Rational Memory vs Reinforcement Learning
Last registered on March 09, 2020


Trial Information
General Information
Rational Memory vs Reinforcement Learning
Initial registration date
September 05, 2019
Last updated
March 09, 2020 8:09 PM EDT
Primary Investigator
Chapman University
Other Primary Investigator(s)
Additional Trial Information
In development
Start date
End date
Secondary IDs
Kunreuther et al. (2013) document a phenomenon whereby the demand for disaster insurance increases after a disaster and then falls when no further disasters arise, even when no serial correlation is present. If individuals were perfect Bayesian learners, we would expect these responses to disappear asymptotically, but they appear to be persistent in equilibrium. These “insurance cycles” can be explained by a more general phenomenon called recency bias where more recent events have an out-sized impact on beliefs and behavior. New theoretical work in Neligh (2019) has demonstrated how recency bias and insurance cycles can naturally result from a rational memory model where memories decay over time, but individuals preserve their memories better by expending costly cognitive resources. In this paper, we conduct an experiment testing some predictions of the rational memory model in an insurance purchasing game. We also test whether player behavior is better described by a rational memory model or a traditional reinforcement learning model by separately manipulating the value of information and the reward associated with it.
External Link(s)
Registration Citation
Neligh, Nathaniel. 2020. "Rational Memory vs Reinforcement Learning." AEA RCT Registry. March 09. https://doi.org/10.1257/rct.4638-3.0.
Experimental Details
In this experiment we independently manipulate the value of information and the reward associated with information in order to determine which has a larger impact on learning. Under the reinforcement learning hypothesis, reinforcement should be the primary determinant of learning. Under rational memory, information value should be the main factor at play.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The basic outcome variable is insurance purchasing.
Primary Outcomes (explanation)
We are interested in how well players learn in different treatments. Degree of learning is measure by looking at number of correct responses. A correct response is either insuring a high risk business or not insuring insure a low risk one.
Secondary Outcomes
Secondary Outcomes (end points)
Recency bias
Secondary Outcomes (explanation)
We are also interested in the degree to which individuals overweight newer information both when purchasing and not purchasing insurance.
Experimental Design
Experimental Design
In this experiment, individuals engage in an insurance purchasing game where they must choose whether or not to insure 3 fictional businesses every period.
Experimental Design Details
Each session is divided into 3 treatments and each treatment is divided into 20 periods.
In all treatments, players are engaging in an insurance purchasing game. In this game, players have 2 fictional businesses that they can individually insure (or not) every period. After players have decided whether to insure their businesses for the period, "disasters" may occur. If a business is insured it provides a fixed 40% chance of receiving the prize every period. If a business is not insured and no disaster occurs, it provides a 100% chance of receiving the prize. If a business is insured and a disaster occurs, the prize is not awarded.
This setup guarantees that players will always want to insure a high risk business and not insure a low risk one. Points earned are reported each period in most treatments, but payment realizations are not reported until the end of the experiment.
Businesses are either high risk or low risk. High risk businesses have a 70% chance of disaster each period while low risk businesses have a 50% chance of disaster. Risk type for each business is fixed throughout a treatment and re-randomized between treatments.
Randomization Method
Disasters randomized by computer during sessions.
Business risk levels randomized by computer during sessions.
Treatment order randomized in office by computer with each session having a different order. All orders will be tested.
Randomization Unit
Treatment order randomized by session
Disasters randomized on the period/business level
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
60 subjects
Sample size: planned number of observations
60 subjects* 2 businesses * 20 periods * 3 treatments =7200 observations
Sample size (or number of clusters) by treatment arms
Design is within subject so all subjects see all treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power calculations were done using simulations assuming that each players correctness in each treatment is drawn from a beta distribution with a mean or 0.553+diff/2 for high values and a mean of 0.553-diff/2 for low value. Standard deviation in both cases is 0.171. Values based on a between subjects pilot, so the effective standard deviation may be much lower. Under these assumptions, the experiment has a 48% of detecting a significant difference if the underlying difference is 10% correctness. The experiment has a 98% chance of detecting a significant difference if the underlying difference is 20% correctness. A difference of 1% correctness has a 4% chance of being detected.
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IRB Name
Chapman University Institutional Review Board (CU IRB)
IRB Approval Date
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Analysis Plan

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Study Withdrawal
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Data Publication
Data Publication
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Program Files
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Reports, Papers & Other Materials
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