Optimal Stopping in a Dynamic Salience Model
Last registered on May 24, 2021

Pre-Trial

Trial Information
General Information
Title
Optimal Stopping in a Dynamic Salience Model
RCT ID
AEARCTR-0005359
Initial registration date
January 29, 2020
Last updated
May 24, 2021 9:51 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Central European University
Other Primary Investigator(s)
PI Affiliation
Frankfurt School of Finance and Management
PI Affiliation
Said Business School, University of Oxford
Additional Trial Information
Status
Completed
Start date
2020-01-30
End date
2020-02-29
Secondary IDs
Abstract
Models of non-linear probability weighting, such as cumulative prospect theory, predict time-inconsistent behavior when applied to a dynamic context (e.g., Machina, 1989). While time-inconsistent behavior is indeed widespread, common specifications of CPT have (too) extreme implications in certain setups (e.g., Ebert and Strack, 2015, 2018). In this study, we compare the implications of (exogeneous) probability weighting as assumed in CPT to those of (endogenous) probability weighting as proposed in salience theory of choice under risk (Bordalo et al., 2012), under the (testable) assumption that the decision maker is naive about his time-inconsistency.

We propose a dynamic salience model to study the choice of when to optimally stop an arithmetic brownian motion with non-positive drift. Our salience model predicts that the optimal stopping behavior is sensitive to the drift of the process; namely, a naive agent will gamble if the drift of the process is slightly negative, but will stop immediately if the drift becomes too negative. This prediction is arguably more plausible than those of CPT, which under common specifications predicts excessive gambling irrespective of the drift of the process, and EUT (with a concave utility function), which predicts no gambling at all.
External Link(s)
Registration Citation
Citation
Dertwinkel-Kalt, Markus, Jonas Frey and Mats Köster. 2021. "Optimal Stopping in a Dynamic Salience Model." AEA RCT Registry. May 24. https://doi.org/10.1257/rct.5359-2.0.
Experimental Details
Interventions
Intervention(s)
Section 4 of the Pre-Analysis Plan describes the experimental design in detail.
Intervention Start Date
2020-01-30
Intervention End Date
2020-02-29
Primary Outcomes
Primary Outcomes (end points)
Stopping times and stopping strategies for arithmetic brownian motions with six different drifts.
Primary Outcomes (explanation)
Section 4 of the Pre-Analysis Plan contains a detailed description of the primary outcomes.
Secondary Outcomes
Secondary Outcomes (end points)
Indicators of twelve choices between a binary lottery and the safe option paying the lottery's expected value.
Secondary Outcomes (explanation)
Section 4 of the Pre-Analysis Plan contains a detailed description of the secondary outcomes.
Experimental Design
Experimental Design
We study optimal stopping behavior of subjects who face arithmetic brownian motions with different drifts. Each subject makes six such stopping decisions, which are framed as selling an asset at a price that evolves stochastically over time. In addition, subjects make twelve static choices between a binary lottery and the safe option paying the lottery's expected value. A detailed description of the experimental design is provided in Section 4 of the Pre-Analysis Plan.
Experimental Design Details
Randomization Method
Randomization done by a computer.
Randomization Unit
Individual
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
150 individuals
Sample size: planned number of observations
900 stopping decisions (+ 1800 static choices)
Sample size (or number of clusters) by treatment arms
150 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Oxford, Said Business School Departmental Research Ethics Committee
IRB Approval Date
2019-10-15
IRB Approval Number
SSH_SBS_C1A_19_64
Analysis Plan
Analysis Plan Documents
Pre-Analysis Plan

MD5: e42881667b56d52de7a5263ece85035a

SHA1: c55da74370bf73881cf1681e47251ed5ad289cca

Uploaded At: January 29, 2020

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
January 31, 2020, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
January 31, 2020, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
158 individuals
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
948 stopping decisions (+ 1896 static choices)
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS