Machine Learning for Collective Behavior

集体行为的机器学习

• 批准号: 0742171
• 负责人: Michael Kearns
• 金额: $ 19.72万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742171&HistoricalAwards=false
• 关键词: Machine; Learning; Collective; Behavior

This research will develop and apply new machine learning methods to model individual behavior in the context of collective problem-solving. The experimental task in earlier empirical research with human subjects was to solve challenging distributed computational problems on networks, such as graph coloring, from only local information. The goal of the new work is to develop machine learning methods whose outputs can accurately reconstruct and predict collective behavior from individual models, and can shed light on related questions such as the empirical diversity of strategies within a human population, and its importance for effective collective behavior.This project contributes in novel ways to several distinct research communities, including machine learning, sociology, economics, and related fields. It integrates research and education in two ways: by giving both graduate students and undergraduates the experience of participating in a cutting-edge research study, and by providing new curriculum for a course entitled "Networked Life."

这项研究将开发并应用新的机器学习方法，以在集体解决问题的背景下对个人行为进行建模。早期对人类受试者的实证研究的实验任务是从仅本地信息中解决网络上有挑战的分布计算问题，例如图形着色。新工作的目的是开发机器学习方法，其输出可以准确地重建和预测各个模型的集体行为，并可以阐明相关问题，例如人口中策略的经验多样性，以及其对有效集体的重要性行为。该项目以新颖的方式为包括机器学习，社会学，经济学和相关领域在内的几个不同的研究社区做出贡献。它通过两种方式整合了研究和教育：通过为研究生和本科生提供参与尖端研究的经验，并通过为题为“网络生活”的课程提供新课程。