21 research outputs found
Prior-Independent Mechanisms for Scheduling
We study the makespan minimization problem with unrelated selfish machines
under the assumption that job sizes are stochastic. We design simple truthful
mechanisms that under various distributional assumptions provide constant and
sublogarithmic approximations to expected makespan. Our mechanisms are
prior-independent in that they do not rely on knowledge of the job size
distributions. Prior-independent approximation mechanisms have been previously
studied for the objective of revenue maximization [Dhangwatnotai, Roughgarden
and Yan'10, Devanur, Hartline, Karlin and Nguyen'11, Roughgarden, Talgam-Cohen
and Yan'12]. In contrast to our results, in prior-free settings no truthful
anonymous deterministic mechanism for the makespan objective can provide a
sublinear approximation [Ashlagi, Dobzinski and Lavi'09].Comment: This paper will appear in Proceedings of the ACM Symposium on Theory
of Computing 2013 (STOC'13
07261 Abstracts Collection -- Fair Division
From 24.06. to 29.06.2007, the Dagstuhl Seminar 07261 % generate automatically
``Fair Division\u27\u27 % generate automatically
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Envy, Regret, and Social Welfare Loss
Incentive compatibility (IC) is a desirable property for any auction mechanism, including those used in online advertising. However, in real world applications practical constraints and complex environments often result in mechanisms that lack incentive compatibility. Recently, several papers investigated the problem of deploying black-box statistical tests to determine if an auction mechanism is incentive compatible by using the notion of IC-Regret that measures the regret of a truthful bidder. Unfortunately, most of those methods are computationally intensive, since they require the execution of many counterfactual experiments. In this work, we show that similar results can be obtained using the notion of IC-Envy. The advantage of IC-Envy is its efficiency: it can be computed using only the auction's outcome. In particular, we focus on position auctions. For position auctions, we show that for a large class of pricing schemes (which includes e.g. VCG and GSP), IC-Envy ≥ IC-Regret (and IC-Envy = IC-Regret under mild supplementary conditions). Our theoretical results are completed showing that, in the position auction environment, IC-Envy can be used to bound the loss in social welfare due to the advertiser untruthful behavior. Finally, we show experimentally that IC-Envy can be used as a feature to predict IC-Regret in settings not covered by the theoretical results. In particular, using IC-Envy yields better results than training models using only price and value features
Packing, Scheduling and Covering Problems in a Game-Theoretic Perspective
Many packing, scheduling and covering problems that were previously
considered by computer science literature in the context of various
transportation and production problems, appear also suitable for describing and
modeling various fundamental aspects in networks optimization such as routing,
resource allocation, congestion control, etc. Various combinatorial problems
were already studied from the game theoretic standpoint, and we attempt to
complement to this body of research.
Specifically, we consider the bin packing problem both in the classic and
parametric versions, the job scheduling problem and the machine covering
problem in various machine models. We suggest new interpretations of such
problems in the context of modern networks and study these problems from a game
theoretic perspective by modeling them as games, and then concerning various
game theoretic concepts in these games by combining tools from game theory and
the traditional combinatorial optimization. In the framework of this research
we introduce and study models that were not considered before, and also improve
upon previously known results.Comment: PhD thesi
Multi-Agent Systems for Computational Economics and Finance
In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artificial intelligence, and economic theory. In particular, we focus on the design and analysis of mechanisms for systems involving multiple strategic agents, both from a theoretical and an applied perspective. We present an overview of our group’s activities, as well as its members, and then discuss in detail past, present, and future work in multi-agent systems