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Trial Start Date October 29, 2025 November 06, 2025
Last Published October 27, 2025 08:54 AM November 04, 2025 12:50 AM
Intervention Start Date October 29, 2025 November 06, 2025
Primary Outcomes (End Points) The key outcome variable is each participant’s betting stake decision in response to the provided bets. Each participant will be offered a set of fifteen (15) rounds. Thus, one independent observation within a treatment corresponds to a vector of fifteen betting decisions made by a single participant, with each stake ranging from $0 to $8. We will then compare these decisions across the four treatments. 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.
Experimental Design (Public) 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 play a set of fifteen (15) rounds of games, which are identical across the four treatments; i.e., games will have identical probabilities and identical returns. 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 success and the return from each $1 stake. However, participants will be provided with a profit calculator on the game screen, which will be optional to use. They may or may not use it to calculate the expected income from any betting stake in case of both successful and unsuccessful outcomes. Using intuition, participants will decide on the optimal level of stakes. Treatment 2: Cashing out at the half time: Bets will have identical probabilities and returns to Treatment 1, but participants will be offered an additional option to cash out. They can cash out during the half time of the round 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 behavior, allowing us to identify deviations from intended decisions. Treatment 4: cash -out anytime: Participants will face identical cash-out bets, but here they can cash out at any time during the round, not just at the halfway point. All four treatments will present the payoff from each unit of betting stake 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 8 dollars in the programmed experiment in Z-tree, from which they can allocate their stakes between 0 and 8 dollars. The difference between their endowment and the amount invested will be part of their profit from the investment. The other part will be the return on the bet, received only if it is successful. 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.
Planned Number of Clusters As one independent observation will be a vector of fifteen betting decisions by an individual participant, we will do clustering at individual level. The planned number of clusters is 200 and 15 observations per cluster. This approach captures the similarities in betting behavior across participants. 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.
Planned Number of Observations 200 participants x 15 betting decisions = 3,000 betting decisions (observations) 200 participants x 9 betting decisions = 1,800 betting decisions (observations)
Pi as first author No Yes
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