Choices and Procedures

Last registered on January 27, 2025

Pre-Trial

Trial Information

General Information

Title
Choices and Procedures
RCT ID
AEARCTR-0015285
Initial registration date
January 26, 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
January 27, 2025, 10:24 AM EST

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

Locations

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Primary Investigator

Affiliation
University of Essex

Other Primary Investigator(s)

PI Affiliation
University of Essex

Additional Trial Information

Status
In development
Start date
2025-01-27
End date
2025-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Classical economic models assume that the decision-maker acts in a way to maximise her preferences. However, the economics literature is largely silent about the procedure that the decision-maker uses to maximise her preferences. In particular, when confronted with observed choice inconsistencies, most behavioural economists attempt to tweak the underlying assumptions about preferences to explain the inconsistencies. Instead, we argue that the observed inconsistencies can be attributed to one of two sources: either a failure to perform the preference maximisation procedure or a failure to hold classically rational preferences. In this project, we aim to disentangle observed choices along the procedural and preference dimensions. To do so, we conduct a laboratory experiment to collect data on the potential procedures that decision-makers might use in order to implement the maximisation of their preferences.
External Link(s)

Registration Citation

Citation
Dianat, Ahrash and Mikhail Freer. 2025. "Choices and Procedures." AEA RCT Registry. January 27. https://doi.org/10.1257/rct.15285-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-01-27
Intervention End Date
2025-03-31

Primary Outcomes

Primary Outcomes (end points)
(a) Choices from budgets that are converted to different notions of rationality:

1) Maximizing a preference relation
2) Maximizing a preference relation that satisfies monotonicity
3) Maximizing a preference relation that satisfies stochastic dominance

(b) All the notions applied to
(i) assuming that subjects pay full attention
(ii) taking into account the limited attention exhibited via keeping the record which alternatives have been opened or closed.

(c) Detailed data on the procedure implemented:
(i) order of alternatives opened
(ii) order in which alternatives have been closed
(iii) order in which alternatives have been chosen
(iv) sets from which alternatives have been opened/closed.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the experiment, each subject faces a menu that consists of 2-12 alternatives. Each alternative is a risky asset that specifies a monetary payment with a given probability. There are 20 budgets per subject presented in random order. Each subject implements a procedure to compare the alternatives and then chooses a unique alternative from the menu based on her preferences. Procedure is implemented by opening (showing) the alternative, selecting (temporary choosing) the alternative, or discarding (closing) the alternative.

We use a between-subject experimental design where each subject participates in one of the following four treatments:

Treatment 1: Delegated Procedure
In this case, the computer will generate a sequence of binary comparisons from the menu for the DM to perform. All discarded alternatives are discarded permanently and cannot be re-opened.

Treatment 2: Assisted Binary Procedure
In this case, the DM can implement any procedure she wishes with the caveat that she is restricted to binary comparisons. That is, the DM can only consider two alternatives from the menu at the same time. All discarded alternatives are discarded permanently and cannot be re-opened.

Treatment 3a: Assisted Procedure I
In this case, the DM can implement any procedure she wishes with no restrictions. That is, the DM can simultaneously consider any number of alternatives from the menu. All discarded alternatives are discarded permanently and cannot be re-opened.

Treatment 3b: Assisted Procedure II
In this case, the DM can implement any procedure she wishes with no restrictions. That is, the DM can simultaneously consider any number of alternatives from the menu. All discarded alternatives are not discarded permanently and can be re-opened.

Treatment 4: Free Procedure
In this case, the DM faces the full menu of 10 alternatives and she chooses one alternative. Since there is no pre- defined procedure, the DM can employ any heuristic to assist her choice. All discarded alternatives are not discarded permanently and can be re-opened.

Our experimental treatments allow us to answer our research questions by gradually increasing the complexity of the decision problem. The outcome variable of interest is the consistency of the DM's choices: that is, the extent to which the DM's observed choices can be generated by the maximisation of her preferences. Any observed inconsistencies in Treatment 1 are due to the DM's failure to accurately express her preferences. Any differences between Treatments 1 and 4 can be attributed to the procedural costs or burdens imposed on the DM. The comparison between Treatments 2 and 3 will shed light on the types of procedures that the DM uses to implement the maximisation of her preferences. In particular, any differences between Treatments 2 and 3 will illustrate the extent to which the DM's optimal procedure requires binary comparisons versus comparisons from larger sets.
Experimental Design Details
Not available
Randomization Method
All randomization is done by the computer
Randomization Unit
Randomization is done at the individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
400 subjects
Sample size: planned number of observations
80 subjects per treatment
Sample size (or number of clusters) by treatment arms
80 subjects per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Essex, ERAMS
IRB Approval Date
2024-03-25
IRB Approval Number
ETH2324-1082