Premium
Errors in the quantification of uncertainty: A product of heuristics or minimal probability knowledge base?
Author(s) -
Gebotys R. J.,
ClaxtonOldfield S. P.
Publication year - 1989
Publication title -
applied cognitive psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 100
eISSN - 1099-0720
pISSN - 0888-4080
DOI - 10.1002/acp.2350030304
Subject(s) - heuristics , representativeness heuristic , inference , test (biology) , psychology , knowledge base , computer science , session (web analytics) , machine learning , artificial intelligence , cognitive psychology , social psychology , world wide web , paleontology , biology , operating system
The use of intuitive heuristics (e. g. representativeness and availability) has been put forward as an explanation for people's assignment of probabilities (Tversky and Kahneman, 1971). This phenomenon is seen as robust since experts as defined by education (professional psychologists), despite advanced training in statistics and methodology, rely on the same heuristics as novices (lay people). Both experts and novices as defined by education were studied in a series of experiments and further classified as experts and novices according to their probability knowledge base, prior to receiving (or not) a brief (15‐minute) training session. Immediately following training, subjects completed a probability test which consisted of ten Tversky and Kahneman (e. g. 1974) problems. The training significantly increased the number of problems correctly solved on the probability test and eliminated the expert/novice education classification. The results of a follow‐up test 5 weeks after the experiment indicated that the training group maintained its superior performance. It is proposed that failure to use proper methods of probability assignment may not be due to intrinsic human inference biases or heuristics, but is a result of a minimal probability knowledge base.