Arbeitspapier
Approximate Bayesian Implementation and Exact Maxmin Implementation: An Equivalence
This paper provides a micro-foundation for approximate incentive compatibility using ambiguity aversion. In particular, we propose a novel notion of approximate interim incentive compatibility, approximate local incentive compatibility, and establish an equivalence between approximate local incentive compatibility in a Bayesian environment and exact interim incentive compatibility in the presence of a small degree of ambiguity. We then apply our result to the implementation of efficient allocations. In particular, we identify three economic settings – including ones in which approximately efficient allocations are implementable, ones in which agents are informationally small, and large double auctions – in which efficient allocations are approximately locally implementable when agents are Bayesian. Applying our result to those settings, we conclude that efficient allocations are exactly implementable when agents perceive a small degree of ambiguity.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 362
- Klassifikation
-
Wirtschaft
- Thema
-
approximate local incentive compatibility
ambiguity aversion
efficiency
informational size
modified VCG mechanism
double auction
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Song, Yangwei
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition
- (wo)
-
München und Berlin
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Song, Yangwei
- Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition
Entstanden
- 2022
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