论文标题
挑战理论：风险二元选择行为的结构和测量
Challenge Theory: The Structure and Measurement of Risky Binary Choice Behavior
论文作者
论文摘要
挑战理论（Shye＆Haber 2015; 2020）表明，归因于每个二元选择问题的新设计的挑战指数（CI）预示了大胆期权的普及，这是获得更高货币结果的较低概率（在收益问题中）；以及较高的货币结果（在损失问题中）的可能性较高。在本文中，我们展示了方面理论如何结构选择行为概念空间并产生赌博行为的合理测量。这项研究的数据包括从126名学生那里获得的回答，并在44个风险决策问题中指定了他们的偏好。 44个问题的最小空间分析（SSA）证实了以下假设：二元风险选择问题的空间可以通过两个二元轴向方面分配：（a）问题类型（增益与损失）； （b）CI（低与高）。代表经过验证的构建体的四个复合变量：增益，损失，高CI和低CI通过通过部分阶尺度分析（POSAC）（POSAC）进行多个尺度处理，从而导致对两个必要且充分的赌博行为测量量表进行有意义且直觉上的解释。
Challenge Theory (Shye & Haber 2015; 2020) has demonstrated that a newly devised challenge index (CI) attributable to every binary choice problem predicts the popularity of the bold option, the one of lower probability to gain a higher monetary outcome (in a gain problem); and the one of higher probability to lose a lower monetary outcome (in a loss problem). In this paper we show how Facet Theory structures the choice-behavior concept-space and yields rationalized measurements of gambling behavior. The data of this study consist of responses obtained from 126 student, specifying their preferences in 44 risky decision problems. A Faceted Smallest Space Analysis (SSA) of the 44 problems confirmed the hypothesis that the space of binary risky choice problems is partitionable by two binary axial facets: (a) Type of Problem (gain vs. loss); and (b) CI (Low vs. High). Four composite variables, representing the validated constructs: Gain, Loss, High-CI and Low-CI, were processed using Multiple Scaling by Partial Order Scalogram Analysis with base Coordinates (POSAC), leading to a meaningful and intuitively appealing interpretation of two necessary and sufficient gambling-behavior measurement scales.
