Greenspan's Synthesis of the 'Keynes-Knight' Approach and the Ramsey-De Finetti-Savage Approach in Decision Making: A Continuum Exists Between Situations of No Knowledge and Complete Knowledge

M. E. Brady
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Abstract

The differences between Knight’s approach in Risk, Uncertainty and Profit (1921) and Keynes’s logical theory of probability approach in the A Treatise on Probability (1921), on the one hand, and the Ramsey-Savage-de Finetti Subjective or Bayesian approach, on the other hand, are based on the question of whether it is always possible or not to estimate a probability with a precise, exact, numerical value. Keynes and Knight argued that it is not always possible to provide a precise numerical answer to the question, “What is the probability of this outcome relative to this evidence?”, while Ramsey, de Finetti and Savage argued that it was always possible. de Finetti and Savage added a qualification regarding their views on numerical probability only in the case involving the initial conditions at the beginning of a probability assessment. Due to a lack of enough evidence in the beginning stage of a probability assessment, an imprecise estimate of probability could result. However, as time went on, more additional, sufficient evidence would result that would always lead to a precise probability estimate. Much important evidence would be missing or vague or Ambiguous (Daniel Ellsberg’s term). Keynes and Knight argued that that there would be many cases of what Keynes called indeterminate probability estimates, where additional evidence would not be sufficient to lead to a precise probability by the time a decision had to be made. It is impossible to postpone many financial, economic, and business decisions until more, relevant information has accumulated that would lead to the convergence of a imprecise probability to a precise probability at some point in the future. Thus, it is the relative strength of the evidence that determines if a numerically precise probability can be assigned. The mathematical laws of the probability calculus assume that the available evidence used is relevant and complete before a probability calculation takes place. This is a situation of strong evidence. On the other hand, if evidence is missing or not available, one is dealing with a situation of weak evidence. Greenspan cuts through the logical, epistemological, and philosophical analysis made by Keynes and Knight to arrive at a simple and direct definition of uncertainty that entails the work of Keynes and Knight.
格林斯潘对决策中“凯恩斯-奈特”方法与拉姆齐-德菲内蒂-萨维奇方法的综合:无知识和完全知识之间存在连续体
一方面,奈特在《风险、不确定性和利润》(1921)中的方法与凯恩斯在《概率论》(1921)中的逻辑概率论方法之间的差异,另一方面,拉姆齐-萨维奇-德菲内蒂主观或贝叶斯方法之间的差异,是基于一个问题,即是否总是有可能用精确的、精确的数值来估计概率。凯恩斯和奈特认为,对于“这种结果相对于这种证据的概率是多少?”这个问题,我们不可能总是给出一个精确的数字答案。,而拉姆齐、德菲内蒂和萨维奇则认为这是可能的。de Finetti和Savage仅在涉及概率评估开始时的初始条件的情况下,才对其关于数值概率的观点增加了一个限制条件。由于在概率评估的开始阶段缺乏足够的证据,可能导致对概率的不精确估计。然而,随着时间的推移,会产生更多额外的、充分的证据,这些证据总能得出一个精确的概率估计。许多重要的证据将会丢失或模糊或模棱两可(丹尼尔·埃尔斯伯格的术语)。凯恩斯和奈特认为,会有很多凯恩斯所说的不确定概率估计的情况,在必须做出决定的时候,额外的证据不足以得出精确的概率。推迟许多金融、经济和商业决策是不可能的,直到更多的相关信息积累起来,这些信息将导致不精确概率在未来的某个时刻收敛为精确概率。因此,是证据的相对强度决定了是否可以分配一个数值上精确的概率。概率演算的数学规律假定在进行概率计算之前所使用的可用证据是相关的和完整的。这是一个强有力的证据。另一方面,如果证据缺失或无法获得,则是在处理证据不足的情况。格林斯潘通过凯恩斯和奈特的逻辑、认识论和哲学分析,得出了一个简单而直接的不确定性定义,这需要凯恩斯和奈特的工作。
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