Department of Economics, UCSC

Most theories of risky choice postulate that a decision maker maximizes the expectationof a Bernoulli (or utility or similar) function. We tour 60 years of empirical search and concludethat no such functions have yet been found that are useful for out-of-sample prediction. Nor dowe find practical applications of Bernoulli functions in major risk-based industries such asfinance, insurance and gambling. We sketch an alternative approach to modeling risky choicethat focuses on potentially observable opportunities rather than on unobservable Bernoullifunctions.