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Related Experiment Video

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

A catastrophe model for the prospect-utility theory question.

Terence A Oliva¹, Sean R McDade

¹Department of Marketing, Fox School of Business, Temple University, 1810 N. 13th Street, Philadelphia, PA, 19122, USA. oliva@temple.edu

Nonlinear Dynamics, Psychology, and Life Sciences

|May 31, 2008

Summary

This study integrates expected utility and prospect theories using a nonlinear dynamical systems catastrophe model. It explains decision-making shifts based on psychological involvement and risk, validated with gambler data.

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Area of Science:

Decision analysis
Behavioral economics
Psychology

Background:

Decision-making under risk is analyzed through expected utility and prospect theories.
Observed anomalies in behavior challenge existing decision-making models.
Individuals inconsistently apply different decision-making approaches.

Purpose of the Study:

To propose a unified framework for decision-making under risk.
To explore the integration of expected utility and prospect theories.
To investigate the role of psychological state in decision-making shifts.

Main Methods:

Utilized a nonlinear dynamical systems (NDS) cusp catastrophe model.
Defined control dimensions as psychological involvement and gains versus losses.
Estimated the catastrophe model using regression analysis on 997 gamblers' data.

Main Results:

The catastrophe model explains shifts between expected utility and prospect approaches.
Psychological involvement significantly influences decision-making behavior.
The model provides a rationale for seemingly irrational decisions under high emotional involvement.

Conclusions:

Nonlinear dynamical systems offer a novel approach to integrate decision-making theories.
Psychological involvement is a critical factor in understanding risk-related choices.
The study validates the catastrophe model with real-world gambling data.