The Most Dangerous Model: A Natural Benchmark for Assessing Model Risk

Society of Actuaries Monograph: Enterprise Risk Management Symposium, 2015

46 Pages Posted: 30 May 2015 Last revised: 27 Jun 2015

See all articles by John Major

John Major

Guy Carpenter & Company, LLC

Ruodu Wang

University of Waterloo - Department of Statistics and Actuarial Science

Micah Woolstenhulme

Guy Carpenter & Company, LLC

Date Written: May 22, 2015

Abstract

We examine the problem of decision making using a probabilistic model when there is material uncertainty concerning the accuracy of the model coupled with limited information about it. Such conditions could hold, for example, for the user of a complex commercial model of natural catastrophe insurance risk. Working within an ambiguity-averse decision framework, we define bounds for a set of plausible alternative models, centered on the “baseline” model provided to the user. Three types of bounds are defined, reflecting the model user’s assumptions about the unknown and inaccessible data to which the baseline model was fit. Given a utility function for a decision option and a bound, we first address the corresponding optimization problem of finding the “worst” (most adverse expected utility) model within the set of plausible models. Second, we construct posterior mean utilities among the unbounded set of alternatives and show the existence of a posterior utility-minimizing worst credible model, i.e. the “most dangerous model.” Among all alternative models to the baseline, this model has the highest product of expected disutility times probability that it, and not the baseline, is the correct model. We present a case study of how the most dangerous model can be used as a naturally occurring benchmark when making decisions in the presence of model risk.

Keywords: ambiguity aversion, robust control, model risk, Gilboa-Schmeidler, model uncertainty

JEL Classification: D81, C44, C61, G22, G32, Q54

Suggested Citation

Major, John and Wang, Ruodu and Woolstenhulme, Micah, The Most Dangerous Model: A Natural Benchmark for Assessing Model Risk (May 22, 2015). Society of Actuaries Monograph: Enterprise Risk Management Symposium, 2015, Available at SSRN: https://ssrn.com/abstract=2611806

John Major (Contact Author)

Guy Carpenter & Company, LLC ( email )

1166 Avenue of the Americas
New York, NY 10036
United States

Ruodu Wang

University of Waterloo - Department of Statistics and Actuarial Science ( email )

Waterloo, Ontario N2L 3G1
Canada

Micah Woolstenhulme

Guy Carpenter & Company, LLC ( email )

New York, NY 10010
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
230
Abstract Views
1,430
rank
145,442
PlumX Metrics