Measuring Strength of Evidence

衡量证据的强度

• 批准号: 8711573
• 负责人: David Krantz
• 金额: $ 26.5万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-08-15 至 1991-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8711573&HistoricalAwards=false
• 关键词: Measuring; Strength; Evidence

In science, in the law, in business, and in everyday social interaction, people attempt to base their beliefs on the available evidence. The standard (Bayesian) theory asserts that people's beliefs consist of probabilities for various propositions, for example, the probability that a particular scientific hypothesis is correct or the probability that an accused person is guilty as charged. But proposed applications of the Bayesian theory in science and in law have encountered strong and quite justifiable resistance, largely because the theory demands that people express detailed beliefs even in the absence of any evidence (prior probabilities). This research will develop non-Bayesian theories in which perceived strength of evidence will be measured on a numerical scale quite different from probability. This measurement system is well adapted to science and the law since, in the absence of evidence, all beliefs can be rated as zero. One main research activity will be to determine formulae for converting probabilistic and statistical information to the strength-of-evidence measurement scale, and thus will provide a basis for systematic integration of such information with other kinds of evidence. Another research activity will be to develop graded standards of statistical and nonstatistical evidence, against which arbitrary items of evidence can be compared and evaluated, much as a meterstick provides a graded series of lengths, against which the lengths of other objects can be measured. The graded standards for evidence will be most useful if they can be applied analytically, decomposing a complex body of evidence into parts, each of which can be evaluated by comparison with the standards. One of the principal research activities will be to evaluate the bias inherent in such analytic decompositions. That research will compare evaluations of partial evidence by two groups of people, those who are aware of the full body of evidence and those who are not. If possible, conditions will be found for which these two groups evaluate evidence identically, i.e., there is no appreciable bias due to parts of the evidence that are ignored in any step of the analytic process. The research will also test conditions under which strong biases are expected.

在科学、法律、商业和日常社会中， 互动，人们试图将他们的信仰建立在 现有证据。 标准（贝叶斯）理论认为， 人们的信念由各种可能性组成 命题，例如，一个特定的 科学假设是正确的，或者 被告人被指控有罪。 但是， 贝叶斯理论在科学和法律中的应用 强大而合理的抵抗，主要是因为 理论要求人们表达详细的信念，即使在 先验概率（prior probabilities）。 这项研究将发展非贝叶斯理论，其中 证据的感知强度将根据数字来衡量， 比例与概率完全不同。 该测量系统 是很好地适应科学和法律，因为，在没有 证据，所有的信念都可以被评为零。 一项主要的研究活动将是确定 将概率和统计信息转换为 证据强度测量量表，因此将提供一个 将这些信息与其他信息系统地结合的基础 各种证据。 另一项研究活动将是开发 统计和非统计证据的分级标准， 任意的证据可以与之进行比较， 评估，就像一个米尺提供了一个分级系列， 长度，其他对象的长度可以与之相对， 测定了 证据的分级标准如果能够发挥作用， 可以应用于分析，分解复杂的证据， 分为几部分，每一部分都可以通过与 标准 主要研究活动之一将是 评估这种分析分解中固有的偏差。 的 研究将比较两个人对部分证据的评估， 一群人，那些知道全身的人， 证据和那些没有证据的人。 如果可能的话， 发现这两个群体对证据的评估是相同的， 也就是说，由于部分证据， 在分析过程的任何一步都被忽略了。 的 研究还将测试在何种条件下， 预期