群众的智慧与部分排名:贝叶斯的方法在JAGS中实施了瑟斯通模型
Lauren E Montgomery1, Nora Bradford1, Michael D Lee2
1Department of Cognitive Sciences, University of California Irvine, Irvine, CA, 92697-5100, USA.
Behavior research methods
|July 30, 2024
概括
我们创建了一个贝叶斯方法,使用瑟斯通模型汇总部分排名. 这种方法从不完整的数据中推断出真正的排名和个人专业知识,适用于各种数据集.
科学领域:
- 统计 统计 统计 统计
- 计算社会科学 计算社会科学
- 心理测量 心理测量 心理测量
背景情况:
- 聚合部分排名数据是一项挑战.
- 现有的方法可能无法有效处理不完整或多样化的子集排名.
- 从部分数据中了解真正的项目顺序和个人专业知识至关重要.
研究的目的:
- 开发一种灵活的贝叶斯方法来聚合部分排名数据.
- 从不完整的排名中推断潜在的真实排名和个人专业知识.
- 为了证明该方法在不同的部分排名数据收集方案中的有效性.
主要方法:
- 使用瑟斯通模型开发了贝叶斯方法.
- 在JAGS (Just Another Gibbs Sampler) 中实现该模型作为图形模型.
- 设计实验以收集各种形式的部分排名数据.
主要成果:
- 贝叶斯方法有效地从实验中聚合了部分排名数据.
- 该方法准确地推断出潜在的真实排名和相对的个人专业知识.
- 已成功应用于美国城市人口排名和美国总统时间表.
结论:
- 开发的贝叶斯方法对于聚合多样化的部分排名数据是有效的.
- 这种方法提供了对"群众智慧"现象的洞察力.
- 这种方法对于涉及不完全或部分排名聚合的研究具有广泛的适用性.
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