Resource allocation decision making in an uncertain environment

This experiment investigated how individuals learn to allocate limited resources across competing activities in order to maximize their objective when the form of the objective function is uncertain. On each of 25 trials, subjects selected an allocation policy and received a corresponding profit. The objective function that was used to assign profits to allocations was unknown initially and was learned through trial by trial outcome feedback. A total of 144 subjects were randomly assigned to nine groups constructed from a 3 (objective function form) by 3 (amount of error variability) factorial design. Several measures of learning were analyzed including (a) the distance between the current and optimal allocation policy as a function of training, (b) the magnitude of change in allocation as a function of past profits, and (c) the direction of change in allocations as a function of the direction of change in past profits. The results provide evidence for an integrated approach to learning — a functional learning process based on global information about the shape of the objective function and a hill-climbing learning process based on local information about trial to trial improvements in profit.