[论文解读] The Estimation of Subjective Probabilities via Categorical Judgments of Uncertainty

Alf Zimmer|arXiv (Cornell University)|Mar 27, 2013

Decision-Making and Behavioral Economics被引用 3

一句话总结

本文研究了人们如何使用类别化（语言）判断来估计主观概率，表明与数值估计相比，语言回应能降低认知负荷和偏差。通过可能性理论和模糊逻辑，本文对语言不确定性类别进行建模，并表明在复杂推理任务中，人们使用语言表达时对基率更敏感，且更不容易陷入谬误。

ABSTRACT

Theoretically as well as experimentally it is investigated how people represent their knowledge in order to make decisions or to share their knowledge with others. Experiment 1 probes into the ways how people 6ather information about the frequencies of events and how the requested response mode, that is, numerical vs. verbal estimates interferes with this knowledge. The least interference occurs if the subjects are allowed to give verbal responses. From this it is concluded that processing knowledge about uncertainty categorically, that is, by means of verbal expressions, imposes less mental work load on the decision matter than numerical processing. Possibility theory is used as a framework for modeling the individual usage of verbal categories for grades of uncertainty. The 'elastic' constraints on the verbal expressions for every sing1e subject are determined in Experiment 2 by means of sequential calibration. In further experiments it is shown that the superiority of the verbal processing of knowledge about uncertainty guise generally reduces persistent biases reported in the literature: conservatism (Experiment 3) and neg1igence of regression (Experiment 4). The reanalysis of Hormann's data reveal that in verbal Judgments people exhibit sensitivity for base rates and are not prone to the conjunction fallacy. In a final experiment (5) about predictions in a real-life situation it turns out that in a numerical forecasting task subjects restricted themselves to those parts of their knowledge which are numerical. On the other hand subjects in a verbal forecasting task accessed verbally as well as numerically stated knowledge. Forecasting is structurally related to the estimation of probabilities for rare events insofar as supporting and contradicting arguments have to be evaluated and the choice of the final Judgment has to be Justified according to the evidence brought forward. In order to assist people in such choice situations a formal model for the interactive checking of arguments has been developed. The model transforms the normal-language quantifiers used in the arguments into fuzzy numbers and evaluates the given train of arguments by means of fuzzy numerica1 operations. Ambiguities in the meanings of quantifiers are resolved interactively.

研究动机与目标

• 探讨个体在决策情境中如何表征和沟通主观不确定性。
• 调查语言（类别化）不确定性判断是否比数值估计施加更少的认知负荷。
• 使用可能性理论对语言不确定性术语的个体差异解释进行建模。
• 评估语言处理是否能减少已知的认知偏差，如保守主义和回归忽视。
• 开发一种基于模糊数值运算的正式模型，用于在预测任务中实现交互式论证评估。

提出的方法

• 开展受控实验，比较在估计事件频率时使用语言与数值回应模式的差异。
• 使用顺序校准法确定个体特定的、‘弹性’的语言不确定性术语约束（例如，‘可能’、‘罕见’）。
• 应用可能性理论，对语言不确定性表达的个体差异解释进行建模。
• 重新分析Hormann的数据，检验在语言与数值判断中对基率敏感性及合取谬误的表现。
• 开发一种正式模型，将自然语言量词转化为模糊数，以支持论证评估。
• 在基于论证的预测任务中实现量词含义的交互式模糊性消解。

实验结果

研究问题

• RQ1 回应模式（语言 vs. 数值）如何影响主观概率估计的准确性和认知负荷？
• RQ2 个体的语言不确定性类别在不同语境下在多大程度上反映稳定且可解释的约束？
• RQ3 使用语言判断是否能减少概率推理中的保守主义和回归忽视等偏差？
• RQ4 当人们用语言表达不确定性时，是否比用数值表达时对基率更敏感？
• RQ5 基于模糊逻辑的模型能否有效支持通过整合语言和数值论证来实现预测中的推理？

主要发现

• 语言回应模式比数值估计施加的认知负荷更小，因为受试者在信息处理中表现出更少的扭曲。
• 使用语言判断的受试者同时调用了语言和数值知识，而数值预测者则局限于数值数据。
• 在语言预测任务中，参与者对基率表现出更高的敏感性，且更不容易陷入合取谬误。
• 对Hormann数据的重新分析表明，语言判断并未导致合取谬误，这挑战了关于语言推理的既有假设。
• 模糊逻辑模型成功消解了量词的模糊性，并实现了预测中论证的结构化、交互式评估。
• 可能性理论有效捕捉了个体对语言不确定性术语解释的差异。