CAREER: Harnessing Prediction Engines and Non-Monetary Mechanisms for Real-Time Decision Making

职业：利用预测引擎和非货币机制进行实时决策智能社会系统——按需运输、智能供应链和物流网络、云平台、智能电网、金融处理网络等——正在彻底改变我们经济和社会生活的方方面面。由于不确定性、复杂的状态空间、组合约束和战略代理行为，所有这些系统都面临着类似的决策挑战。这个教师早期职业发展计划（Career）项目的目的是围绕使用数据驱动的预测预言和非货币市场机制，为智能系统的实时决策开发一个统一的框架。这种做法导致政策简单、易于解释和在实践中执行；然而，理解它们的表现需要新的理论和方法思想。这项研究将通过与按需运输、云计算、在线支付处理和当地食品银行等合作伙伴的合作来补充，这将为教学目的提供一系列例子。这些合作与外展计划密切相关，该计划以发展社会系统模拟器公共图书馆为中心。这些项目将为本科生提供研究项目，为课程提供体验问题，并为吸引K-12学生进入STEM领域提供公开演示。从技术角度来看，本研究将开发严格的框架：(i)利用预测预言作为实时控制政策的输入，以及（ii）在模拟货币机制的基础上设计非货币分配政策。该范式的一个范例是模拟即服务（SaaS）的思想，其中复杂的数据驱动模拟器将用作控制策略和机制的输入。这种方法利用历史和实时数据，结合底层系统的独特约束，并产生简单实用的策略。这种简单的代价是很难证明严格的保证。为了克服这一点，研究将把机器学习和机制设计理论的进展与模型预测控制的基本哲学结合起来。特别是，新的理论技术，从随机耦合，凸优化，鞅对偶和测量集中的思想，将得到发展。该奖项反映了美国国家科学基金会的法定使命，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。

CAREER: Harnessing Prediction Engines and Non-Monetary Mechanisms for Real-Time Decision MakingSmart societal systems - on-demand transportation, smart supply-chain and logistics networks, cloud platforms, the smart grid, financial-processing networks, etc - are revolutionizing all aspects of our economic and social lives. All these systems face similar decision-making challenges due to uncertainty, complex state-spaces, combinatorial constraints, and strategic agent behavior. The aim of this Faculty Early Career Development Program (CAREER) project is to develop a unified framework for real-time decision-making for smart systems, built around the use of data-driven prediction oracles and non-monetary market mechanisms. Such an approach leads to policies that are simple, easy to interpret and implement in practice; understanding their performance however requires new theoretical and methodological ideas. The research will be complemented by collaborations with partners in on-demand transportation, cloud computing, online payment processing and the local food-bank, which will provide a portfolio of examples for pedagogical purposes. These collaborations tie in with outreach plans, which center on the development of a public library of societal systems simulators. These will provide research projects for undergraduate students, experiential problems for courses, and public demonstrations for attracting K-12 students to STEM fields. From a technical perspective, this research will develop rigorous frameworks for: (i) harnessing prediction oracles as inputs to real-time control policies, and (ii) designing non-monetary allocation policies based on emulating monetary mechanism. An exemplar of the paradigm is the idea of simulation-as-a-service (SaaS), wherein complex data-driven simulators will be used as inputs for control policies and mechanisms. Such an approach leverages historical and real-time data, incorporates the unique constraints of the underlying system, and results in simple and practical policies. The cost of this simplicity is that it is harder to prove rigorous guarantees. To overcome this, the research will couple advances in machine learning and mechanism design theory with the underlying philosophy of model-predictive control. In particular, new theoretical techniques, using ideas from stochastic coupling, convex optimization, martingale duality, and measure concentration, will be developed.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.