Judgment Aggregation. Gabriella PigozziЧитать онлайн книгу.
Judgment Aggregation: A Primer
Synthesis Lectures on Artificial Intelligence and Machine Learning
Editors
Ronald J. Brachman, Yahoo! Labs
William W. Cohen, Carnegie Mellon University
Peter Stone, University of Texas at Austin
Judgment Aggregation: A Primer
Davide Grossi and Gabriella Pigozzi
2014
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Copyright © 2014 by Morgan & Claypool
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Judgment Aggregation: A Primer
Davide Grossi and Gabriella Pigozzi
www.morganclaypool.com
ISBN: 9781627050876 paperback
ISBN: 9781627050883 ebook
DOI 10.2200/S00559ED1V01Y201312AIM027
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Lecture #27
Series Editors: Ronald J. Brachman, Yahoo! Labs
William W. Cohen, Carnegie Mellon University
Peter Stone, University of Texas at Austin
Series ISSN
Synthesis Lectures on Artificial Intelligence and Machine Learning
Print 1939-4608 Electronic 1939-4616
Judgment Aggregation: A Primer
Davide Grossi
University of Liverpool
Gabriella Pigozzi
Université Paris Dauphine
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING #27
ABSTRACT
Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results,