business solutions. Indeed, the drama of Moneyball does not
arise out of the gripping details of Paul dePodesta’s data analysis, but out of the resistance that general manager Billy Beane
faced when he attempted to change the culture of his organization from judgment-driven to data-driven decision-making.
Kahneman calls this phenomenon “the hostility to algorithms.”
While it is unevenly distributed from organization to organization, a hostility to algorithms certainly can impede (and
at times has impeded) the adoption of analytical methods in
insurance companies. Sometimes predictive modeling projects
are delayed for years. Other times, good models go to waste
because of skepticism in the field. It is not unusual to encounter resistance from experienced and well-positioned skeptics
who, consistent with Tetlock’s and Kahneman’s analyses, doubt
the ability of actuarial models to compete with—much less outperform—traditional underwriting methods. As happened with
the Oakland A’s, analytical techniques ultimately end up being
adopted. But there can be drama along the way.
A Quality Insight
Near the end of his book, Kahneman states that “whatever else
it produces, an organization is a factory that manufactures judgments and decisions.” Recent cognitive science has taught us
that an overreliance on unchecked intuitions and professional
judgment can lead to persistently suboptimal decision processes
and even inefficient markets. This is an important message for
practitioners of actuarial science, data science, and business analytics who are considering how their tools can be used to help
businesses and individual consumers make better decisions.
JAMES GUSZCZA is a fellow of the Casualty Actuarial Society
and an assistant professor of actuarial science, risk management
and insurance at the university of Wisconsin-Madison.
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