# The Expected Value of Information and the Probability of Surprise

*Risk Analysis*, vol. 19, no. 1, 1999

## Abstract

Risk assessors attempting to use probabilistic approaches to describe uncertainty often find
themselves in a data-sparse situation: available data are only partially relevant to the parameter
of interest, so one needs to adjust empirical distributions, use explicit judgmental distributions,
or collect new data. In determining whether or not to collect additional data, whether
by measurement or by elicitation of experts, it is useful to consider the expected value of
the additional information. The expected value of information depends on the prior distribution
used to represent current information; if the prior distribution is too narrow, in many
risk-analytic cases the calculated expected value of information will be biased downward.
The well-documented tendency toward overconfidence, including the neglect of potential
surprise, suggests this bias may be substantial. We examine the expected value of information,
including the role of surprise, test for bias in estimating the expected value of information,
and suggest procedures to guard against overconfidence and underestimation of the expected
value of information when developing prior distributions and when combining distributions
obtained from multiple experts. The methods are illustrated with applications to potential
carcinogens in food, commercial energy demand, and global climate change.

#### Keywords: probability; uncertainty; data; risk assessment

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