Utility Maximization Vs Regret Minimization: Choice Behavior Under Uncertainty

Author(s)

Jiao X1, van Cranenburgh S2, Gu NY3
1University of Southern California, Los Angeles, CA, USA, 2Delft University of Technology, Delft, Netherlands, 3University of San Francisco, Santa Clarita, CA, USA

OBJECTIVES: To examine whether random utility maximization (RUM) or Random Regret Minimization (RRM) better describes how people make decisions when facing with different levels of risks and survivals.

METHODS: A discrete choice experiment (DCE) was designed with 6 attributes including 5 health domains of the EQ-5D-5L (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) plus the out-of-pocket costs expressed as % of annual household income (5%, 10%, 20% and 50%). Responses were collected using SurveyMonkey in December 2022 (n=150). Modified Federov algorithm was adopted to select the experimental design using Ngene. Ten choice sets were designed, each with 3 options. One dominant test choice set was included for quality screening. Both RUM and RRM models were used for the estimation.

RESULTS: Ninety respondents (60%) passed the screening and were included in the analysis (54 failed to pass the dominant test, 6 gave the same answers to every scenario). Mean age was 40.1 years (SD ±16.0); 42% male; 71.1% white, 11.1% black; 90.0% had college degree; 81.2% had health insurance coverages. All estimated coefficients were significant in both models with pain/discomfort being the most impactful factor in respondents’ decision-making, followed by mobility and selfcare (p<0.01). RRM and RUM showed similar decision rules and, the log-likelihood estimations were also comparable across models suggesting similar data fittings.

CONCLUSIONS: In this experiment, respondents did not yield strong semi-compensatory behavior, indicating traditional RUM better describes choice behavior. Further investigation on choice behaviors under different levels of risks and survivals are warranted.

Conference/Value in Health Info

2023-05, ISPOR 2023, Boston, MA, USA

Value in Health, Volume 26, Issue 6, S2 (June 2023)

Code

SA27

Topic

Study Approaches

Topic Subcategory

Prospective Observational Studies

Disease

No Additional Disease & Conditions/Specialized Treatment Areas

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