Search Results for author: Yannai A. Gonczarowski

Found 8 papers, 0 papers with code

Algorithmic Collusion by Large Language Models

no code implementations31 Mar 2024 Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer

The rise of algorithmic pricing raises concerns of algorithmic collusion.

Common Knowledge, Regained

no code implementations7 Nov 2023 Yannai A. Gonczarowski, Yoram Moses

We demonstrate its usefulness by deriving for it an agreement theorem \`a la Aumann (1976), showing it arises in the setting of Geanakoplos and Polemarchakis (1982) with timing frictions added, and applying it to characterize equilibrium behavior in a dynamic coordination game.

The Distortion of Binomial Voting Defies Expectation

no code implementations NeurIPS 2023 Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang

In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome.

Zero-Knowledge Mechanisms

no code implementations11 Feb 2023 Ran Canetti, Amos Fiat, Yannai A. Gonczarowski

Commitment is achieved by public declaration, which enables players to verify incentive properties in advance and the outcome in retrospect.

Strategyproofness-Exposing Mechanism Descriptions

no code implementations27 Sep 2022 Yannai A. Gonczarowski, Ori Heffetz, Clayton Thomas

A menu description presents a mechanism to player $i$ in two steps.

The Complexity of Interactively Learning a Stable Matching by Trial and Error

no code implementations18 Feb 2020 Ehsan Emamjomeh-Zadeh, Yannai A. Gonczarowski, David Kempe

In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable.


The Sample Complexity of Up-to-$\varepsilon$ Multi-Dimensional Revenue Maximization

no code implementations7 Aug 2018 Yannai A. Gonczarowski, S. Matthew Weinberg

We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multi-dimensional settings.

Computational Efficiency

Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues

no code implementations NeurIPS 2017 Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff

In this work we derive a variant of the classic Glivenko-Cantelli Theorem, which asserts uniform convergence of the empirical Cumulative Distribution Function (CDF) to the CDF of the underlying distribution.

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