Truthful Mechanism Design via Correlated Tree Rounding

Azar, Yossi; Hoefer, Martin; Maor, Idan; Reiffenhäuser, Rebecca; Vöcking, Berthold

doi:10.1145/2764468.2764503

Cited by 8 publications

(22 citation statements)

References 49 publications

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“…Our approach is to formulate a mathematical optimization problem for R n . This approach was not common until recently when several successful truthful or truthful in expectation mechanisms have been constructed using linear or nonlinear programs [2,3,7,15]. This paper continues the trend to combine optimization with mechanism design and has the following contributions:…”

Section: Introduction and Main Resultsmentioning

confidence: 93%

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New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

Kuryatnikova

Vera

2019

Theory Comput Syst

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We consider the minimum makespan problem for n tasks and two unrelated parallel selfish machines. Let R n be the best approximation ratio of randomized monotone scale-free algorithms. This class contains the most efficient algorithms known for truthful scheduling on two machines. We propose a new M in − M ax formulation for R n , as well as upper and lower bounds on R n based on this formulation. For the lower bound, we exploit pointwise approximations of cumulative distribution functions (CDFs). For the upper bound, we construct randomized algorithms using distributions with piecewise rational CDFs. Our method improves upon the existing bounds on R n for small n. In particular, we obtain almost tight bounds for n = 2 showing that |R 2 − 1.505996| < 10 −6 .

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Section: Introduction and Main Resultsmentioning

confidence: 93%

“…The minimum makespan problem is one of many optimization problems considered in algorithmic mechanism design. These include, among others, combinatorial auctions (see, e.g., [2], [6] and references therein) and graph theoretic problems, such as the shortest paths tree [11] and the maximum matching problem [27].…”

Section: Introduction and Main Resultsmentioning

confidence: 99%

New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

Kuryatnikova

Vera

2019

Theory Comput Syst

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“…In recent years, tools of mechanism design have been widely used in the field of computer science to overcome various problems (e.g., privacy, pricing methods and enforcement of truthful information revelation) in modern markets [116,156,13]. In this thesis, I suggest different mechanisms, each sophisticatedly designed for a specific type of market.…”

Section: List Of Tablesmentioning

confidence: 99%

“…I show that when using the Two-Part Tariff with any of the agents, for any expected-profit maximizing pair (c sub , c click > 0) there is necessarily an expected-profit maximizing pair (c sub , c click = 0) that guarantees a greater expected profit for the platform, i.e., V T woP art (S , c sub , c click ) > V T woP art (S , c sub , 0). 13 The expected profit with (3.15)).…”

Section: Advanced Service Schemesmentioning

confidence: 99%

Intelligent Mechanisms for Platforms in Modern Markets

Hajaj¹

2019

Preprint

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This thesis explores the use of autonomous agents serving as mediators between different entities in modern markets to maximize an expected revenue (e.g., the agents' own profit or users' social welfare). This mediation can take many forms and serve many purposes, such as buyers and sellers at online shopping environments, men and women at dating sites, and different traders at barter markets. These agents differ from one another in terms such as the cost of their mediation services (e.g, they can offer their services for a fee or complimentary). In the first case, when the services are costly, agents must ponder the terms they set for their services. Similarly, when offering their services for free they have to be as appealing as possible in order to overcome external competition. This thesis provides theoretically-justified and empirically-validated approaches for designing and operating intelligent mechanisms for mediation agents in both electronic and barter markets. While two of the mechanisms presented within this thesis guide the platform for how to use selective information disclosure for its benefit, a different one is designed specifically in order to avoid selective information disclosure to the platform. The research is based on both theoretical analysis and online validation of suggested heuristics. Theoretical analysis is carried out using concepts from search theory, and game theory and the online validation is based on Amazon Mechanical Turk, a well-known crowdsourcing platform imitating people's behavior in real-life.The models used in this thesis consider settings of autonomous, fully-rational, and self-interested agents interacting with both fully-rational agents and bounded-rational people, and how the difference in their decision-making processes may imply differently, yet correlated, strategies for the system designer. The different model variants are applicable and can be mapped onto various different real-life applications (e.g., online shopping, car purchasing, and barter markets). The models differ primarily in the assumptions they make. In electronic markets, assumptions relating to the agents' source of income and the users' computational capabilities (fully-rational agents vs. bounded-rational people). For barter markets, this thesis focuses on fully-rational agents that differ in the number of goods they hold and can be traded on the market. Based on the common model for these markets, this thesis provides a proof of impossibility result on the ability to enforce the agents' strategy-proof behavior regardless of the means used. Still, the thesis shows that by adding a set of real-life assumptions on the model, one can circumvent this impossibility result and validate that the truthful strategy is indeed the dominant one. The analysis is accompanied by extensive simulations and online experiments, making use of various testbeds. The simulations and online experiments are used to validate the applicability of the suggested mechanisms for modern markets and to the different entities populating them.

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“…Designing truthful mechanisms using the framework of Lavi and Swamy for a given problem is straightforward, however, this type of mechanisms is slow in practice and requires many black-box invocations of an existing approximation algorithm for the problem. Very recently, Azar et al [2015] present a truthful-in-expectation 1/2-approximation algorithm for GAP with private values using the framework proposed by Lavi and Swamy and a new rounding technique.…”

Section: Introductionmentioning

confidence: 99%

A Truthful Mechanism for the Generalized Assignment Problem

Fadaei

Bichler

2017

ACM Trans. Econ. Comput.

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We propose a truthful-in-expectation, (1 − 1/e)-approximation mechanism for a strategic variant of the generalized assignment problem (GAP). In GAP, a set of items has to be optimally assigned to a set of bins without exceeding the capacity of any singular bin. In the strategic variant of the problem we study, values for assigning items to bins are the private information of bidders and the mechanism should provide bidders with incentives to truthfully report their values. The approximation ratio of the mechanism is a significant improvement over the approximation ratio of the existing truthful mechanism for GAP.The proposed mechanism comprises a novel convex optimization program as the allocation rule as well as an appropriate payment rule. To implement the convex program in polynomial time, we propose a fractional local search algorithm which approximates the optimal solution within an arbitrarily small error leading to an approximately truthful-in-expectation mechanism. The presented algorithm improves upon the existing optimization algorithms for GAP in terms of simplicity and runtime while the approximation ratio closely matches the best approximation ratio given for GAP when all inputs are publicly known.

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Truthful Mechanism Design via Correlated Tree Rounding

Cited by 8 publications

References 49 publications

New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

Intelligent Mechanisms for Platforms in Modern Markets

A Truthful Mechanism for the Generalized Assignment Problem

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