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选择规则与累加器网络

Choice Rules and Accumulator Networks.

作者信息

Bhatia Sudeep

机构信息

Behavioral Science Group, Warwick Business School, University of Warwick.

出版信息

Decision (Wash D C ). 2017 Jul;4(3):146-170. doi: 10.1037/dec0000038. Epub 2015 Jul 27.

Abstract

This article presents a preference accumulation model that can be used to implement a number of different multi-attribute heuristic choice rules, including the lexicographic rule, the majority of confirming dimensions (tallying) rule and the equal weights rule. The proposed model differs from existing accumulators in terms of attribute representation: Leakage and competition, typically applied only to preference accumulation, are also assumed to be involved in processing attribute values. This allows the model to perform a range of sophisticated attribute-wise comparisons, including comparisons that compute relative rank. The ability of a preference accumulation model composed of leaky competitive networks to mimic symbolic models of heuristic choice suggests that these 2 approaches are not incompatible, and that a unitary cognitive model of preferential choice, based on insights from both these approaches, may be feasible.

摘要

本文提出了一种偏好积累模型,该模型可用于实现多种不同的多属性启发式选择规则,包括字典序规则、多数确认维度(计分)规则和平等权重规则。所提出的模型在属性表示方面与现有累加器不同:通常仅应用于偏好积累的泄漏和竞争,也被假定参与处理属性值。这使得该模型能够执行一系列复杂的按属性比较,包括计算相对排名的比较。由泄漏竞争网络组成的偏好积累模型模仿启发式选择符号模型的能力表明,这两种方法并非不相容,并且基于这两种方法的见解构建一个统一的偏好选择认知模型可能是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0058/5484390/f7c39c38855f/dec_4_3_146_fig1a.jpg

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