Optimal and Adaptive Learning Models for Nondeterministic Tasks

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9310347 Daniel Friedman This research examines how people learn in nondeterministic tasks (such as medical diagnosis). Learning is difficult such tasks because occasionally a decision maker can be right for the wrong reason or (like an expert doctor with an inconclusive chart) wrong for the right reason. In experimental settings we will vary the complexity of the task, the type of decision required, and the learning environment in order to see when people are able learn effectively and when they do not. An example of the type of task would be medical decision making. A doctor examining a medical chart mentally combines the information from each separate symptom and makes a diagnosis. Doctors' diagnostic abilities increase over time as they learn more about the informativeness of each symptom and how best to combine the information. Often the chart information is inconclusive, so even the best doctor sometimes makes an incorrect diagnosis. This kind of task appears frequently in other decision domains. Economists and some other social scientists use equilibrium models that in effect assume instantaneous learning, and results of this study should be useful in showing where these models are likely to be accurate or inaccurate. The results also should be directly useful to cognitive scientists and decision makers (including medical diagnosticians) who wish to improve the learning process in nondeterministic tasks. ***