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More-or-less elicitation (MOLE): reducing bias in range estimation and forecasting


  • Matthew B. Welsh

    () (University of Adelaide)

  • Steve H. Begg

    (University of Adelaide)


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 computerized 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.

Suggested Citation

  • Matthew B. Welsh & Steve H. Begg, 2018. "More-or-less elicitation (MOLE): reducing bias in range estimation and forecasting," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 171-212, June.
  • Handle: RePEc:spr:eurjdp:v:6:y:2018:i:1:d:10.1007_s40070-018-0084-5
    DOI: 10.1007/s40070-018-0084-5

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    References listed on IDEAS

    1. András Vargha & Harold D. Delaney, 2000. "A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong," Journal of Educational and Behavioral Statistics, , vol. 25(2), pages 101-132, June.
    2. Uriel Haran & Don A. Moore & Carey K. Morewedge, 2010. "A simple remedy for overprecision in judgment," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(7), pages 467-476, December.
    3. Arthur Carvalho, 2016. "An Overview of Applications of Proper Scoring Rules," Decision Analysis, INFORMS, vol. 13(4), pages 223-242, December.
    4. Furnham, Adrian & Boo, Hua Chu, 2011. "A literature review of the anchoring effect," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(1), pages 35-42, February.
    5. repec:wly:riskan:v:35:y:2015:i:7:p:1230-1251 is not listed on IDEAS
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