Take The Best, Dawes' Rule, and Compensatory Decision Strategies: A Regression-based Classification Method
Take The Best, Dawes' Rule, and Compensatory Decision Strategies: A Regression-based Classification Method
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采取最佳、道斯规则和补偿决策策略：基于回归的分类方法
DOI：
10.1023/a:1016080517126
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发表时间：
2002
影响因子：
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通讯作者：
A. Bröder
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文献类型：
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作者：
A. Bröder
“取最佳”启发式（G. Gigerenzer et al., 1991）、加权相加规则和等权决策规则等策略描述是关于多属性决策任务中信息集成的相互竞争的理论。行为决策研究面临着从行为数据中得出不可观察决策策略结论的问题。尽管由于“结构建模”和“过程跟踪”等方法传统，已经取得了相当大的进展，但这些范式在测试关于个体策略的特定假设方面存在一定的局限性。本文对其中的一些问题作了简要的总结。介绍了一种对个体反应模式进行分类的演绎法。关于回归系数的预测是从关于决策策略的相互竞争的实质性假设中推导出来的。这些可以在个体参与者的层面上进行测试。通过蒙特卡罗仿真验证了该分类方法的有效性。描述了该方法的一些有用的应用，讨论了该方法的局限性和可能的推广。
Strategy descriptions like the ``Take The Best''-heuristic (G. Gigerenzer et al., 1991), the weighted additive rule, and the equal weight decision rule are competing theories about information integration in multi-attribute decision tasks. Behavioral decision research is confronted with the problem of drawing conclusions about unobservable decision strategies from behavioral data. Although there has been considerable progress due to methodical traditions like `Structural Modeling' and `Process Tracing', these paradigms have certain limitations in testing specific hypotheses about individual strategies. Some of these problems are summarized briefly. A deductive method for classifying individual response patterns is introduced. Predictions about regression coefficients are deduced from competing substantial hypotheses about strategies for decision making. These can be tested at the level of individual participants. The validity of this classification procedure is demonstrated with a Monte Carlo simulation. Some useful applications of the method are described, limitations of the method and potential generalizations are discussed.
灵长类动物。
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