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Rational Memory vs Reinforcement Learning
Last registered on September 06, 2019

Pre-Trial

Trial Information
General Information
Title
Rational Memory vs Reinforcement Learning
RCT ID
AEARCTR-0004638
Initial registration date
September 05, 2019
Last updated
September 06, 2019 1:41 PM EDT
Location(s)

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Primary Investigator
Affiliation
Chapman University
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2019-09-19
End date
2020-03-30
Secondary IDs
Abstract
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
Citation
Neligh, Nathaniel. 2019. "Rational Memory vs Reinforcement Learning." AEA RCT Registry. September 06. https://doi.org/10.1257/rct.4638-1.0.
Experimental Details
Interventions
Intervention(s)
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
2019-09-19
Intervention End Date
2020-03-30
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
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
Not available
Randomization Method
Disasters randomized by computer during sessions.
Business risk levels randomized by computer during sessions.
Treatment order randomized in office by computer. In order to balance the sample, after generating a random ordering of trials 1 2 3 4, we also conduct sessions ordered 4 3 2 1, 2 1 4 3, and 3 4 1 2. Each ordering is used for three sessions.
Randomization Unit
Treatment order randomized by session
Disasters randomized on the period/business level
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
120 subjects
Sample size: planned number of observations
120 subjects* 3 businesses * 25 periods * 4 treatments = 24000 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.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Chapman University Institutional Review Board (CU IRB)
IRB Approval Date
2019-07-29
IRB Approval Number
0910H017
Analysis Plan

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