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Is Imprecise Knowledge Better than Conflicting Expertise? Evidence from Insurers’ Decisions in the United States

Laure Cabantous (), Denis Hilton (), Howard Kunreuther () and Erwann Michel-Kerjan ()
Additional contact information
Laure Cabantous: Nottingham University Business School
Denis Hilton: CLLE, Universite Toulouse II-Le Mirail
Howard Kunreuther: Center for Risk Management and Decision Processes The Wharton School, University of Pennsylvania

ICBBR Working Papers from International Centre for Behavioural Business Research

Abstract: Testing whether risk professionals (here insurers) behave differently under risk and ambiguity when they cover catastrophic risks (floods and earthquakes) and non-catastrophic risks (fires), this paper reports the results of the first field experiment in the United States designed to distinguish two sources of ambiguity: imprecise ambiguity (outside experts agree on a range of probability, but not on any point estimate) versus conflict ambiguity (each expert group provides precise probability estimates which differ from one group to another). Insurers charge higher premiums when faced with ambiguity than when the probability of a loss is well specified. Furthermore they charge more for conflict ambiguity than imprecise ambiguity for flood and hurricane hazards, but less so in the case of fire. The source of ambiguity also impacts causal inferences insurers make to reduce their uncertainty.

Keywords: Ambiguity; Source of Uncertainty; Insurance Pricing; Decision-Making (search for similar items in EconPapers)
JEL-codes: C93 D81 D83 (search for similar items in EconPapers)
Date: 2010-02-23
New Economics Papers: this item is included in nep-cbe, nep-ias, nep-knm and nep-upt
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Citations: View citations in EconPapers (11) Track citations by RSS feed

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http://www.nottingham.ac.uk/~lizecon/RePEc/bbr/pdf/7.pdf

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Journal Article: Is imprecise knowledge better than conflicting expertise? Evidence from insurers’ decisions in the United States (2011) Downloads
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