Economics at your fingertips  

Strategy-Proof Aggregation of Approximate and Imprecise Judgments

Marcello Basili, Ernesto Savaglio () and Stefano Vannucci ()

Department of Economics University of Siena from Department of Economics, University of Siena

Abstract: The present work is devoted to the study of aggregation rules for several types of approximate judgments and their strategy-proofness properties when the relevant judgment space is lattice-ordered and endowed with a natural metric, and the agents/experts have single-peaked preferences consistent with it. In particular, approximate probability estimates as modeled by intervals of probability values, numerical measurements with explicit error bounds, approximate classifications, and conditional judgments that are amenable to composition by means of a set of logical connectives are considered. Relying on (bounded) distributivity of the relevant lattices, we prove the existence of a large class of inclusive and unanimity-respecting strategy-proof aggregation rules for approximate assessments or conditional judgments, consisting of sup-projections and sup-inf polynomials as parameterized by certain families of locally winning coalitions called committees. Amongst them, the majority aggregation rule is characterized as the only one that ensures both anonymity (i.e. an equal treatment of agents) and bi-idempotence (i.e. a definite choice between the only two judgments nominated by a maximally polarized body).

JEL-codes: D71 D81 (search for similar items in EconPapers)
Date: 2021-11
New Economics Papers: this item is included in nep-des and nep-mic
References: Add references at CitEc

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Department of Economics University of Siena from Department of Economics, University of Siena Contact information at EDIRC.
Bibliographic data for series maintained by Fabrizio Becatti ().

Page updated 2024-06-07
Handle: RePEc:usi:wpaper:864