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Career: Learning for Strategic Interactions in Societal-Scale Cyber-Physical Systems

职业：学习社会规模网络物理系统中的战略交互

基本信息

批准号：
2240110
负责人：
Eric Mazumdar
金额：
$ 55万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2028-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240110&HistoricalAwards=false
关键词：
Career Learning Strategic Interactions Societal

项目摘要

In societal-scale cyber-physical systems (SCPS), machine learning algorithms are increasingly becoming the interface between stakeholders---from matching drivers and riders on ride-sharing platforms to the real-time scheduling of energy resources in electric vehicle (EV) charging stations. The fact that the different stakeholders in these systems have different objectives gives rise to strategic interactions which can result in inefficiencies and negative externalities across the SCPS. This NSF CAREER project seeks to develop a foundational understanding of the strategic interactions that arise in SCPS, the impacts they have on social welfare, and how they affect algorithmic decision-making. The goal is to shift how engineers design algorithms for SCPS. Currently, learning algorithms are trained and developed in isolation, and uncertainty and strategic interactions are treated---if at all--- as adversarial or worst-case. In contrast, the proposed research aims to develop algorithms that can consider economic interactions, human behavior, and uncertainty when making decisions. The theory and algorithms developed through this project will be validated on two physical testbeds: 1. an EV charging testbed where drivers routinely mis-report preferences for faster charging, and 2. the Caltech Social Science Experimental Laboratory where controlled experiments will be conducted to understand how people respond to algorithms. The proposal also includes an integrated education and outreach plan, which includes outreach to K-12 students and new undergraduate and graduate courses on the complexities of learning in SCPS.Key goals of this project include developing a unified design methodology for learning in the presence of strategic behaviors in SCPS and the systematic study of the control actions and control authority that individual users and policymakers can wield to achieve societal goals. The fact that strategic manipulations in SCPS are played out through the (mis)-reporting of data or through algorithmic decision-making distinguishes these problems from those classically studied in game theory and economics. Furthermore, in contrast with existing work in computer science and economics that study strategic interactions, this project aims to take a dynamic view of SCPS, which leverages tools and ideas from dynamical systems theory and stochastic processes to complement ideas in machine learning, game theory, and behavioral economics. This perspective will allow for new insights into how repeated interactions affect strategic decision-making in SCPS and which design decisions impact learning in game theoretic settings. This opens the door to new insights and the analysis of previously overlooked control knobs for achieving societal goals in SCPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在社会规模的网络物理系统（SCPS）中，机器学习算法正日益成为利益相关者之间的接口--从在乘车共享平台上匹配司机和乘客到电动汽车（EV）充电站中能源资源的实时调度。这些系统中的不同利益相关者有不同的目标，这一事实引起了战略互动，可能导致整个SCPS的效率低下和负外部性。这个NSF CAREER项目旨在对SCPS中出现的战略互动，它们对社会福利的影响以及它们如何影响算法决策进行基础性理解。目标是改变工程师设计SCPS算法的方式。目前，学习算法是孤立地训练和开发的，不确定性和战略互动被视为对抗性或最坏情况。相比之下，拟议中的研究旨在开发能够在决策时考虑经济互动、人类行为和不确定性的算法。通过本项目开发的理论和算法将在两个物理测试平台上进行验证：1。电动汽车充电测试平台，其中驾驶员经常误报更快充电的偏好，以及2.加州理工学院社会科学实验室将进行受控实验，以了解人们对算法的反应。该提案还包括一项综合教育和外联计划，其中包括对K-12名学生和新的本科生和研究生课程的学习的复杂性在SCPS。该项目的主要目标包括开发一个统一的设计方法学的学习存在的战略行为在SCPS和系统的研究控制行动和控制权力，个人用户和决策者可以行使，以实现社会目标。SCPS中的战略操纵是通过（错误）报告数据或通过算法决策来实现的，这一事实将这些问题与博弈论和经济学中经典研究的问题区分开来。此外，与计算机科学和经济学中研究战略相互作用的现有工作相比，该项目旨在对SCPS进行动态观察，利用动态系统理论和随机过程的工具和思想来补充机器学习，博弈论和行为经济学中的思想。这种观点将允许新的见解如何重复的互动影响战略决策SCPS和设计决策的影响，在博弈论的设置学习。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。