Dynamic decision making under risk and uncertainty

Last registered on November 04, 2025

Pre-Trial

Trial Information

General Information

Title
Dynamic decision making under risk and uncertainty
RCT ID
AEARCTR-0017087
Initial registration date
October 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
October 27, 2025, 8:54 AM EDT

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

Last updated
November 04, 2025, 12:50 AM EST

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

Locations

Region

Primary Investigator

Affiliation
The University of Adelaide

Other Primary Investigator(s)

PI Affiliation
The University of Adelaide

Additional Trial Information

Status
In development
Start date
2025-11-06
End date
2026-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Live cash-out bets are phenomenal in modern gambling markets and contribute substantially to overall betting volumes. In a controlled experimental environment with fair bets, we will examine how individuals with some risk preferences make decisions dynamically when a cash-out option is available. We will explore the behavioral pattern where individuals plan to cash out and therefore place larger stakes, but ultimately end up not cashing out. We hypothesize that
H1: Cash-out bets weakly increase betting stakes relative to non-cash-out bets.
H2: This effect is partly driven by time inconsistency: individuals do not follow through on their plans. They plan to cash out but ultimately do not, or they plan not to cash out but end up cashing out.
H3: The errors of people who plan to cash out but end up not cashing out are more frequently happened than the errors of people who plan not to cash out but end up cashing out.
External Link(s)

Registration Citation

Citation
Sarwar, Rubayat and Ralph-Christopher Bayer. 2025. "Dynamic decision making under risk and uncertainty." AEA RCT Registry. November 04. https://doi.org/10.1257/rct.17087-1.1
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-11-06
Intervention End Date
2026-06-30

Primary Outcomes

Primary Outcomes (end points)
The key outcome variable is each participant’s betting decision in response to the provided bets. Each participant will be asked to decide how much of the provided endowment they would like to bet on the outcome of nine (09) simulated games, which are identical across the four treatments; i.e., games will have identical probabilities and identical payouts. Thus, one independent observation within a treatment corresponds to a vector of nine betting decisions made by a single participant, with each stake ranging from $0 to $10. We will then compare these decisions across the four treatments.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcome will be the pairwise comparison between participants’ planned cash-out decisions and their actual choices, which will serve as an indicator of time inconsistency.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To investigate the underlying behavioral mechanisms that drive people toward betting products offering dynamic decision-making opportunities (e.g., cash-out bets), we will conduct controlled economic experiments in a laboratory setting. There will be four groups of participants, corresponding to four different treatments. We will test how betting decisions differ among these groups. Each participant will be asked to decide how much money they would like to bet on the outcome of nine (09) simulated games, which are identical across the four treatments; i.e., games will have identical probabilities and identical payouts. Identical games across the four groups will help control the factors that could otherwise influence betting decisions. The difference among these treatments are discussed by following.


Treatment 1: Non-cash out bets: Participants will be offered with fair bets showing the probability of winning and the payout for each dollar bet. However, participants will be provided with a payout calculator on the game screen, which will be optional to use. Using intuition, participants will decide on the optimal level of betting stakes.

Treatment 2: Cashing out at the half time: Bets will have identical probabilities and payouts to Treatment 1, but participants will be offered an additional option to cash out. They can cash out during the half time of the game if they choose to.

Treatment 3: Cash-out bets with plans: This treatment is identical to Treatment 2 but with an additional step: participants will be asked in advance to reveal their plans about their choice of cashing-out. Their stated plans will be compared with their actual choices, allowing us to identify deviations from planned choices.

Treatment 4: cash-out anytime: Participants will face identical cash-out bets, but here they can cash out at any time during the game before it ends, not just at the halfway point.

All four treatments will present the payout of each dollar bet explicitly, including any cash-out amount. In our experiment, as the games are objective, the value of probability information given in the experiment is free from human judgment. Each participant will undergo only one of these four treatments, making this experiment a cross-subject design. For each round, participants will be given a virtual endowment of 10 dollars in the programmed experiment in Z-tree, from which they can allocate their stakes between 0 and 10 dollars. The difference between their endowment and the betting stake will be part of their earnings from the experiment. The other part will be the payout from the bet, received only if the game is won.
Experimental Design Details
Not available
Randomization Method
We randomly assign each experimental session to one of the 4 treatments. Participants voluntarily sign up to attend in our sessions. But at the time of registration, they do not know the treatment they will receive.
Randomization Unit
Session
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
As one independent observation will be a vector of nine betting decisions by an individual participant, we will do clustering at individual level. The planned number of clusters is 200 and 9 observations per cluster. This approach captures the similarities in betting behavior across participants.
Sample size: planned number of observations
200 participants x 9 betting decisions = 1,800 betting decisions (observations)
Sample size (or number of clusters) by treatment arms
Each treatment has 50 participants, and with 4 treatments, the total number of participants is 4 × 50 = 200.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Faculty of Arts, Business, Law and Economics Lower Risk Human Research Ethics Committee, The University of Adelaide
IRB Approval Date
2024-07-22
IRB Approval Number
H-2024-101