SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Mueller ST. J. Cogn. Eng. Decis. Mak. 2009; 3(2): 111-130.

Copyright

(Copyright © 2009, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1518/155534309X441871

PMID

unavailable

Abstract

The recognition-primed decision (RPD) model (Klein, 1993) is an account of expert decision making that focuses on how experts recognize situations as being similar to past experienced events and thus rely on memory and experience to make decisions. A number of computational models exist that attempt to account for similar aspects of expert decision making. In this paper, I briefly review these extant models and propose the Bayesian recognitional decision model (BRDM), a Bayesian implementation of the RPD model based primarily on models of episodic recognition memory (Mueller & Shiffrin, 2006; Shiffrin & Steyvers, 1997). The proposed model accounts for three important factors used by experts to make decisions: evidence about a current situation, the prior base rate of events in the environment, and the reliability of the information reporter. The Bayesian framework integrates these three aspects of information together in an optimal way and provides a principled framework for understanding recognitional decision processes.

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print