Many important decisions are based on cutoffs. For example, a doctor may suspect, but not be sure, that a child has an ear infection. The doctor's suspicion is a judgment based on the child's symptoms, and antibiotics will be prescribed if that suspicion is strong enough, that is, if it is above some cutoff. Other "cutoff" decisions are police deciding to arrest suspects, juries deciding guilt or innocence, airport security deciding to detain passengers, emergency managers deciding to evacuate a hurricane-threatened area, and personnel managers hiring job applicants. In each case, the decision to act or not is based on a judgment that must be made under uncertainty. Decisions can result in two kinds of error: acting when inaction is more appropriate or not acting when action should be taken. Such errors can be costly and can never be avoided entirely, but understanding their causes can contribute to reducing them.
This project will investigate how people learn to make cutoff decisions when their feedback is limited. Feedback is limited when decision makers do not find out if they made the right decision. Often the decision itself eliminates the possibility of feedback. For example, personnel managers rarely find out if applicants not hired would have been good employees. The research will be conducted using a new laboratory procedure designed to simulate decision-making conditions. Important properties of decision making situations will be varied, including the amount of uncertainty, the probability that the event of interest occurs, and the costs and benefits of errors and correct decisions. This project will establish a new paradigm for studying an important class of decisions, will apply that paradigm to critical issues of human decision-making performance, and will potentially lead to methods for improving performance.
