CAREER: Optimal Design, Policy and Risk Management of Central Nodes in Stochastic Networks

职业：随机网络中央节点的优化设计、策略和风险管理

基本信息

批准号：
1653354
负责人：
Andreea Minca
金额：
$ 50万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653354&HistoricalAwards=false
关键词：
CAREER Optimal Design Policy Risk

项目摘要

The goal of this Faculty Early Career Development (CAREER) award is to develop a comprehensive framework for the design of and optimal resource allocation in star-shaped stochastic networks with a central control node. The central node serves to manage system risk by pooling a portion of the resources of each individual node and underwriting a node that falls below a threshold value. This project will advance methodologies in both operations research and stochastic analysis. Such systems arise in many contexts, including distributed retail operations, where resources are inventories of goods and backordering is allowed, and financial networks, where resources are cash and other capital assets and individual nodes may experience cash shortfalls. The education plan will broaden participation of females in STEM fields through a structured week-long outreach activity with female high school students and by organizing special sessions for junior female researchers to interact with senior female executives in the risk management and financial industry at an annual national conference. Resources of the central node and individual nodes are modeled as multidimensional marked point processes, and first passage times are characterized and studied. Optimal contributions from each node to the pooled central node are determined by considering individual nodes' stochastic behavior in a cooperative game-theoretic setting, and the Nash equilibrium of these games will be studied. The case where the central node has an independent Brownian component that can enhance system operation will be also be considered. The developed models will be calibrated with available data.

这个教师早期职业发展（职业）奖的目标是为具有中央控制节点的星形随机网络设计和最佳资源分配的全面框架。中央节点通过汇总每个单个节点的一部分资源并承保一个低于阈值的节点来管理系统风险。该项目将在运营研究和随机分析中推进方法论。这种系统在许多情况下出现，包括分布式零售业务，允许资源是商品和退货的库存，资源是现金和其他资本资产的财务网络，并且各个节点可能会遇到现金不足。该教育计划将通过与女性高中生进行为期一周的结构化外展活动，并通过为初级女性研究人员组织特殊会议，以在年度全国会议上与风险管理和金融行业的高级女高管进行互动，从而扩大女性参与STEM领域的参与。中央节点和各个节点的资源被建模为多维标记点过程，并对第一个段落时间进行了表征和研究。从每个节点到汇总的中央节点的最佳贡献是通过在合作游戏理论环境中考虑各个节点的随机行为来确定的，并且将研究这些游戏的NASH平衡。还将考虑中央节点具有独立的布朗组件可以增强系统操作的情况。开发的模型将通过可用数据校准。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Decentralized Governance of Stablecoins with Closed Form Valuation

封闭式估值的稳定币去中心化治理

DOI：
发表时间：
2023
期刊：
Lecture notes in operations research
影响因子：
0
作者：
Huo, Lucy;Klages-Mundt, Ariah;Minca, Andreea;Münter, Frederik Christian;Wind, Mads Rude
通讯作者：
Wind, Mads Rude

Optimal intervention in economic networks using influence maximization methods

使用影响力最大化方法对经济网络进行最佳干预