Thibaut Horel , Yaron Singer: In the first part of this thesis we show the limitations of algorithmic mechanism design. Efficiency-Revenue Trade-offs in Auctions. Shahar Dobzinski , Christos H. WWW Companion Volume
Christos Papadimitriou BibTeX citation: Distributed Computation of Complex Contagion in Networks. Limitations and Possibilities of Algorithmic Mechanism Design. Shahar Dobzinski , Christos H. Approximation Guarantees for Adaptive Sampling.
Equilibrium in Combinatorial Public Projects.
We introduce a novel class of problems where the bottleneck for implementation is the constraint on payments. Skip to main content.
Robust Classification of Financial Risk. Learning on a budget: By resulting to approximations, this result circumvents well known impossibility results from classical mechanism design theory that deem incentive compatibility to be infeasible under a budget. Pricing Tasks in Online Labor Markets. In the second part of this thesis we show the possibilities of algorithmic mechanism design.
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Yuval ShavittYaron Singer: Elchanan MosselChristos H. Silvio LattanziYaron Singer: Efficiency-Revenue Trade-Offs in Auctions. Mechanisms for complement-free procurement.
Approximation Guarantees for Adaptive Sampling.
Lior Seeman ayron, Yaron Singer: Minimizing a Submodular Function from Samples. The adaptive complexity of maximizing a submodular function.
Inapproximability of Combinatorial Public Projects. SIGecom Exchanges 12 2: Shahar DobzinskiChristos H. On the Hardness of Being Truthful.
How to win friends and influence people, sibger The Importance of Communities for Learning to Influence. The theory, known as algorithmic mechanism design, builds on the foundations of classical mechanism design from microeconomics and is based on the idea of incentive compatible protocols. Adaptive Seeding in Social Networks.
Ashwinkumar BadanidiyuruChristos H. Budget feasible mechanism design. Yaron SingerManas Mittal: Robust Guarantees of Stochastic Greedy Algorithms.
We show that for a broad class of these problems, there are incentive compatible mechanisms with desirable approximation guarantees that do not require overpayments. In the first part of this thesis we show the limitations of algorithmic mechanism design. The Power of Optimization from Samples. Sharon QianYaron Singer: We introduce a novel class of problems which are approximable in the absence of strategic constraints, and have an optimal incentive compatible solution when no computational constraints are enforced; we show that, under standard computational assumptions, for this class of problems there is no algorithm with a reasonable approximation ratio that is both computationally feasible and yxron compatible.
Incentives, Computation, and Networks: