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The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature

Last registered on October 17, 2023
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Pre-Trial

Trial Information

General Information

Title
The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature
RCT ID
AEARCTR-0012180
Initial registration date
October 10, 2023

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
October 17, 2023, 11:48 AM EDT

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Seung-Keun Martinez
Affiliation
University of Notttingham
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Ned Augenblick
PI Affiliation
UC Berkeley Haas School of Business
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2023-10-11
End date
2023-10-17
Keywords
Behavior
Additional Keywords
Risk Preferences, Choice over Uncertainty, Lotteries
JEL code(s)
D90, C90, D81
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In preceding theoretical work, we develop methodology to measure the implied (expected) utility curvature from choices over objective lotteries. In this incentivized survey, we study the consistency of implied utility curvature over simple objective lotteries.
External Link(s)

Registration Citation

Citation
Augenblick, Ned and Seung-Keun Martinez. 2023. "The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12180-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-10-11
Intervention End Date
2023-10-17

Primary Outcomes

Primary Outcomes (end points)
We will be eliciting preferences over objective lotteries. The elicitation device consists of two steps.
1) A binary choice between Lottery A and Lottery B.
2) A choice of how much guaranteed money must be taken away from the preferred lottery to make the decision maker indifferent between the two lotteries.

The choice described above will be incentivized through the standard Becker-DeGroot-Marschak mechanism.
Primary Outcomes (explanation)
We plan to analyze the consistency of individual choices across the different choices they make. We will do this in two principle ways. One way is by looking at the relative frequency that Lottery A or Lottery B is chosen. We will also use the elicited indifference point between each lottery A and lottery B to compute the coefficient of absolute risk aversion that would make the individual indifferent between lottery A and lottery B under the presumption of expected utility. We will calculate this coefficient of absolute risk aversion for every choice.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Each participant will be required to make 10 2-part choices over uncertainty (described above in primary outcomes). The Lotteries are follows.

Lottery A: 1.00*$20
Lottery B: 0.50*$10+0.50*$30

Lottery A: 1.00*$40
Lottery B: 0.50*$30+0.50*$50

Lottery A: 1.00*$12
Lottery B: 0.90*$10+0.10*$30

Lottery A: 1.00*$32
Lottery B: 0.90*$30+0.10*$50

Lottery A: 1.00*$28
Lottery B: 0.10*$10+0.90*$30

Lottery A: 1.00*$48
Lottery B: 0.10*$30+0.90*$50

Lottery A: 0.75*$20+0.25*$40
Lottery B: 0.25*$10+0.75*$30

Lottery A: 0.75*$40+0.25*$60
Lottery B: 0.25*$30+0.75*$50

Lottery A: 0.50*$20+0.50*$40
Lottery B: 0.125*$10+0.75*$30+0.125*$50

Lottery A: 0.50*$40+0.50*$60
Lottery B: 0.125*$30+0.75*$50+0.125*$70

We will also collect basic demographic information via prolific, and we will ask people to state their agreement to the the following statements on a likert-scale:

I felt certain about my lottery evaluations.
I am comfortable with financial risk.
I am better at math than most people.
Experimental Design Details
Randomization Method
Automated randomization by Qualtrics.
Randomization Unit
Within-subject randomization at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 individual participants. Since each individual will answer 10 risk elicitation questions, we will treat each participant as a "cluster."
Sample size: planned number of observations
10 observations per participant for a total of 500 observations.
Sample size (or number of clusters) by treatment arms
Within subject randomization of risk elicitation question. All participants will see all risk elicitation questions.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Nottingham
IRB Approval Date
2023-07-11
IRB Approval Number
N/A
Analysis Plan

Analysis Plan Documents

Pre Analysis Plan

MD5: 07827b3f2299173d514e062be3b956e2

SHA1: 372a998ffd8036e3f2ae0254cb96390eeb461175

Uploaded At: October 10, 2023

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials