Complexity and Decision Making under Risk

Last registered on June 27, 2025
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Pre-Trial

Trial Information

General Information

Title
Complexity and Decision Making under Risk
RCT ID
AEARCTR-0016214
Initial registration date
June 24, 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
June 27, 2025, 8:41 AM EDT

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

Locations

Country
Korea (Republic of)
Region
Seoul

Primary Investigator

Name
Uihyeon Jung
Affiliation
Seoul National University
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Other Primary Investigator(s)

PI Name
Syngjoo Choi
PI Affiliation
Seoul National University
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PI Name
Eungik Lee
PI Affiliation
Indiana University Bloomington
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Additional Trial Information

Status
On going
Start date
2025-04-22
End date
2025-07-30
Keywords
Behavior, Finance & Microfinance, Lab
Additional Keywords
Complexity, Choice under Risk, Behavioral Attenuation, Cognitive Uncertainty, Social Learning
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This study investigates how complexity affects decision-making under risk, focusing on risk attitudes and rationality. Participants make a series of portfolio allocation decisions varying in complexity, with some receiving state-payoff feedback (information treatment). The study examines whether complexity-induced behavioral attenuation stems from cognitive uncertainty, which reflects optimization difficulties. Additional variables are measured to explore their roles in shaping decision-making. In a follow-up experiment, a social learning treatment is introduced, providing participants with the average choices of previous participants.









External Link(s)

Registration Citation

Citation
Choi, Syngjoo , Uihyeon Jung and Eungik Lee. 2025. "Complexity and Decision Making under Risk." AEA RCT Registry. June 27. https://doi.org/10.1257/rct.16214-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This lab experiment will be conducted with 300 participants aged 18 or older, recruited from four-year universities located in Seoul, Korea. It will be conducted over Zoom and will last approximately two hours.








Intervention (Hidden)
Intervention Start Date
2025-04-22
Intervention End Date
2025-07-30

Primary Outcomes

Primary Outcomes (end points)
- 30 portfolio choices under baseline condition
- 30 portfolio choices under complex condition
- Compare relative demands over prices between baseline condition and complex condition
Primary Outcomes (explanation)
Portfolio choices are used to estimate individual risk preferences under baseline and complex conditions. Cognitive uncertainty, elicited every 5th round (12 rounds total), captures difficulties of optimization in decision-making. These estimates allow testing the below hypotheses:
H1: Measured risk aversion increases under complexity compared to baseline (behavioral attenuation driven by complexity).
H2: A substantial portion of individuals exhibit behavioral attenuation, while others show amplification or no change.
H3: Individuals with higher cognitive uncertainty will exhibit more behavioral attenuation.

Secondary Outcomes

Secondary Outcomes (end points)
- Raven test score (cognitive ability)
- Objective uncertainty (measured via state-payoff matching task)
Secondary Outcomes (explanation)
These two measures are used to explore how individual characteristics relate to risk attitudes and rationality under complexity.

Experimental Design

Experimental Design
The first experiment is sixty portfolio allocation problems based on Choi et al. (2007). Thirty baseline problems are allocation across two assets, and thirty complex problems are allocation across four assets, which contain two additional assets that are just convex combinations of baseline assets. Participants can make their decisions either by dragging the investment bars or directly entering their allocations in the investment box. By between variation, we introduce state-payoff information treatment, which automatically computes and shows their state-payoff determined by their investment decisions. If participants who receive information treatment also exhibit behavioral attenuation, we can conclude that attenuation was an individual-specific inherent phenomenon, caused by not knowing how to optimize, rather than computational complexity.

We elicit cognitive uncertainty in every 5th round, 12 rounds in total. Also, after the experiment, we additionally measure two individual variables: cognitive ability and objective uncertainty. Cognitive ability is measured by 12 problems from Raven Advanced Progressive Matrices. Objective uncertainty is defined as the portfolio choice. More specifically, participants solve 6 problems which give a target-state payoff, and properly allocate investment as closely as possible to satisfy the state payoff. These two measurements show ancillary yet meaningful explanations of how decision under complexity, risk attitude, and rationality are associated with these two measurements.

The second experiment is identical in structure to the first, except for the social learning treatment: participants are shown the average investment allocations made by previous participants. For each information treatment condition, we provide separate averages based on whether the source participants exhibited behavioral attenuation or not. This design allows us to investigate if the social learning treatment helps reduce cognitive uncertainty, then its effect differs depending on the social learning treatment conditions.
Experimental Design Details
Randomization Method
Participants are randomly assigned to treatment arms. The treatment arm regarding baseline and complex conditions is within-subject. The treatment arms regarding the information of state payoffs and social learning are between-subject.
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not Clustered
Sample size: planned number of observations
300 participants
Sample size (or number of clusters) by treatment arms
50 for each arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Seoul National University Institutional Review Board
IRB Approval Date
2025-03-12
IRB Approval Number
2503/003-007
Analysis Plan

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