A dynamic, stochastic, computational model of preference reversal phenomena
A dynamic, stochastic, computational model of preference reversal phenomena
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DOI：
10.1037/0033-295x.112.4.841
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发表时间：
2005-10-01
影响因子：
5.4
通讯作者：
Busemeyer, JR
中科院分区：
文献类型：
--
作者：
Johnson, JG;Busemeyer, JR
一组选项之间的偏好排序可能取决于引出方法（例如，选择或定价）；这些偏好逆转挑战了传统的决策理论。先前解释这些逆转的尝试依赖于允许通过改变决策权重、属性值或这些信息的组合来改变启发方法中选项的效用——然而，没有一个理论成功地解释了所有的现象。在本文中，作者提出了一个新的计算模型，该模型在不改变决策权重、值或组合规则的情况下解释了经验趋势。相反，当前的模型指定了一个动态的评估和响应过程，它正确地预测了6种激发方法之间的偏好顺序，保持了不同方法之间的稳定评估，并对响应分布和响应时间做出了新的预测。
Preference orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across elicitation methods by changing the decision weights, the attribute values, or the combination of this information-still, no theory has successfully accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response distributions and response times.