Mitigating against cognitive bias when eliciting expert intuition

Devitt, S. Kate, Pearce, Tamara R., Perez, Tristan, & Bruza, Peter D. (2016) Mitigating against cognitive bias when eliciting expert intuition. In International Conference on Thinking, 4-6 August 2016, Brown University, Providence, RI. (Unpublished)



Experts are increasingly being called upon to build decision support systems. Expert intuitions and reflective judgments are subject to similar range of cognitive biases as ordinary folks, with additional levels of overconfidence bias in their judgments. A formal process of hypothesis elicitation is one way to mitigate against some of the impact of systematic biases such as anchoring bias and overconfidence bias. Normative frameworks for hypothesis or ‘novel option’ elicitation are available across multiple disciplines. All frameworks acknowledge the importance and difficulty of generating hypotheses that are a) sufficiently numerous b) lateral and c) relevant and d) plausible. This paper explores whether systematic hypothesis generation can generate the desired degree of creative, ‘out-of-the-box’ style options given that abductive reasoning is one of the least tractable styles of thinking that appears to shirk systematization. I argue that while there is no universal systematic hypothesis generation procedure, experts can be exposed to deliberate and systematic information ecosystems to reduce the prevalence of certain types of cognitive biases and improve decision support systems.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

12 since deposited on 01 Sep 2016
12 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 98542
Item Type: Conference Item (Poster)
Refereed: No
Additional URLs:
Keywords: Abduction, Cognitive bias, Option generation
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Faculty of Law
Current > Institutes > Institute for Future Environments
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Law
Copyright Owner: Copyright 2016 [please consult the authors]
Deposited On: 01 Sep 2016 23:09
Last Modified: 10 Sep 2016 07:41

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page