Algorithmic Decision Theory: 8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings | SpringerLink
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Algorithmic Decision Theory

8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Rupert Freeman

    1. University of Virginia, Charlottesville, USA

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  2. Nicholas Mattei

    1. Tulane University, New Orleans, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15248)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ADT 2024.

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About this book

This book constitutes the conference proceedings of the 8th International Conference on Algorithmic Decision Theory, ADT 2024, held in New Brunswick, NJ, USA, during October 14-16, 2024.

The 18 full papers and 8 one-page abstracts presented were carefully selected from 39 submissions. The papers cover most of the major aspects of algorithmic decision theory, such as preference modeling and elicitation, voting, preference aggregation, fair division and resource allocation, coalition formation, game theory, and matching.
 

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Table of contents (18 papers)

  1. Front Matter

    Pages i-xi
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  2. Voting Theory

    1. Front Matter

      Pages 1-1
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    2. Complexity of Candidate Control for Single Nontransferable Vote and Bloc Voting

      • Garo Karh Bet, Jörg Rothe, Roman Zorn
      Pages 3-17

    3. Compiling the Votes of a Subelectorate for Multi-winner Voting Rules

      • Neel Karia, Jérôme Lang
      Pages 18-32

    4. On Approximately Strategy-Proof Tournament Rules for Collusions of Size at Least Three

      • David Mikšaník, Ariel Schvartzman, Jan Soukup
      Pages 33-47

    5. As Time Goes By: Adding a Temporal Dimension to Resolve Delegations in Liquid Democracy

      • Evangelos Markakis, Georgios Papasotiropoulos
      Pages 48-63

  3. Game Theory, Optimization, and Decision Making

    1. Front Matter

      Pages 65-65
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    2. Simple Stochastic Stopping Games: A Generator and Benchmark Library

      • Avi Rudich, Isaac Rudich, Rachel Rue
      Pages 67-81

    3. Protective and Nonprotective Subset Sum Games: A Parameterized Complexity Analysis

      • Jaroslav Garvardt, Christian Komusiewicz, Berthold Blatt Lorke, Jannik Schestag
      Pages 82-97

    4. Algorithmic Decision Analysis for Multi-stage Games with Incomplete Information

      • J. M. Camacho, Roi Naveiro, David Ríos Insua
      Pages 98-112

    5. Non-maximizing Policies that Fulfill Multi-criterion Aspirations in Expectation

      • Simon Dima, Simon Fischer, Jobst Heitzig, Joss Oliver
      Pages 113-127

    6. Adversarial Risk Analysis for Automated Lane-Changing in Heterogeneous Traffic

      • Roi Naveiro, David Ríos Insua, William N. Caballero
      Pages 128-143

    7. A Fully Bayesian Approach to Bilevel Problems

      • Vedat Dogan, Steven Prestwich, Barry O’Sullivan
      Pages 144-159

    8. Collaborative Information Dissemination with Graph-Based Multi-Agent Reinforcement Learning

      • Raffaele Galliera, Kristen Brent Venable, Matteo Bassani, Niranjan Suri
      Pages 160-173

    9. Toward Fair and Strategyproof Tournament Rules for Tournaments with Partially Transferable Utilities

      • David Pennock, Ariel Schvartzman, Eric Xue
      Pages 174-188

  4. Preference Theory

    1. Front Matter

      Pages 189-189
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    2. Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems

      • Margot Herin, Patrice Perny, Nataliya Sokolovska
      Pages 191-206

    3. Learning Multiple Multicriteria Additive Models from Heterogeneous Preferences

      • Vincent Auriau, Khaled Belahcène, Emmanuel Malherbe, Vincent Mousseau
      Pages 207-224

    4. Independent Relaxed Subproblems for Dominance Testing in CP-Nets

      • Liu Jiang, Thomas E. Allen
      Pages 225-240

  5. Allocations and Matching

    1. Front Matter

      Pages 241-241
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Other volumes

  1. Algorithmic Decision Theory

Editors and Affiliations

  • University of Virginia, Charlottesville, USA

    Rupert Freeman

  • Tulane University, New Orleans, USA

    Nicholas Mattei

Bibliographic Information

  • Book Title: Algorithmic Decision Theory

  • Book Subtitle: 8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings

  • Editors: Rupert Freeman, Nicholas Mattei

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-73903-3

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

  • Softcover ISBN: 978-3-031-73902-6Published: 16 October 2024

  • eBook ISBN: 978-3-031-73903-3Published: 15 October 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 302

  • Number of Illustrations: 13 b/w illustrations, 26 illustrations in colour

  • Topics: Artificial Intelligence, Computer Systems Organization and Communication Networks, Mathematics of Computing

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