
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Distributed resource allocation through utility design  Part I: optimizing the performance certificates via the price of anarchy
Game theory has emerged as a novel approach for the coordination of mult...
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When Smoothness is Not Enough: Toward Exact Quantification and Optimization of the PriceofAnarchy
Today's multiagent systems have grown too complex to rely on centralized...
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Distributed resource allocation through utility design  Part II: applications to submodular, supermodular and set covering problems
A fundamental component of the game theoretic approach to distributed co...
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Analysis of a Poissonpicking symmetric winnerstakeall game with randomized payoffs
Winnerstakeall situations introduce an incentive for agents to diversi...
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The Sharer's Dilemma in Collective Adaptive Systems of SelfInterested Agents
In collective adaptive systems (CAS), adaptation can be implemented by o...
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The Cost of Denied Observation in Multiagent Submodular Optimization
A popular formalism for multiagent control applies tools from game theory, casting a multiagent decision problem as a cooperationstyle game in which individual agents make local choices to optimize their own local utility functions in response to the observable choices made by other agents. When the systemlevel objective is submodular maximization, it is known that if every agent can observe the action choice of all other agents, then all Nash equilibria of a large class of resulting games are within a factor of 2 of optimal; that is, the price of anarchy is 1/2. However, little is known if agents cannot observe the action choices of other relevant agents. To study this, we extend the standard gametheoretic model to one in which a subset of agents either become blind (unable to observe others' choices) or isolated (blind, and also invisible to other agents), and we prove exact expressions for the price of anarchy as a function of the number of compromised agents. When k agents are compromised (in any combination of blind or isolated), we show that the price of anarchy for a large class of utility functions is exactly 1/(2+k). We then show that if agents use marginalcost utility functions and at least 1 of the compromised agents is blind (rather than isolated), the price of anarchy improves to 1/(1+k). We also provide simulation results demonstrating the effects of these observation denials in a dynamic setting.
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