Judgment Aggregation

Endriss, Ulle; Moulin, Hervé

doi:10.1017/cbo9781107446984.018

Cited by 54 publications

(48 citation statements)

References 41 publications

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“…The technique we have used to prove it is also used in Chapter 2 (Zwicker, 2016) on voting theory and in Chapter 17 (Endriss, 2016) on judgment aggregation. These chapters also discuss possible approaches for dealing with such impossibilities by weakening our requirements somewhat.…”

Section: Classical Social Choice Theorymentioning

confidence: 99%

Introduction to Computational Social Choice

Brandt

Conitzer

Endriss

et al. 2016

Handbook of Computational Social Choice

Self Cite

345

376

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Section: Classical Social Choice Theorymentioning

confidence: 99%

Introduction to Computational Social Choice

Brandt

Conitzer

Endriss

et al. 2016

Handbook of Computational Social Choice

Self Cite

345

376

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“…Obviously, the rules differ in the richness of information each member is required to impute. In addition, they also differ in the complexity of the aggregation of imputed information (Bettencourt, 2009;Endriss et al, 2012). We argue that this complexity may affect the relationship between the information asymmetry and deception.…”

Section: Theory and Hypothesesmentioning

confidence: 91%

Manipulative Imputation in Distributed Decision Support Settings: The Implications of Information Asymmetry and Aggregation Complexity

Malekovic¹,

Sutanto

Goutas

2016

Decision Support Systems

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According to earlier research, distributed decision support structures are susceptible to deception. We complement the existing works by analyzing group members' attempts to manipulate group decisions supported by distributed communications. Experimentally, we manipulated two systemic features of a distributed support structure: the members' information asymmetry and decision rule complexity. Both of these features refer to structural properties of aggregated information exchange. We confirmed several hypothesized effects: An increase in the information asymmetry in the aggregation of information increases the incidence of the members' manipulative tendency. It also increases the effectiveness of the members' manipulative imputations. However, the complexity of a decision rule negatively moderates both of these effects. We point out the theoretical relevance and managerial implications of our findings. We conclude that managing team members' information asymmetry and complexity of issues under their practical consideration may result in valuable disclosures.

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“…By making high-frequency collective decisions possible, the development of voting avatars will be confronted with a large and interconnected space of alternative choices. This would require compact representations for voters' views and preferences, such as those developed in the area of combinatorial voting (Lang and Xia, 2016), as well as a detailed understanding of the counterintuitive results and paradoxical situations that arise when aggregating them, such as those settings analysed by the theory of judgment aggregation (Endriss, 2016).…”

Section: Multiagent Systems and Artificial Intelligencementioning

confidence: 99%

Agent-Mediated Social Choice

Grandi

2019

The Future of Economic Design

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Direct democracy is often proposed as a possible solution to the 21st-century problems of democracy. However, this suggestion clashes with the size and complexity of 21st-century societies, entailing an excessive cognitive burden on voters, who would have to submit informed opinions on an excessive number of issues. In this paper I argue for the development of "voting avatars", autonomous agents debating and voting on behalf of each citizen. Theoretical research from artificial intelligence, and in particular multiagent systems and computational social choice, proposes 21st-century techniques for this purpose, from the compact representation of a voter's preferences and values, to the development of voting procedures for autonomous agents use only.

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Judgment Aggregation

Cited by 54 publications

References 41 publications

Introduction to Computational Social Choice

Introduction to Computational Social Choice

Manipulative Imputation in Distributed Decision Support Settings: The Implications of Information Asymmetry and Aggregation Complexity

Agent-Mediated Social Choice

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