THE INFLUENCE OF EXPERIENCE AND INPUT INFORMATION UPON POSTERIOR PROBABILITY ESTIMATION IN A SIMULATED THREAT-DIAGNOSIS SYSTEM
Abstract
Two experiments are described in which posterior probability estimates made by humans are compared with similar estimates made by a computer using a modification of Bayes' theorem incorporating human estimates of P(D/H). The task was to estimate, on the basis of intelligence data from a simulated threat-evaluation situation, the likelihood of various alternative hypotheses that could account for the observed data. The purpose of the first experiment was to determine the effect of increased experience upon the human's ability to estimate posterior probabilities. The purpose of the second experiment was to compare human and automated posterior probability estimates under several levels of input data fidelity. It was predicted that, under low fidelity conditions, human posterior probability estimates would become increasingly inferior to automated solutions. This hypothesis was only partially confirmed. In both experiments, but particularly in the second, the humans provided higher posterior probability estimates than the certainty in the data justified. With respect to the desing of diagnostic systems, the present research tends to confirm the feasibility of automated Bayesian hypothesis-selection incorporating expert human estimates of the conditional probabilities P(D/H).
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1965
- Accession Number
- AD0615758
Entities
People
- David A. Schum
- Irwin L. Goldstein
- Jack F. Southard
Organizations
- Ohio State University