Algorithms for strategyproof classification

Meir, Reshef; Procaccia, Ariel D.; Rosenschein, Jeffrey S.

doi:10.1016/j.artint.2012.03.008

Cited by 62 publications

(51 citation statements)

References 27 publications

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“…As a corollary, we get the first lower approximation bound on discrete facility location and show it to be linear in the number of agents. This result also entails a similar lower bound for realizable SP classification problems, thereby showing that the negative result of Meir et al [2012] still applies under particular important restrictions.…”

Section: Our Contributionmentioning

confidence: 66%

“…It is therefore important to clarify the necessity of the continuity assumption. Meir et al [2011;2012] studied characterizations and approximation bounds for the strategyproof classification problem. While this mat at first seem quite unrelated to the problem at hand, a variation of their model (which they call the "realizable" setting) is in fact equivalent to facility location on subgraphs of the binary cube.…”

Section: Previous Workmentioning

confidence: 99%

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Mechanism design on discrete lines and cycles

Dokow

Feldman

Meir

et al. 2012

Proceedings of the 13th ACM Conference on Electronic Commerce

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We study strategyproof (SP) mechanisms for the location of a facility on a discrete graph. We give a full characterization of SP mechanisms on lines and on sufficiently large cycles. Interestingly, the characterization deviates from the one given by Schummer and Vohra [2004] for the continuous case. In particular, it is shown that an SP mechanism on a cycle is close to dictatorial, but all agents can affect the outcome, in contrast to the continuous case. Our characterization is also used to derive a lower bound on the approximation ratio with respect to the social cost that can be achieved by an SP mechanism on certain graphs. Finally, we show how the representation of such graphs as subsets of the binary cube reveals common properties of SP mechanisms and enables one to extend the lower bound to related domains.

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Section: Our Contributionmentioning

confidence: 66%

Section: Previous Workmentioning

confidence: 99%

Mechanism design on discrete lines and cycles

Dokow

Feldman

Meir

et al. 2012

Proceedings of the 13th ACM Conference on Electronic Commerce

Self Cite

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“…The origins of the agenda of approximate mechanism design without money can be traced to the paper of Dekel et al [19] on incentive compatible learning, a line of work that was followed up in recent papers [42,43]. It turns out that the study of incentives in general learning-theoretic domains reduces to simpler settings where strategyproof approximation mechanisms without money can be designed.…”

Section: Related Workmentioning

confidence: 99%

Approximate Mechanism Design without Money

Procaccia

Tennenholtz

2013

ACM Trans. Econ. Comput.

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191

335

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The literature on algorithmic mechanism design is mostly concerned with game-theoretic versions of optimization problems to which standard economic money-based mechanisms cannot be applied efficiently. Recent years have seen the design of various truthful approximation mechanisms that rely on payments. In this paper, we advocate the reconsideration of highly structured optimization problems in the context of mechanism design. We explicitly argue for the first time that, in such domains, approximation can be leveraged to obtain truthfulness without resorting to payments. This stands in contrast to previous work where payments are almost ubiquitous, and (more often than not) approximation is a necessary evil that is required to circumvent computational complexity.We present a case study in approximate mechanism design without money. In our basic setting agents are located on the real line and the mechanism must select the location of a public facility; the cost of an agent is its distance to the facility. We establish tight upper and lower bounds for the approximation ratio given by strategyproof mechanisms without payments, with respect to both deterministic and randomized mechanisms, under two objective functions: the social cost, and the maximum cost. We then extend our results in two natural directions: a domain where two facilities must be located, and a domain where each agent controls multiple locations.

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“…The intersection of Machine Learning and Mechanism Design is an active research area which includes work in various topics such as online mechanisms [35], dynamic auctions [13,4], dynamic pricing [46], secretary problems [21], offline learning from self-interested data sources [10,37] and a number of others. A more detailed review of this area, or any of the topics listed above, is beyond the scope of this paper.…”

Section: Additional Related Workmentioning

confidence: 99%

Characterizing Truthful Multi-armed Bandit Mechanisms

2014

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We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare.If the advertisers bid their true private values, our problem is equivalent to the multi-armed bandit problem, and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by regret, the difference in social welfare between the algorithm and the benchmark which always selects the same "best" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that deterministic truthful mechanisms have certain strong structural properties -essentially, they must separate exploration from exploitation -and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret. General Terms: theory, algorithms, economics. Keywords: mechanism design, truthful mechanisms, single-parameter auctions, pay-per-click auctions, multi-armed bandits, regret. * This is a full version of a conference paper published in 10th ACM Conf. on Electronic Commerce (EC), 2009. Apart from the revised presentation, this version is updated to reflect the follow-up work [9,56,22,48] and the current snapshot of open questions. ACM Categories and subject descriptors: F.2.2 [Analysis of Algorithms and Problem

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Algorithms for strategyproof classification

Cited by 62 publications

References 27 publications

Mechanism design on discrete lines and cycles

Mechanism design on discrete lines and cycles

Approximate Mechanism Design without Money

Characterizing Truthful Multi-armed Bandit Mechanisms

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