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Correcting Common Errors in Probabilistic Evaluations: Efficacy of Debiasing M. B. Welsh; M. B. Welsh U. Adelaide Search for other works by this author on: This Site Google Scholar S. H. Begg; S. H. Begg U. Adelaide Search for other works by this author on: This Site Google Scholar R. B. Bratvold R. B. Bratvold U. Stavanger Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. Paper Number: SPE-102188-MS https://doi.org/10.2118/102188-MS Published: September 24 2006 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Get Permissions Search Site Citation Welsh, M. B., Begg, S. H., and R. B. Bratvold. "Correcting Common Errors in Probabilistic Evaluations: Efficacy of Debiasing." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. doi: https://doi.org/10.2118/102188-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search Abstract 'Debiasing" refers to techniques taught during risk and uncertainty training that enable trainees to avoid or reduce errors arising from cognitive biases. We discuss current techniques and their applicability to oil and gas decisions.To test the debiasing efficacy of sitting a general decision making course, engineering students completed a survey [1] designed to test for the well-known overconfidence and anchoring [2] biases both immediately prior to and following a short course on the psychology of decision making containing sections on cognitive biases and debiasing. No feedback or specific training was included in the course, - which described debiasing techniques for various cognitive biases including those tested within the battery.Participants' results on the overconfidence task showed a 21% improvement in calibration – from 27% to 48%, indicating a significant impact of knowing about biases and debiasing methods on susceptibility to overconfidence. However, no improvement was seen in participants' performance on the anchoring task.The first result, taken in conjunction with findings from Welsh et al [1], suggests that training in dealing with uncertainty, and knowledge of the modes of action of cognitive biases, can reduce overconfidence but that this benefit is reduced as time since training increases. The second result, again in conjunction with previous findings [1, 3], suggests that training alone is insufficient to overcome the anchoring bias in isolation from domain specific knowledge. That is, to reduce the adverse effects of the anchoring bias, trainees need to be both trained in debiasing techniques and possess knowledge about the field in question such that they can apply the debiasing techniques to their knowledge.We use these preliminary, experimental results to highlight and inform discussion regarding the potential benefits of debiasing. Specifically, we argue that the efficacy of debiasing within oil and gas decision making requires further research – in particular, a longitudinal study to track the efficacy of debiasing over time. We conclude that an understanding of the potential and limitations of training in debiasing techniques will allow management to formulate effective strategies for the introduction and maintenance of debiasing training to reduce the impact of errors arising from cognitive biases. Additionally, it is argued that introducing future industry personnel to these concepts during their university studies will ease their integration into industry practice Keywords: Upstream Oil & Gas, reserves evaluation, knowledge, participant, Artificial Intelligence, questionnaire, student, cognitive bias, welsh, true value Subjects: Reserves Evaluation, Risk Management and Decision-Making, Probabilistic methods Copyright 2006, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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