Work

Dynamic adverse selection with the best and the worst in mind

Year: 2025

Type: article

Abstract: This paper analyzes a dynamic adverse selection market where buyers hold ambiguous beliefs about seller quality, modeled using neo-additive capacities. We investigate how market outcomes diverge from the standard Bayesian benchmark when buyers update their non-additive beliefs using an alternative update rule, optimistic or pessimistic. We find that the pessimistic rule consistently amplifies adverse selection relative to the Bayesian case by depressing willingness-to-pay, while the optimistic rule mitigates it by maintaining higher valuations. The analysis explores how baseline ambiguity, ambiguity aversion, information specificity, and buyer risk aversion interact with these two updating rules to shape market participation, efficiency, and the severity of adverse selection over time. The potential dynamic inconsistency inherent in the optimistic and pessimistic rules is highlighted as a key driver of these distinct market dynamics, offering a behavioral perspective on market persistence and unraveling under Knightian uncertainty.

Source: Mathematical Social Sciences

DOI: 10.1016/j.mathsocsci.2025.102490

Author: Pascal Toquebeuf

Institution: Laboratoire d'Economie Appliquée de Grenoble

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,850

Funder: Université Grenoble Alpes