Welcome to the Future of
Life Reinsurance Reporting!
comment. Statistical thinking is a skill that can be taught and a
habit that can be instilled.
As an example, Kahneman reports that experimental subjects
could be cured of loss aversion (the tendency to feel losses more
keenly than gains) by being instructed to “think like a trader.” A
small personal anecdote points in the same direction. On the first
day of a semester-long class, I made the mistake of presenting the
Linda story to a class of bright actuarial science seniors at the
University of Wisconsin-Madison. Not only were they not fooled;
many of them didn’t even see why the question was interesting.
Only after this rather awkward experience did I read in Thinking,
Fast and Slow that social science graduate students at Stanford
and Berkeley were the outliers in Kahneman and Tversky’s experiments. Because these students had taken many statistics
courses, relatively few of them fell prey to the conjunction fallacy.
A more specific strategy for overcoming biases, one that also
plays to the strengths of actuarial training, is using data and
analysis as a corrective to intuition-driven decision-making.
Kahneman and others, for example, advocate a rudimentary
form of data analysis to avoid the so-called planning fallacy,
a tendency to underestimate the time and expense needed to
complete a project. Once again, the availability heuristic is the
culprit. When we make forecasts, we account for potential risks
and delays that easily come to mind. Less cognitively available
potential risks and delays are not similarly accounted for. An
effective data-driven countermeasure is “reference-class forecasting.” This simply means calculating the average cost and
time of a comparable set (or reference class) of projects.
This is a straightforward case of what has come to be known
as “business analytics”—using data and analysis to promote better business decisions. A famous example from popular culture
is Michael Lewis’ book (and now movie) Moneyball, which describes how the cash-poor Oakland A’s was able to use data
analysis to identify baseball players who were undervalued by
the market. This inefficient market for talent existed in the first
place because of the sorts of cognitive biases that Kahneman
discusses. Baseball scouts traditionally made hiring decisions
based on fallible intuitions, traditions, and rules of thumb. Because of this, the price (salaries) of the relevant assets (players)
did not reflect all of the available information. In a recent
Vanity Fair interview with Kahneman, Michael Lewis wrote that
he had not been aware of the cognitive science implications
of his story until he read a perceptive review of Moneyball
by economists Richard Thaler and Cass Sunstein, authors of
the highly influential book Nudge: Improving Decisions About
Health, Wealth, and Happiness and advisers to President Barack
Obama and British Prime Minister David Cameron.
Moneyball-type stories have been repeated dozens of times
in many less glamorous domains—including insurance. Predictive models routinely are used to help experts select and
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