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Volume 11, Issue 1-2June 2023
Editor:
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
ISSN:2167-8375
EISSN:2167-8383
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research-article
Catastrophe by Design in Population Games: A Mechanism to Destabilize Inefficient Locked-in Technologies
Article No.: 1, Pages 1–36https://doi.org/10.1145/3583782

In multi-agent environments in which coordination is desirable, the history of play often causes lock-in at sub-optimal outcomes. Notoriously, technologies with significant environmental footprint or high social cost persist despite the successful ...

research-article
The Price of Anarchy of Two-Buyer Sequential Multiunit Auctions
Article No.: 2, Pages 1–26https://doi.org/10.1145/3584864

We study the efficiency of first-/second-price sequential multiunit auctions with two buyers and complete information. Extending the primal-dual framework for obtaining efficiency bounds to this sequential setting, we obtain tight price of anarchy bounds. ...

research-article
A Framework for Computing the Nucleolus via Dynamic Programming
Article No.: 3, Pages 1–21https://doi.org/10.1145/3580375

This article defines a general class of cooperative games for which the nucleolus is efficiently computable. This class includes new members for which the complexity of computing their nucleolus was not previously known. We show that when the minimum ...

research-article
Reaching Individually Stable Coalition Structures
Article No.: 4, Pages 1–65https://doi.org/10.1145/3588753

The formal study of coalition formation in multi-agent systems is typically realized in the framework of hedonic games, which originate from economic theory. The main focus of this branch of research has been on the existence and the computational ...

research-article
A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts
Article No.: 5, Pages 1–23https://doi.org/10.1145/3580374

The past few years have seen several works exploring learning economic solutions from data, including optimal auction design, function optimization, stable payoffs in cooperative games, and more. In this work, we provide a unified learning-theoretic ...

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