PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge

• Published in: Cognitive science Vol. 26; no. 5; pp. 563 - 607
• Main Authors: Juslin, Peter, Persson, Magnus
• Format: Journal Article
• Language: English
• Published: 10 Industrial Avenue, Mahwah, NJ 07430‐2262, USA Lawrence Erlbaum Associates, Inc 01.09.2002; Taylor & Francis; Wiley Subscription Services, Inc
• Subjects: Algorithms; Cognition & reasoning; History of science and technology; Lazy algorithm; Logic and calculus; Mathematical sciences and techniques; Probabilistic inference; Probability; PROBEX; Pattern; Similarity; Rationality; Genus; Environment; Inference; Ecology; Cognitive Psychology; Algorithm; Knowledge; Decision; Probabilities
• ISSN: 0364-0213; 1551-6709; 1551-6709
• DOI: 10.1207/s15516709cog2605_2

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed ions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.