Theory of Adaptive Decision Making

适应性决策理论

• 批准号: 8710103
• 负责人: Jerome Busemeyer
• 金额: $ 6.78万
• 依托单位: Purdue Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-01 至 1990-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8710103&HistoricalAwards=false
• 关键词: Theory; Adaptive; Decision; Making

The purpose of this research is to investigate how individuals learn to improve their decision-making skills on the basis of experience resulting from consequences of past decisions. The research was designed to evaluate empirically a mathematical learning theory that can be summarized by three principles: The first principle describes how each situation is categorized for the purpose of identifying a set of appropriate decision-making rules; the second principle describes how one particular rule is selected from a set of competing rules on the basis of average past performance; and the third principle describes how the performance of each decision rule is slowly improved by a fine tuning process guided by feedback from previous consequences. The first two experiments will test the hypothesis that a simple "hill-climbing" mechanism is used to fine tune and improve the performance of a given rule. Experiment 1 will investigate how diagnostic criteria are learned in a medical diagnosis task, and Experiment 2 will investigate how individuals learn to improve resource allocations to maximize profits. The next two experiments will test the hypothesis that rules are selected on the basis of performance estimates produced by a recency-weighted averaging mechanism. Experiment 3 will investigate how individuals learn to choose among fictitious medical treatments that differ according to efficacy, and Experiment 4 will investigate how individuals learn to choose among diagnostic rules for categorizing fictitious medical patients. The last two experiments will test the hypothesis that decision situations are categorized prior to rule selection, and rule performance is assessed separately for each category. Experiment 5 will test the hypothesis that untrained decision makers rely on concrete surface features, whereas trained desision makers rely on abstract structural principles for categorizing decision situations. Experiment 6 will investigate how individuals learn to discriminate rule performance on the basis of envirionmental cues. Overall, this research program will improve our understanding about the evolution of decision strategies, the development of both optimal and non-optimal strategies, and the process that novice decision makers use to become experts through practice.

这项研究的目的是调查个人如何 学会在以下基础上提高自己的决策能力 由过去的决定所产生的后果。 的 一项研究旨在从经验上评估一个数学模型， 学习理论可以概括为三个原则： 第一原则描述了每种情况如何分类， 确定一套适当决策的目的 规则;第二个原则描述了一个特定的规则是如何 根据平均值从一组竞争规则中选出 过去的表现;第三个原则描述了如何 每个决策规则的性能通过罚款慢慢提高 由先前结果的反馈引导的调整过程。 前两个实验将测试一个简单的假设， “爬山”机制是用来微调和改善 执行一个给定的规则。 实验1将研究如何 在医学诊断任务中学习诊断标准，并且 实验2将研究个体如何学习提高 资源配置以实现利润最大化。 未来两 实验将测试规则被选择的假设， 最近加权的业绩估计的基础 平均机制 实验3将研究如何 人们学会在虚构的医疗方案中做出选择 根据功效不同，实验4将 研究个体如何学会在诊断中做出选择 对虚构的医疗病人进行分类的规则。 最后两 实验将检验这一假设，即决策情况是 在规则选择之前进行分类，并且规则性能 对每个类别分别进行评估。 实验5将测试 未经训练的决策者依赖于具体的假设 表面特征，而训练有素的决策者依赖于 分类决策的抽象结构原则 situations. 实验6将研究个体如何学习 根据环境因素区分规则执行情况 线索 总的来说，这项研究计划将提高我们的 了解决策策略的演变， 最佳和非最佳战略的发展，以及 新手决策者成为专家的过程， 实践