Avinatan Hassidim , Yaron Singer: In the past decade, a theory of manipulation-robust algorithms has been emerging to address the challenges that frequently occur in strategic environments such as the internet. In the second part of this thesis we show the possibilities of algorithmic mechanism design. Papadimitriou , Yaron Singer: Papadimitriou , George Pierrakos , Yaron Singer: Elchanan Mossel , Christos H. Limitations and Possibilities of Algorithmic Mechanism Design.
Information-theoretic lower bounds for convex optimization with erroneous oracles. 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. We introduce a novel class of problems where the bottleneck for implementation is the constraint on payments. WWW Companion Volume Yaron Singer , Avinatan Hassidim:
PapadimitriouYaron Singer: Adaptive Seeding in Social Networks. How to win friends and influence people, truthfully: Harikrishna NarasimhanDavid C.
Learning on a budget: Learning to Optimize Combinatorial Functions. Shahar DobzinskiChristos H.
Skip to main content. Trading potatoes in distributed multi-tier routing systems. Robust Guarantees of Stochastic Greedy Algorithms. Mechanisms for complement-free procurement. Inapproximability of Combinatorial Public Projects.
WWW Companion Volume Influence maximization through adaptive seeding. Submodular Optimization under Noise. As it turns out, however, implementing incentive compatible thesls as advocated in classical mechanism design theory often necessitates solving intractable problems.
Mechanisms for Fair Attribution. Adaptive Seeding for Monotone Submodular Functions. Learning Diffusion using Hyperparameters.
Fast Parallel Algorithms for Feature Selection. Optimization for Approximate Submodularity. 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.
Such protocols achieve system-wide objectives through careful design that ensures it is in every agent’s best interest to comply with the protocol. In the first part of this thesis we show the limitations of algorithmic mechanism design.
Budget feasible mechanism design. The Limitations of Optimization from Samples. This settles the central open question in algorithmic mechanism design which, since its inception, has been focused on trying to show the hardness of polynomial time incentive compatibility.
The Power of Optimization from Samples. Pricing Tasks in Online Tgesis Markets. Limitations and Possibilities of Algorithmic Mechanism Design.
Shaddin Dughmi’s Homepage
We singfr 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 incentive compatible.
Thibaut HorelYaron Singer: Eric BalkanskiYaron Singer: To address this, algorithmic mechanism design focuses on designing computationally-feasible incentive compatible approximation algorithms. In the past decade, a theory of manipulation-robust algorithms has been emerging to address the challenges that frequently occur in strategic environments such as the internet.