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

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.