The University of Southampton
University of Southampton Institutional Repository

Naturalistic heuristics for decision making

Naturalistic heuristics for decision making
Naturalistic heuristics for decision making

Over the last 20 years, both naturalistic decision making and fast and frugal heuristics programs have radically broken with mainstream decision science, moving beyond the confines of artificial tasks and safe academic laboratories. We document commonalities of these programs and discuss ways in which a synthesis could contribute to a more relevant, precise, predictive, and effective decision science. We begin by reviewing the common roots and philosophies of the two programs, such as their respect for the capable decision maker and their acknowledgment of the importance of task ecology. We then identify four specific areas of synergetic potential, including ecological rationality and metacognition. Our review culminates in a case study of naturalistic heuristics based on a particular class of fast and frugal heuristics. These fast and frugal trees provide examples of effective, well-specified decision-making algorithms applied in a naturalistic domain: emergency medical diagnosis. By leveraging the strengths of each program, we point out some of the ways in which more sustainable progress can be fostered on issues that matter the most—for example, decisions that save and transform lives.

1555-3434
256-274
Keller, Niklas
91a86565-f90e-476e-872d-1bad6c58a80a
Cokely, Edward T.
d120e243-a8e5-4608-8108-3e072d77c269
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Wegwarth, Odette
20d152b1-1b5e-4ed8-a928-53b1a39911c1
Keller, Niklas
91a86565-f90e-476e-872d-1bad6c58a80a
Cokely, Edward T.
d120e243-a8e5-4608-8108-3e072d77c269
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Wegwarth, Odette
20d152b1-1b5e-4ed8-a928-53b1a39911c1

Keller, Niklas, Cokely, Edward T., Katsikopoulos, Konstantinos V. and Wegwarth, Odette (2010) Naturalistic heuristics for decision making. Journal of Cognitive Engineering and Decision Making, 4 (3), 256-274. (doi:10.1518/155534310X12844000801168).

Record type: Article

Abstract

Over the last 20 years, both naturalistic decision making and fast and frugal heuristics programs have radically broken with mainstream decision science, moving beyond the confines of artificial tasks and safe academic laboratories. We document commonalities of these programs and discuss ways in which a synthesis could contribute to a more relevant, precise, predictive, and effective decision science. We begin by reviewing the common roots and philosophies of the two programs, such as their respect for the capable decision maker and their acknowledgment of the importance of task ecology. We then identify four specific areas of synergetic potential, including ecological rationality and metacognition. Our review culminates in a case study of naturalistic heuristics based on a particular class of fast and frugal heuristics. These fast and frugal trees provide examples of effective, well-specified decision-making algorithms applied in a naturalistic domain: emergency medical diagnosis. By leveraging the strengths of each program, we point out some of the ways in which more sustainable progress can be fostered on issues that matter the most—for example, decisions that save and transform lives.

This record has no associated files available for download.

More information

Published date: 1 January 2010

Identifiers

Local EPrints ID: 439462
URI: http://eprints.soton.ac.uk/id/eprint/439462
ISSN: 1555-3434
PURE UUID: 92ae327b-d58f-458b-933f-f8ef69e5a18c
ORCID for Konstantinos V. Katsikopoulos: ORCID iD orcid.org/0000-0002-9572-1980

Catalogue record

Date deposited: 23 Apr 2020 16:54
Last modified: 09 Jan 2022 03:55

Export record

Altmetrics

Contributors

Author: Niklas Keller
Author: Edward T. Cokely
Author: Odette Wegwarth

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×