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CSUMS: Information and Decision Algorithm Laboratories (IDeA Labs)

CSUMS：信息与决策算法实验室（IDeA Labs）

基本信息

批准号：
0639328
负责人：
Jeffrey Humpherys
金额：
$ 70.48万
依托单位：
Brigham Young University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-15 至 2012-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0639328&HistoricalAwards=false
关键词：
CSUMS Information Decision Algorithm Laboratories

项目摘要

This project furthers the development of a network of interdisciplinary student-focused research laboratories that explore algorithmic decision processes from a common mathematical framework of information, dynamics, and control. These "Information and Decision Algorithm Laboratories", or "IDeA Labs", form a research model designed to demonstrate the power of mathematics to students by allowing them to abstract fundamental concepts such as approximation, optimization, and control from a diverse set of application domains. Each of these laboratories house research projects, invite collaborations, facilitate interaction with other research groups, and encourage partnerships with industry. Nevertheless, all the projects are focused abstractly on algorithmic decision processes and introduce students to the common mathematical themes underlying these problem areas.Algorithmic decision processes are ubiquitous, arising everywhere from the strategies deployed by corporations to investment decisions made by fund managers, and from regulatory signals deployed in metabolic networks to policy decisions made by governments. The purpose of this work is to cut through the jargon of particular disciplines and reveal to students the common mathematical and computational structures behind algorithmic decision processes through bona fide research experiences in a variety of disciplines.

该项目进一步发展了一个以学生为中心的跨学科研究实验室网络，从信息，动力学和控制的共同数学框架中探索算法决策过程。这些“信息和决策算法实验室”或“IDEA实验室”形成了一个研究模型，旨在通过允许学生从不同的应用领域中抽象出近似，优化和控制等基本概念来向学生展示数学的力量。这些实验室中的每一个都有研究项目，邀请合作，促进与其他研究小组的互动，并鼓励与工业界建立伙伴关系。尽管如此，所有的项目都抽象地集中在算法决策过程上，并向学生介绍这些问题领域的共同数学主题。算法决策过程无处不在，从公司部署的战略到基金经理做出的投资决策，从代谢网络中部署的监管信号到政府做出的政策决策。这项工作的目的是切断特定学科的行话，并通过各种学科的真实研究经验，向学生揭示算法决策过程背后的常见数学和计算结构。