Expectational v. Instrumental Reasoning: What Statistics Contributes to Practical Reasoning

M. Thalos
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Abstract

Utility theories—both Expected Utility (EU) and non-Expected Utility (non-EU) theories—offer numericalized representations of classical principles meant for the regulation of choice under conditions of risk—a type of formal representation that reduces the representation of risk to a single number. I shall refer to these as risk-numericalizing theories of decision . I shall argue that risk--numericalizing theories (referring both to the representations and to the underlying axioms that render numericalization possible) are not satisfactory answers to the question: “How do I take the (best) means to my ends?” In other words, they are inadequate or incomplete as instrumental theories. They are inadequate because they are poor answers to the question of what it is for an option to be instrumental towards an end. To say it another way, they do not offer a sufficiently rich account of what it is for something to be a means (an instrument) toward an end.
期望推理与工具推理:统计学对实际推理的贡献
效用理论——期望效用(EU)和非期望效用(non-EU)理论——为风险条件下的选择调节提供了经典原则的数字表示——一种将风险表示减少到单个数字的形式表示。我将把这些理论称为风险数值化决策理论。我认为,风险数字化理论(既涉及表征,也涉及使数字化成为可能的基本公理)并不能令人满意地回答这个问题:“我如何采取(最好的)手段达到我的目的?”换句话说,它们作为工具理论是不充分或不完整的。它们是不充分的,因为它们不能很好地回答这样一个问题,即一种选择对实现目的有什么帮助。换句话说,他们并没有提供一个足够丰富的说明,什么是达到目的的手段(工具)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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