physics would have remained metaphysical, had scientists
clung to the doctrine of perfect circular motion.
Analogously, Kahneman, Tversky, Thaler, Sunstein, and Ariely urge economists to dispense with the unrealistic and philosophically motivated doctrine of rational expectations in favor
of the messy but empirical regularities of behavioral science.
If, as Thaler hopes, the word “economics” eventually comes to
mean what we today call “behavioral economics,” it is possible
that the relevance of economics to other fields will be magnified considerably.
For insurers, this has potential relevance to any point at
which economic theory impinges on insurance research, marketing, or actuarial work. For example, much of the existing
academic literature on the underwriting cycle has been written
from the point of view of rational expectations and efficient markets. This might leave potentially valuable explanations of the
underwriting cycle on the table. An early suggestion along these
lines came in a 1993 presentation by David Skurnick on potential
explanations for the underwriting cycle. He ended his list with a
prescient observation about psychology. He commented:
Insurance managements are human beings. We don’t always
make rational decisions. We’re unduly influenced by recent
events, even when we’re making long-term plans based on
In other words, we rely on the availability heuristic when
assessing risks and are vulnerable to the resulting biases.
Skurnick also suggests conformity effects and herd behavior
as further influences on insurance management decisions. Such
comments are rare in academic literature on the underwriting
cycle but might hold the key to an improved understanding
with significant management implications.
A second example is on the consumer side of the equation.
Recall the implication of Ariely’s Economist example and related anchoring experiments—contrary to classical economics,
consumers’ demand functions are neither stable nor independent of supply and other contextual factors. This is relevant
knowledge when analyzing policyholders’ retention behavior
and sensitivity to price changes.
Analyzing Analytics—As I argued in these pages last year
(“Analyzing Analytics,” July/August 2008), a major reason why
predictive models have become ubiquitous in disparate realms
of business, medicine, sports, entertainment, government, and
education is that they compensate for the “predictable irrationality” of their users. Just as eyeglasses help us see better,
predictive models help us make better decisions.
Michael Lewis’ book Moneyball vividly recounts how statistical analysis was able to outperform the professional judg-