On the Modelling of Risk Preferences: A Classroom Replication Experiment
Last registered on September 20, 2015


Trial Information
General Information
On the Modelling of Risk Preferences: A Classroom Replication Experiment
Initial registration date
September 20, 2015
Last updated
September 20, 2015 4:53 AM EDT
Primary Investigator
University of Reading
Other Primary Investigator(s)
PI Affiliation
Henley Business School, University of Reading
Additional Trial Information
Start date
End date
Secondary IDs
This is a classroom experiment aiming at replicating findings underlying historical developments that have triggered the development of descriptive models of decision making. In particular, we aim to replicate patterns underlying Markowitz-expected utility, Dual-expected utility and prospect theory. We further aim to test parametric fittings of these models against each-other, in order to assess and quantify the relative costs of using the simplifications underlying the simpler models conditional on subsets of data used. An additional purpose of this paper is to test the feasibility and implications of pre-registering hypotheses and an analysis plan for laboratory and classroom experiments.
External Link(s)
Registration Citation
Bouchouicha, Ranoua and Ferdinand Vieider. 2015. "On the Modelling of Risk Preferences: A Classroom Replication Experiment." AEA RCT Registry. September 20. https://www.socialscienceregistry.org/trials/850/history/5324
Experimental Details
There is no treatment intervention in the traditional sense. The experiment consists in measuring individual risk preferences using choice lists.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
We aim to test three different theories and their merits: Markowitz expected utility, Dual expected utility, and prospect theories. Details of the hypotheses are spelled out in the attached documents.
Primary Outcomes (explanation)
We elicit certainty equivalents. Only subjects switching only once will be included in the final analysis. We will normalise the certainty equivalents (ce) as follows: (ce-y)/(x-y), where x and y are the high and low outcomes in the binary prospects employed.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experiment consists in the measurement of individual preferences. High nominal incentives up to 200 GBP will be used. Two subjects will be randomly selected to play for real money, in a class with approximately 50 students.
Experimental Design Details
Randomization Method
Two subjects will be extracted by drawing numbers from a bag.
Randomization Unit
Individual students.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
not applicable
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers
One of the stylized facts underlying prospect theory is a fourfold patternof risk preferences. People have been shown to be risk seeking for small probabil-ity gains and large probability losses, while being risk averse for large probabilitygains and small probability losses. Another fourfold pattern of risk preferences over outcomes, postulated by Harry Markowitz in 1952, has received much less attention and is currently not integrated into prospect theory. In two experiments, we show that risk preferences may change over outcomes. While we find people to be risk seek-ing for small outcomes, this turns to risk neutrality and later risk aversion as stakes increase. We then show how a one-parameter logarithmic utility function fits such stake effects significantly better under prospect theory than the power or exponential functions mostly used when fitting prospect theory models. We further investigate the extent to which the use of ill-suited functional forms to represent utility may result in violations of prospect theory, and whether such violations disappear when using logarithmic utility.