A New Stochastic, Context Sensitive, and Intransitive Choice Model

心理学研究表明，人们的偏好并不是一成不变的，决策者往往会偏离所谓“理性”模型的预测。然而，大多数决策理论要么做出确定性预测来解决偏离理性原则的问题，要么用随机模型来处理行为的可变性，而随机模型无法解释这种违反。本文研究了一种新的随机决策模型——比例差异模型（PD）。PD是一个简单的双参数模型，它基于决策者以比例方式交易属性值的概念，并且这种交易是一个可变的过程。将进行一些实验来检验PD模型的预测。第一项研究将通过比较PD与文献中发现的其他三种模型的描述能力来测试PD；初步测试表明PD是一个更好的模型。第二项研究将重点关注PD对情境效应的预测，如选择环境中最大属性值的影响和已知的反射效应。第三个研究将调查模糊信息下的决策。本研究的结果将推动描述性决策的知识状态。

Psychological research has demonstrated that people's preferences are not constant and that decision makers often depart from predictions made by so called `rational` models. Most decision making theories, however, either make deterministic predictions to address departures from rational principles, or handle variability of behavior with stochastic models that cannot account for such violations. This research examines a new, stochastic model of decision making, the proportional difference model (PD). PD is a simple, two-parameter model, based on the notion that decision makers trade attribute values in a proportional manner and that this trading is a variable process. A number of experiments will be conducted to examine the predictions of the PD model. The first study will test PD by comparing its descriptive ability to that of three other models found in the literature; preliminary tests suggest PD is a better model. The second study will focus on PD's predictions of context effects, such as effects of maximum attribute values in the choice environment and the known reflection effect. The third study will investigate decision making with vague information. The results of this research will advance the state of knowledge of descriptive decision making.