Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116319
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Type: Journal article
Title: More-or-less elicitation (MOLE): reducing bias in range estimation and forecasting
Author: Welsh, M.
Begg, S.
Citation: Euro Journal on Decision Processes, 2018; 6(1-2):171-212
Publisher: Springer
Issue Date: 2018
ISSN: 2193-9438
2193-9446
Statement of
Responsibility: 
Matthew B. Welsh, Steve H. Begg
Abstract: Biases like overconfidence and anchoring affect values elicited from people in predictable ways – due to people’s inherent cognitive processes. The More-Or-Less Elicitation (MOLE) process takes insights from how biases affect people’s decisions to design an elicitation process to mitigate or eliminate bias. MOLE relies on four, key insights: 1) uncertainty regarding the location of estimates means people can be unwilling to exclude values they would not specifically include; 2) repeated estimates can be averaged to produce a better, final estimate; 3) people are better at relative than absolute judgements; and, 4) consideration of multiple values prevents anchoring on a particular number. MOLE achieves these by having people repeatedly choose between options presented to them by the computerised tool rather than making estimates directly, and constructing a range logically consistent with (i.e., not ruled out by) the person’s choices in the background. Herein, MOLE is compared, across four experiments, with eight elicitation processes – all requiring direct estimation of values – and is shown to greatly reduce overconfidence in estimated ranges and to generate best guesses that are more accurate than directly estimated equivalents. This is demonstrated across three domains – in perceptual and epistemic uncertainty and in a forecasting task.
Keywords: Bias; elicitation; forecasting; overconfidence; range estimation; anchoring
Rights: © Springer-Verlag GmbH Germany, part of Springer Nature and EURO - The Association of European Operational Research Societies 2018
RMID: 0030086689
DOI: 10.1007/s40070-018-0084-5
Grant ID: http://purl.org/au-research/grants/arc/LP160101460
Appears in Collections:Australian School of Petroleum publications

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