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Journal of Logic and Computation
Article . 2017 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2016
Data sources: DBLP
DBLP
Article . 2017
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MPG.PuRe
Article . 2017
Data sources: MPG.PuRe
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Pareto optimality and strategy-proofness in group argument evaluation

Authors: Awad, Edmond; Caminada, Martin; Pigozzi, Gabriella; Podlaszewski, Mikolaj; Rahwan, Iyad;

Pareto optimality and strategy-proofness in group argument evaluation

Abstract

An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy-proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents’ preferences.

Countries
France, United States
Keywords

Pareto optimality, QA75, Strategy-proofness, 330, Argumentation, Judgment aggregation, 006.3, Intelligence artificielle, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Top 10%
Green
bronze