93 research outputs found
Utility Design for Distributed Resource Allocation -- Part I: Characterizing and Optimizing the Exact Price of Anarchy
Game theory has emerged as a fruitful paradigm for the design of networked
multiagent systems. A fundamental component of this approach is the design of
agents' utility functions so that their self-interested maximization results in
a desirable collective behavior. In this work we focus on a well-studied class
of distributed resource allocation problems where each agent is requested to
select a subset of resources with the goal of optimizing a given system-level
objective. Our core contribution is the development of a novel framework to
tightly characterize the worst case performance of any resulting Nash
equilibrium (price of anarchy) as a function of the chosen agents' utility
functions. Leveraging this result, we identify how to design such utilities so
as to optimize the price of anarchy through a tractable linear program. This
provides us with a priori performance certificates applicable to any existing
learning algorithm capable of driving the system to an equilibrium. Part II of
this work specializes these results to submodular and supermodular objectives,
discusses the complexity of computing Nash equilibria, and provides multiple
illustrations of the theoretical findings.Comment: 15 pages, 5 figure
Characterizing the interplay between information and strength in Blotto games
In this paper, we investigate informational asymmetries in the Colonel Blotto
game, a game-theoretic model of competitive resource allocation between two
players over a set of battlefields. The battlefield valuations are subject to
randomness. One of the two players knows the valuations with certainty. The
other knows only a distribution on the battlefield realizations. However, the
informed player has fewer resources to allocate. We characterize unique
equilibrium payoffs in a two battlefield setup of the Colonel Blotto game. We
then focus on a three battlefield setup in the General Lotto game, a popular
variant of the Colonel Blotto game. We characterize the unique equilibrium
payoffs and mixed equilibrium strategies. We quantify the value of information
- the difference in equilibrium payoff between the asymmetric information game
and complete information game. We find information strictly improves the
informed player's performance guarantee. However, the magnitude of improvement
varies with the informed player's strength as well as the game parameters. Our
analysis highlights the interplay between strength and information in
adversarial environments.Comment: 8 pages, 2 figures. Accepted for presentation at 58th Conference on
Decision and Control (CDC), 201
The Anarchy-Stability Tradeoff in Congestion Games
This work focuses on the design of incentive mechanisms in congestion games,
a commonly studied model for competitive resource sharing. While the majority
of the existing literature on this topic focuses on unilaterally optimizing the
worst case performance (i.e., price of anarchy), in this manuscript we
investigate whether optimizing for the worst case has consequences on the best
case performance (i.e., price of stability). Perhaps surprisingly, our results
show that there is a fundamental tradeoff between these two measures of
performance. Our main result provides a characterization of this tradeoff in
terms of upper and lower bounds on the Pareto frontier between the price of
anarchy and the price of stability. Interestingly, we demonstrate that the
mechanism that optimizes the price of anarchy inherits a matching price of
stability, thereby implying that the best equilibrium is not necessarily any
better than the worst equilibrium for such a design choice. Our results also
establish that, in several well-studied cases, the unincentivized setting does
not even lie on the Pareto frontier, and that any incentive with price of
stability equal to 1 incurs a much higher price of anarchy.Comment: 27 pages, 1 figure, 1 tabl
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