AIDED HUMAN PROCESSING OF INCONCLUSIVE EVIDENCE IN DIAGNOSTIC SYSTEMS: A SUMMARY OF EXPERIMENTAL EVALUATION.
Final rept. 1 Oct 65-1 Jun 66,
OHIO STATE UNIV COLUMBUS HUMAN PERFORMANCE CENTER
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The report describes three experimental evaluations of a procedure for aiding men in combining probabilistic inconclusive information. The procedure is called Semi PIP Probabilistic Information Processing. In the semi PIP procedures, a computer relieves man of part of the task of combining probabilistic evidence. The experiments involved a simulated military threat-diagnosis task in which probabilistic data are used as a basis for deciding among alternative hypothesized threats. The Semi PIP was compared with an unaided procedure called POP Posterior Probability, and both procedures compared with a mathematically ideal combination of evidence Bayes theorem. It was found from the three experiments that 1 overall, the difference between Semi PIP and POP performance was very slight 2 POP was best when a small total amount of evidence accumulated very rapidly and, 3 Semi PIP was slightly, though consistently, superior when the total amount of evidence to be evaluated was large, when the total diagnostic impact in a set of evidence was large, or when both these conditions prevailed. Specific implications of the results for diagnostic system design and for research on basic human inference processes are summarized. A glossary of key terms from Bayesian decision theory is included in the report. Author
- Military Operations, Strategy and Tactics
- Human Factors Engineering and Man Machine Systems