Structuring knowledge retrieval: An analysis of decomposed quantitative judgments

Subjects were asked to estimate the answers to 16 questions concerning uncertain quantities like, “How many people are employed by hospitals in the U.S.?” under five different aiding conditions. The most-aided group (Full Algorithm) was given a complete algorithm and asked to make estimates for all the parts of the algorithm and to combine the parts as indicated to arrive at an estimate of the desired quantity. The second group (Partial Algorithm) was given the same algorithm without indications of how to combine the parts. After making estimates of the parts, these subjects then estimated the desired quantity. The third group (List & Estimate) was asked to list components or factors they thought were relevant, make an estimate of each item on their list, and then estimate the desired quantity. The fourth group (List) was asked to make such a list, but they were not asked to make estimates of each item before making an estimate of the desired quantity. The fifth group received no aid. The results generally showed improved performance in terms of both accuracy and consistency across subjects with increasing structure of the aid. Generalization of these results to practical estimation situations is possible but limited by the need, in real situations, for the estimator to develop the algorithm, a task that was done here by the experimenters.