Maximal domain for strategy-proof probabilistic rules in economies with one public good

Shuhei Morimoto ()

Social Choice and Welfare, 2013, vol. 41, issue 3, 637-669

Abstract: We consider the problem of choosing a level of a public good on an interval of the real line among a group of agents. A probabilistic rule chooses a probability distribution over the interval for each preference profile. We investigate strategy-proof probabilistic rules in the case where distributions are compared based on stochastic dominance relations. First, on a “minimally rich domain”, we characterize the so-called probabilistic generalized median rules (Ehlers et al., J Econ Theory 105:408–434, 2002 ) by means of stochastic-dominance (sd) strategy-proofness and ontoness. Next, we study how much we can enlarge a domain to allow for the existence of sd-strategy-proof probabilistic rules that satisfy ontoness and the no-vetoer condition. We establish that the domain of “convex” preferences is the unique maximal domain including a minimally rich domain for these properties. Copyright Springer-Verlag Berlin Heidelberg 2013

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00355-012-0700-4 (text/html)
Access to full text is restricted to subscribers.

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: https://EconPapers.repec.org/RePEc:spr:sochwe:v:41:y:2013:i:3:p:637-669

Ordering information: This journal article can be ordered from
http://www.springer. ... c+theory/journal/355

DOI: 10.1007/s00355-012-0700-4

Access Statistics for this article

Social Choice and Welfare is currently edited by Bhaskar Dutta, Marc Fleurbaey, Elizabeth Maggie Penn and Clemens Puppe

More articles in Social Choice and Welfare from Springer, The Society for Social Choice and Welfare Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().