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Estimating Belief-based Utility
Last registered on July 27, 2020

Pre-Trial

Trial Information
General Information
Title
Estimating Belief-based Utility
RCT ID
AEARCTR-0006233
Initial registration date
July 27, 2020
Last updated
July 27, 2020 10:26 AM EDT
Location(s)
Primary Investigator
Affiliation
Bonn University
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2020-07-28
End date
2020-08-28
Secondary IDs
Abstract
We collect data from a laboratory experiment to investigate the belief-based component of the utility function. The experiment consists of two parts: an IQ test and a learning exercise. In the main treatment, every participant observes a signal about his relative performance in the IQ test. The signal is either perfectly informative or entirely uninformative with equal probability. Then the participant is asked to report his subjective probability of the signal being indicative of his performance. In the control treatment, participants do not observe signals but are asked to submit reports for each possible signal realization. We assume that a signal about cognitive ability received in the main treatment affects subjects' belief-based utility, while a signal that is being considered in the control treatment does not. This allows us to empirically investigate the belief-based component of the utility function and estimate its parameters at the population level, as well as for groups of individuals with similar characteristics. Moreover, we plan to use the data from the main treatment to reconcile contradictory evidence on asymmetric updating (Eil and Rao, 2011; Mobius et al, 2014; Schwardmann and van der Weele, 2016; Buser et al., 2018; Coutts, 2018) and further investigate the role of expectations in beliefs formation. In particular, we plan to examine beliefs updating about events to which subjects assign zero prior probability. Last but not least, we use additional survey data to examine the occurrence of time inconsistency in belief formation arising due to anticipatory feelings over upcoming signal realizations. Therefore, we plan to test the psychological expected utility theory formulated by Caplin and Leahy (2001).
External Link(s)
Registration Citation
Citation
Kozakiewicz, Marta. 2020. "Estimating Belief-based Utility." AEA RCT Registry. July 27. https://doi.org/10.1257/rct.6233-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-07-28
Intervention End Date
2020-08-28
Primary Outcomes
Primary Outcomes (end points)
There are two main outcome variables: 1. the subjective beliefs about one's IQ test result measured before observing a signal, and 2. the subjective probability of the signal being indicative of one's relative performance (measured in the main treatment after observing the signal, and after considering the signal in the control treatment).
Primary Outcomes (explanation)
The final outcome variable will be constructed based on deviations of subjective probability (2) from the Bayesian benchmark based on the subjective beliefs about one's IQ test result (1). The final outcome variable will be compared between subjects in the two treatments.
Secondary Outcomes
Secondary Outcomes (end points)
Additionally, we collect data on subjects' personality traits, anxiety (state and trait), test-related emotions experienced during the task and habitual use of emotion regulation strategies.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Participants are randomly assigned to one of two treatments. In the main treatment, every participant observes a signal about his relative performance in the IQ test. The signal is either perfectly informative or entirely uninformative with equal probability. Then the participant is asked to report his subjective probability of the signal being his performance. In the control treatment, participants do not observe signals but are asked to submit reports for each possible signal realization.
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
randomization at the level of experimental session (every session is randomly assigned to be either the treatment or the control); randomization at the individual level in the main treatment (a participant obtains a signal that is equal to his relative performance with probability 0.5 or, with the same probability, equal to a randomly drawn number)
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2 treatments
Sample size: planned number of observations
396
Sample size (or number of clusters) by treatment arms
294 in the main treatment, 102 in the control treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2020-07-13
IRB Approval Number
vke5RZr7
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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