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PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge
Author(s) -
Juslin Peter,
Persson Magnus
Publication year - 2002
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2605_2
Subject(s) - computer science , probabilistic logic , frugality , inference , bounded rationality , similarity (geometry) , rationality , representation (politics) , artificial intelligence , algorithm , machine learning , theoretical computer science , epistemology , ecology , philosophy , politics , political science , law , image (mathematics) , biology
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 abstractions; (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.