Dynamic Experiments for Estimating Preferences: Risk (DEEP Risk)

Toubia, O., Johnson, E., Evgeniou, T., & Delquie, P. (2012). Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters. Management Science.

For setup please contact a Center for Decision Sciences Research Coordinator at deep@decisionsciences.columbia.edu


Table of Contents


Description


References


Description:

Purpose

 DEEP Risk is an adaptive test -- allowing for a precise, robust, and fast elicitation of risk preferences.
Questions

  • 16 binary choice questions

Sub-scales
Domain

 Risk Attitude: Behavioral Measures of Risk
Sample items
  • Please consider the two gambles below. Which of these two gambles would you rather play?

    Option A
    90% Chance to Win $1
    10% Chance to Win $5

    Option B
    30% Chance to Win $100
    70% Chance to Lose $20


References:

Scale:
Toubia, O., Johnson, E., Evgeniou, T., & Delquie, P. (2012). Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters. Management Science.

Uses:
Huang, D., & Luo, L. (2013). Consumer Preference Elicitation of Complex Products using Fuzzy Support- Vector-Machine (SVM) Active Learning.

Schley, D. R., & Peters, E. (2014). Assessing “Economic Value” Symbolic-Number Mappings Predict Risky and Riskless Valuations. Psychological science, 0956797613515485.

Ray, D., Golovin, D., Krause, A., & Camerer, C. (2012). Bayesian Rapid Optimal Adaptive Design (BROAD): Method and application distinguishing models of risky choice.

Chesney, T. (2013). Networked individuals predict a community wide outcome from their local information. Decision Support Systems.
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