Name
Abstract | ||
|---|---|---|
A Probabilistic Information Processing System (PIP) uses men and machines in a novel way to perform diagnostic information processing. Men estimate likelihood ratios for each datum and each pair of hypotheses under consideration or a sufficient subset of these pairs. A computer aggregates these estimates by means of Bayes' theorem of probability theory into a posterior distribution that reflects the impact of all available data on all hypotheses being considered. Such a system circumvents human conservatism in information processing, the inability of men to aggregate information in such a way as to modify their opinions as much as the available data justify. It also fragments the job of evaluating diagnostic information into small separable tasks. The posterior distributions that are a PIP's output may be used as a guide to human decision making or may be combined with a payoff matrix to make decisions by means of the principle of maximizing expected value. A large simulation-type experiment compared a PIP with three other information processing systems in a simulated strategic war setting of the 1970's. The difference between PIP and its competitors was that in PIP the information was aggregated by computer, while in the other three systems, the operators aggregated the information in their heads. PIP processed the information dramatically more efficiently than did any competitor. Data that would lead PIP to give 99:1 odds in favor of a hypothesis led the next best system to give 4¿: 1 odds. |
Year | DOI | Venue |
|---|---|---|
1968 | 10.1109/TSSC.1968.300119 | Systems Science and Cybernetics, IEEE Transactions |
Keywords | Field | DocType |
process design,optimization,cybernetics,system design,computational modeling,likelihood ratio,space technology,bayes theorem,entropy,probability theory,data processing,expected value,information processing,posterior distribution,thermodynamics,information theory,diagnosis | Information theory,Data processing,Information processing,Computer science,Information processor,Posterior probability,Artificial intelligence,Odds,Probabilistic logic,Machine learning,Bayes' theorem | Journal |
Volume | Issue | ISSN |
4 | 3 | 0536-1567 |
Citations | PageRank | References |
16 | 37.96 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ward Edwards | 1 | 16 | 37.96 |
| Lawrence Phillips | 2 | 16 | 37.96 |
| Hays, William L. | 3 | 16 | 37.96 |
| Barbara Goodman | 4 | 16 | 37.96 |