Filtering information from human experts

Author

Mendel, Max B. ; Sheridan, Thomas B.

Author_Institution

Dept. of Mech. Eng., MIT, Cambridge, MA, USA

Volume

19

Issue

1

fYear

1989

Firstpage

6

Lastpage

16

Abstract

The authors propose a model, or filter, for debiasing opinions from multiple experts and combining them into a single consistent estimate of some variable of interest. A distinguishing feature of the approach consists of making the calibration of experts an integral part of filtering. This enables the filter to learn from previous experience with the experts. The theoretical development takes a Bayesian perspective, using B. de Finetti´s notion of exchangeability (1964). Experimental results with a preliminary computer implementation of the filter show that its estimates are better than those from comparable filters that do not involve calibration

Keywords

Bayes methods; filtering and prediction theory; knowledge engineering; learning systems; Bayesian perspective; computer implementation; exchangeability; expert calibration; human experts; information filtering; learning; opinion combination; opinion debiasing; Bayesian methods; Calibration; Displays; Electrical equipment industry; Humans; Industrial control; Information filtering; Information filters; Man machine systems; Supervisory control;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.24527

Filename

24527