Scientists studying decision-making often provide a set of choices, each specified with values or distributions of values, and probabilities or distributions of probabilities. For example, “Would you prefer $100 with probability 1.0 or $1 with probability .9 and $1,000 with probability 0.1?” Other decision research examines choices made in the absence of most quantitative information; for example, “Would you prefer a Ford now or a Porsche a year from now?,” “Which food would you prefer,” but models the findings with precise quantitative assumptions. Yet other research does neither; for example, modeling verbally stated choices with verbally stated heuristics. This article asks about the relevance of the first two research approaches for much of the decision-making made in life. The use of quantitative research and modeling is unsurprising, given that this approach underlies most of science. In life, values and probabilities are almost always partly or wholly vague and qualitative rather than quantitative. For example, when deciding which house to buy, there are relevant features such as size, color, neighborhood schools, construction materials, attractiveness, and many more, but the decision-maker finds it difficult and of little use to assign these precise values or weights. Nonetheless, humans have evolved to make decisions in such vaguely specified settings. I provide an example showing how a very high degree of uncertainty can defeat the application of quantitative decision-making, but such a demonstration is not critical if quantitative research and modeling produce a good understanding of and a good approximation to decision-making in the natural environment. This perspective addresses these issues.

中文翻译：

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定量研究决策是否合理？

研究决策的科学家通常会提供一组选择，每个选择都指定了值或值的分布，以及概率或概率分布。例如，“您更喜欢概率为 1.0 的 100 美元还是概率为 0.9 的 1 美元和概率为 0.1 的 1,000 美元？” 其他决策研究检查在没有大多数定量信息的情况下做出的选择；例如，“你现在更喜欢福特还是一年后的保时捷？”、“你更喜欢哪种食物”，但使用精确的定量假设对发现进行建模。然而其他研究也没有。例如，使用口头陈述的启发式方法对口头陈述的选择进行建模。本文询问前两种研究方法与生活中大部分决策的相关性。定量研究和建模的使用不足为奇，鉴于这种方法是大多数科学的基础。在生活中，价值和概率几乎总是部分或全部模糊和定性而不是定量。例如，在决定买哪所房子时，有相关的特征，如大小、颜色、邻里学校、建筑材料、吸引力等等，但决策者发现很难分配这些精确的值或用处不大或权重。尽管如此，人类已经进化到可以在这种模糊指定的环境中做出决定。我提供了一个例子来说明高度不确定性如何破坏定量决策的应用，但是如果定量研究和建模能够很好地理解和很好地近似自然中的决策，那么这样的论证并不重要环境。