Optimal Stopping in a Dynamic Salience Model

Last registered on May 24, 2021


Trial Information

General Information

Optimal Stopping in a Dynamic Salience Model
Initial registration date
January 29, 2020

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
January 29, 2020, 1:44 PM EST

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

Last updated
May 24, 2021, 9:51 AM EDT

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



Primary Investigator

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

Start date
End date
Secondary IDs
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

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


Section 4 of the Pre-Analysis Plan describes the experimental design in detail.
Intervention Start Date
Intervention End Date

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
Was the treatment clustered?

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)

Institutional Review Boards (IRBs)

IRB Name
University of Oxford, Said Business School Departmental Research Ethics Committee
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: e42881667b56d52de7a5263ece85035a

SHA1: c55da74370bf73881cf1681e47251ed5ad289cca

Uploaded At: January 29, 2020


Post Trial Information

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Is the intervention completed?
Intervention Completion Date
January 31, 2020, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
January 31, 2020, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
158 individuals
Was attrition correlated with treatment status?
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?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials