A Comprehensive Analysis of the Subproportionality and Risk-Tolerance/Risk-Aversion Properties

Last registered on June 01, 2026

View Trial History

Pre-Trial

Trial Information

General Information

Title

A Comprehensive Analysis of the Subproportionality and Risk-Tolerance/Risk-Aversion Properties

RCT ID

AEARCTR-0015734

Initial registration date

April 03, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

April 10, 2025, 7:22 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

June 01, 2026, 2:02 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Charles Sprenger

Affiliation

Caltech

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Ted O'Donoghue

PI Affiliation

Cornell

Contact Investigator

PI Name

Camila Farres

PI Affiliation

Caltech

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2025-04-07

End date

2025-05-05

Keywords

Behavior

Additional Keywords

Uncertainty, Risk, Prospect Theory

JEL code(s)

D1, D81

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This project proposes to systematically study two key properties central to non-Expected Utility (EU) decisionmaking models: subproportionality and changing patterns of risk tolerance with probabilities. Subproportionality reflects evidence that when facing a choice between a relatively high probability of a moderate prize and relatively low probability of a high prize, individuals are more likely to choose the latter alternative as both probabilities are scaled down. Changing patterns of risk tolerance reflects the classic finding that in choices between a certain amount and a risky lottery, choices often appear risk tolerant at low probabilities, and risk averse at high probabilities of winning in the risky lottery. Existing evidence on these two phenomena are largely derived from separate data sets with incomplete coverage of the relevant space of parameters. This project proposes to systematically investigate the entire space to deliver a more complete empirical foundation for theories of non-EU decisionmaking.

External Link(s)

Registration Citation

Citation

Farres, Camila, Ted O'Donoghue and Charles Sprenger. 2026. "A Comprehensive Analysis of the Subproportionality and Risk-Tolerance/Risk-Aversion Properties." AEA RCT Registry. June 01. https://doi.org/10.1257/rct.15734-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Decisions under uncertainty will be completed under various parameterizations for the probabilities of prizes. A total of 189 probability combinations are considered, constituting 189 different treatment conditions. Subjects will be randomly assigned to a subset of conditions (30 in total) and comparison of valuations across conditions will be conducted.

Intervention (Hidden)

See document

Intervention Start Date

2025-04-07

Intervention End Date

2025-05-05

Primary Outcomes

Primary Outcomes (end points)

Valuations of lotteries to be compared across parameter combinations (treatments)

Primary Outcomes (explanation)

See document

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The experimental design establishes the relevant space of parameters for simultaneously studying subproportionality and changing risk tolerance over probabilities. 800 subjects will each complete 30 tasks at different, randomly-assigned locations in the parameter space.

Experimental Design Details

See document

Randomization Method

Randomization of parameters done by computer. Different subjects will face problems from different locations in the parameter space. Subjects are randomly assigned to complete 30 of 189 possible tasks.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

800

Sample size: planned number of observations

800*30 = 24000 total observations

Sample size (or number of clusters) by treatment arms

A total of 189 points in the parameter space are proposed leading to ~126 per location. 1/6 of observations are direct repeats, leaving ~ 105 independent observations per location. As noted above, different parameters constitute different conditions and are randomly assigned.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

For evaluation differences in valuations across two locations: a MDE of around $2.75 (around 18% of expected average valuation, $15, with s.d. of 7) is calculated.

Supporting Documents and Materials

IRB