Using eye-tracking to examine strategies for evaluating compound lotteries

Last registered on October 27, 2025

Pre-Trial

Trial Information

General Information

Title
Using eye-tracking to examine strategies for evaluating compound lotteries
RCT ID
AEARCTR-0016854
Initial registration date
October 07, 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
October 27, 2025, 9:27 AM EDT

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
Tehran Institute for Advanced Studies, Khatam University

Other Primary Investigator(s)

PI Affiliation
Centre for Social and Behaviour Change, Ashoka University

Additional Trial Information

Status
On going
Start date
2025-10-01
End date
2026-12-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We use eye-tracking to investigate how participants evaluate compound lotteries, differentiating between two competing strategies: Backward induction, which aligns with the compound independence (CI) axiom, and forward induction, which supports the reduction of compound lotteries (ROCL) axiom.
External Link(s)

Registration Citation

Citation
Hajimoladarvish, Narges and Sneha Shashidhara. 2025. "Using eye-tracking to examine strategies for evaluating compound lotteries." AEA RCT Registry. October 27. https://doi.org/10.1257/rct.16854-1.0
Experimental Details

Interventions

Intervention(s)
A compound lottery is a lottery where the outcomes themselves are lotteries, adding multiple stages of risk. Reducing such compound lotteries to simpler ones—the Reduction of Compound Lotteries (ROCL) axiom—is a central tenet of expected utility theory. ROCL axiom posits that a compound lottery can be simplified into an equivalent simple lottery, preserving the same expected utility. This reduction is based on the expected utility theory, where decision-makers rely on the final-stage outcomes and their probabilities akin to a forward induction approach to problem-solving. On the other hand, the Compound Independence (CI) axiom proposes an alternative approach to simplifying compound lotteries, where each stage is evaluated independently. CI axiom, compatible with many decision theories, suggests that people first evaluate the final-stage lotteries separately and use those evaluations to simplify the overall lottery process like backward induction (Segal, 1990). Eye-tracking data can help determine which strategy participants predominantly follow by analysing the sequence and timing of their visual fixations. Participants will complete the main experimental tasks while their eye movements will be recorded with a Tobii Pro Fusion eye-tracker (120 Hz). Participants will evaluate both simple and compound lotteries by entering their certainty equivalents (CEs). Lotteries appear as horizontal decision trees randomized in order and orientation, being displayed in either a standard (straight) or mirrored (flipped) position on the screen.
Intervention Start Date
2025-11-01
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
Behavioural outcomes are the following:

- Certainty equivalents (CEs) for compound lotteries, their actuarially equivalent lotteries, and their embedded simple lotteries.
- Accuracy in valuation tasks, defined as the percentage of CEs consistent with stochastic dominance and dominance orderings.
- Response times.

The following eye-tracking measures are analyzed as averages across trials:

- First fixation location and time-to-first-fixation (TTFF).
- Fixation order – the sequence of attention between probabilities and outcomes, allowing comparison of fixation order patterns.
- Temporal attention distribution – each trial is divided into 10 equal intervals, and time spent on each area of interest (AOI) is measured across these intervals.
- Proportion of total trial time spent on different AOIs.
- Fixation duration and pupil size.

Trial-Level Outcome:

- Order of fixations within each trial.

Constructed Outcomes

- The use of Forward induction strategy is identified if fixation sequences prioritise initial first stage probabilities, consistent with ROCL axiom.
- Backward induction strategy is identified when fixation sequences emphasise the final-stage lotteries, consistent with the CI axiom.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a within-subjects laboratory experiment with lottery evaluation task incentivised with BMD procedure. Each participant will complete the tasks in a controlled lab setting with eye-tracking throughout.

To test the ROCL and CI axioms, we elicit certainty equivalents of compound lotteries (two-stage lotteries), their corresponding actuarially equivalent lotteries, and the embedded second-stage lotteries in compound lotteries on their own (simple lotteries). We used two sets of compound lotteries, A and B, where A had a greater expected value than B. Each lottery was presented separately, and participants indicated their certainty equivalent by typing the amount in a text box below the lottery description. Trials had no time limit, and a one-second fixation screen appeared between trials to reset attention.

The eye-tracking data allows comparison of forward vs. backward induction strategies within each trial and as averages across trials. The use of forward induction strategy is identified if fixation sequences prioritise initial first stage probabilities, consistent with ROCL axiom. Backward induction strategy is identified when fixation sequences emphasise the final-stage lotteries, consistent with the CI axiom.
Experimental Design Details
Not available
Randomization Method
The order and orientation of the lotteries are randomised by a computer.
Randomization Unit
NA
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
There is no cluster
Sample size: planned number of observations
We have 54 trials for each subject.
Sample size (or number of clusters) by treatment arms
A sample size of 70 participants was determined, consistent with typical eye-tracking studies (Zhang et al., 2024; Alós-Ferrer & Ritschel, 2022).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ashoka University Institutional Review Board
IRB Approval Date
2023-04-01
IRB Approval Number
23-X-10044-Hajimoladarvish
Analysis Plan

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