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.

中文翻译：

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或多或少的启发（MOLE）：减少范围估计和预测中的偏差

由于人们固有的认知过程，过度自信和锚定之类的偏见会以可预测的方式影响人们产生的价值。或多或少的启发（MOLE）过程从偏见如何影响人们的决策中获取见解，从而设计出一种减轻或消除偏见的启发过程。MOLE依赖于四个关键洞察力：（1）估计位置的不确定性意味着人们可能不愿意排除他们不会明确包括的价值；（2）可以对重复的估计进行平均，以得出更好的最终估计；（3）相对判断胜于绝对判断；（4）考虑多个值会阻止锚定在特定数字上。MOLE通过让人们在计算机工具提供给他们的选项之间反复选择而不是直接进行估算来实现这些目标，并在逻辑上构造一个与背景中的人的选择一致（即不排除其选择范围）的范围。在这里，在四个实验中，对MOLE进行了比较，将其与八个启发过程（均需要直接估计值）进行了比较，结果表明，它大大降低了估计范围内的过分自信，并产生了比直接估计的等价物更准确的最佳猜测。这在感知和认知不确定性以及预测任务的三个领域得到了证明。具有八个启发过程-所有这些过程都需要直接估计值-并且被证明可以大大减少估计范围内的过度自信，并且可以产生比直接估计的等效结果更准确的最佳猜测。这在感知和认知不确定性以及预测任务的三个领域得到了证明。具有八个启发过程-所有这些过程都需要直接估计值-并且被证明可以大大减少估计范围内的过度自信，并且可以产生比直接估计的等效结果更准确的最佳猜测。这在感知和认知不确定性以及预测任务的三个领域得到了证明。