Open AccessARE BAYESIAN DECISIONS ARTIFICIALLY INTELLIGENT? THE EFFECT OF TASK AND PERSONALITY ON CONSERVATISM IN PROBABILISTIC INFERENCE 1
Author(s) -
Alker Henry A.,
Hermann Margaret G.
Publication year - 1970
Publication title -
ets research bulletin series
Language(s) - English
Resource type - Journals
eISSN - 2333-8504
pISSN - 0424-6144
DOI - 10.1002/j.2333-8504.1970.tb00421.x
Subject(s) - conservatism , bayesian probability , bayes' theorem , psychology , task (project management) , probabilistic logic , bayesian inference , inference , relevance (law) , personality , affect (linguistics) , bayesian statistics , cognitive psychology , information processing , econometrics , computer science , social psychology , artificial intelligence , mathematics , economics , management , communication , politics , political science , law
ABSTRACT A 2 × 3 design was used to explore the effects of complexity and importance of decision on conservatism in probabilistic inference. 120 undergraduate males considered either two or three alternatives for one of three problems differing in perceived importance. Upon receiving certain information, subjects indicated which of the several alternative explanations for their problem was more or most probable. These probability statements were compared with “optimal” probabilities arrived at using Bayes' theorem. The more important and complex the decisions were perceived to be, the more conservative the subjects were when compared to “optimal” Bayesian values ( p < .01). The differences were more pronounced as the number of alternatives to be considered increased ( p < .01). Seven individual difference variables measuring adequacy in processing information were unrelated to conservatism. A process analysis suggested that strongly held initial opinions can limit the judged relevance of subsequent information and, in turn, affect the “optimal” Bayesian decision.