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

• 批准号: 1847393
• 负责人: Siddhartha Banerjee
• 金额: $ 50.05万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847393&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Prediction; Engines; Non

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.

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各个方面。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）项目的目的是开发一个统一的框架，用于智能系统的实时决策，围绕使用数据驱动的预测预言和非货币市场机制。这种方法会产生简单、易于解释和在实践中实施的政策;然而，了解其绩效需要新的理论和方法论思想。该研究将通过与按需运输，云计算，在线支付处理和当地粮食银行的合作伙伴进行合作来补充，这将为教学目的提供一系列例子。这些合作与外展计划相结合，该计划的中心是开发一个社会系统模拟器的公共图书馆。这些将为本科生提供研究项目，课程的经验问题，以及吸引K-12学生到STEM领域的公开演示。从技术角度来看，这项研究将制定严格的框架：（i）利用预测预言作为实时控制政策的输入，以及（ii）基于模拟货币机制设计非货币分配政策。该范例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。这种方法利用了历史和实时数据，结合了基础系统的独特约束，并产生了简单实用的政策。这种简单性的代价是更难证明严格的保证。为了克服这一点，该研究将把机器学习和机制设计理论的进步与模型预测控制的基本原理结合起来。特别是，新的理论技术，使用随机耦合，凸优化，鞅对偶，和测量浓度的想法，将被开发。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Real-time Approximate Routing for Smart Transit Systems

智能交通系统的实时近似路线

• DOI: 10.1145/3460091
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Périvier, Noémie;Hssaine, Chamsi;Samaranayake, Samitha;Banerjee, Siddhartha
• 通讯作者: Banerjee, Siddhartha

From Monetary to Nonmonetary Mechanism Design via Artificial Currencies

通过人工货币从货币机制设计到非货币机制设计

• DOI: 10.1287/moor.2020.1098
• 发表时间: 2021
• 期刊: Mathematics of Operations Research
• 影响因子: 1.7
• 作者: Gorokh, Artur;Banerjee, Siddhartha;Iyer, Krishnamurthy
• 通讯作者: Iyer, Krishnamurthy

Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Trade-off Curve

• DOI: 10.1287/opre.2022.2397
• 发表时间: 2023-11-23
• 期刊: OPERATIONS RESEARCH
• 影响因子: 2.7
• 作者: Sinclair，Sean R.;Jain，Gauri;Yu，Christina Lee
• 通讯作者: Yu，Christina Lee

ORSuite: Benchmarking Suite for Sequential Operations Models

ORSuite：顺序操作模型的基准测试套件

• DOI: 10.1145/3512798.3512819
• 发表时间: 2022
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Archer, Christopher;Banerjee, Siddhartha;Cortez, Mayleen;Rucker, Carrie;Sinclair, Sean R.;Solberg, Max;Xie, Qiaomin;Lee Yu, Christina
• 通讯作者: Lee Yu, Christina

Online Allocation and Pricing: Constant Regret via Bellman Inequalities

在线分配和定价：贝尔曼不等式带来的持续遗憾

• DOI: 10.1287/opre.2020.2061
• 发表时间: 2021
• 期刊: Operations Research
• 影响因子: 2.7
• 作者: Vera, Alberto;Banerjee, Siddhartha;Gurvich, Itai
• 通讯作者: Gurvich, Itai