Non-Additive Utility Theories for Uncertain Alterations Into Mixed Consequences

将不确定改变转化为混合后果的非相加效用理论

• 批准号: 9808057
• 负责人: R. Duncan Luce
• 金额: $ 22.86万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-15 至 2002-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9808057&HistoricalAwards=false
• 关键词: Non; Additive; Utility; Theories; Uncertain

Luce Experimental studies and attempts by decision analysists to use extant models to predict people's behavior for decisions involving mixed consequences of both gains and losses have not been very successful. Examples where trouble has been seen is in medical decision making. Because of the great importance of such applications, it is urgent that better models be developed and tested. Recent theoretical work by the PI has shown why the troubles may arise. He has shown under very plausible, empirically testable conditions that the computations one should use considerably more complex expressions than the weighted averages of utilities of consequences that are commonly employed. Indeed, he derived two distinct mathematical forms from slightly different assumptions. One goal of the project is to pit these assumptions against one another directly and to see empirically which is more accurate. Also, attempts will be made to determine whether these new expressions actually provide improved predictions of observed behavior. Some numerical fitting will be based on existing data, but considerable additional data are needed for more thorough-going tests. The PI will continue to develop the area theoretically. In particular, he will focus considerable attention on the form of the weighting functions whose properties as far less specified than are those of the utility function.

实验研究和决策分析学家尝试使用现有模型来预测人们在涉及得失混合后果的决策时的行为，但都不是很成功。出现问题的例子是医疗决策。由于这些应用的重要性，迫切需要开发和测试更好的模型。PI最近的理论研究揭示了这些麻烦可能出现的原因。他已经表明，在非常合理的、经验可检验的条件下，计算应该使用比通常使用的结果效用加权平均要复杂得多的表达式。事实上，他从稍微不同的假设中推导出了两种截然不同的数学形式。该项目的一个目标是直接将这些假设相互对立，并从经验上看哪个更准确。此外，还将尝试确定这些新的表达式是否真的能更好地预测观察到的行为。一些数值拟合将基于现有数据，但需要大量额外数据进行更彻底的测试。PI将继续在理论上发展该地区。特别是，他将把相当多的注意力集中在加权函数的形式上，其性质远不如效用函数的性质明确。