SEQUENTIAL CHOICE UNDER AMBIGUITY - INTUITIVE SOLUTIONS TO THE ARMED-BANDIT PROBLEM

The process by which individuals learn from feedback when making recur rent choices among ambiguous alternatives is explored. We describe an experiment in which subjects solve a variant of the classic armed-band it problem of dynamic decision theory, set in the context of airline c hoice. Subjects are asked to make repeated choices between two hypothe tical airlines, one having an on-time departure probability which is k nown a priori, and the other has an ambiguous probability whose true v alue can only be discovered by making sample trips on the airline. Sub jects attempt to make choices in such a way as to maximize the total n umber of one-time departures over a fixed planning horizon. We examine the extent to which actual choice patterns over time are consistent w ith those which would be made by a decision maker acting as an optimal Bernoulli sampler. The data offer support for a number of expected-an d some unexpected-departures from optimality, including a tendency to underexperiment with promising options and overexperiment with unpromi sing options, and a tendency to increasingly switch between airlines a s the average base rate of departures decreases. Implications of the w ork for the descriptive validity of normative dynamic decision models is explored, as well as for the generalizability of previous findings about choice under ambiguity to dynamic settings.