Consensus diagnoses arise when several experts contribute their opinions about the relative merits of a series of competing hypothesis, and a single decision maker combines their responses and makes a decision without further discussion among the contributors. Consensus diagnoses were simulated by allowing an oracle to generate 'opinions' based on universal background knowledge and all available information about the specific problem being diagnosed. Contributors' opinions were generated by perturbing the oracle's opinion; the size of the perturbation depended on the contributor's degree of expertise. Several different aggregation functions were then used to reclaim the oracle's opinion from those of the contributors. The performance of these functions was compared as panel size and hypothesis-set size varied from two to ten. Comparative and individual analyses indicated that for panels assembled under circumstances similar to those of this study, small, simple methods work best. >
['Keung-Chi Ng', 'Bruce Abramson']
Consensus diagnosis: a simulation study