PROBabilities from EXemplars (PROBEX): a "lazy" algorithm for probabilistic inference from generic knowledge
2002 (English)In: Cognitive science, ISSN 0364-0213, E-ISSN 1551-6709, Vol. 26, no 5, 563-607 p.Article in journal (Refereed) Published
PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgmentbased on generic knowledge is presented. Its properties are that: (a) it provides an exemplar modelsatisfying 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 ecologicalrationality 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 thatweights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implementsspeed and frugality in a psychologically plausible way.
Place, publisher, year, edition, pages
2002. Vol. 26, no 5, 563-607 p.
PROBEX, Lazy algorithm, Probabilistic inference
IdentifiersURN: urn:nbn:se:uu:diva-90824DOI: 10.1207/s15516709cog2605_2OAI: oai:DiVA.org:uu-90824DiVA: diva2:163307