CIF: Small: Statistical Signal Processing of Social Networks with Behavioral Economics Constraints

CIF：小：具有行为经济学约束的社交网络的统计信号处理

• 批准号: 2112457
• 负责人: Vikram Krishnamurthy
• 金额: $ 49.21万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112457&HistoricalAwards=false
• 关键词: CIF; Small; Statistical; Signal; Processing

Social networks are ubiquitous. There is strong motivation to understand important sociological phenomena in social networks by constructing novel engineering models, algorithms and analysis. This project contributes to fundamental research in understanding the interaction between statistical inference, human decision making and information flow in social networks. The research addresses three interrelated themes. The first theme develops novel algorithms for detecting a change in the strategies of human decision makers that share information over a social network, for example, identifying reactive behavior to viral content on Twitter. The second theme models and analyzes how humans interacting over a social network can result in sociological phenomena such as the glass-ceiling effect and segregation. The third theme studies efficient polling algorithms in large-scale social networks where only a fraction of nodes can be polled to determine their decisions. For example, which nodes should be polled to achieve a statistically accurate estimate of sociological phenomena such as the emergence of the glass-ceiling effect? The theoretical claims and findings in this project will be validated via extensive analysis of real-world social network datasets.This project develops novel engineering models, algorithms and analysis to understand the interaction between statistical signal processing, behavioral economics (human decision making) and network science (information flow in social networks). The project conducts fundamental research in three interrelated themes. The first theme studies multi-agent information fusion and change detection with behavioral economics constraints (anticipatory decision making and rational inattention); the goal is to understand how information fusion is achieved among human decision makers. The second theme investigates how sophisticated agents interacting over a social network give rise to the important sociological phenomena of the glass-ceiling effect and segregation. The glass-ceiling effect refers to the barrier that keeps certain groups from rising to influential positions in society, regardless of their qualifications. In a social-network context, the investigator explores how individual traits like preferential attachment and homophily leads to the glass-ceiling effect. The third theme studies the design of statistically efficient network polling algorithms. In large-scale social networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimate of sociological phenomena such as the glass-ceiling effect? Some nodes may be reluctant to reveal their true opinion. This may lead to incorrect polling estimates. How can this be compensated for? The theoretical claims and findings in this project will be validated via extensive analysis of real-world social-network datasets.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.

社交网络无处不在。通过构建新颖的工程模型、算法和分析来理解社交网络中的重要社会学现象有着强烈的动机。 该项目有助于理解社交网络中统计推断、人类决策和信息流之间的相互作用的基础研究。研究涉及三个相互关联的主题。第一个主题开发了新的算法，用于检测通过社交网络共享信息的人类决策者的策略变化，例如，识别Twitter上病毒内容的反应行为。 第二个主题建模并分析了人类在社交网络上的互动如何导致玻璃天花板效应和隔离等社会学现象。 第三个主题是研究大规模社交网络中的高效轮询算法，其中只有一小部分节点可以被轮询以确定它们的决策。例如，应该对哪些节点进行调查，以实现对玻璃天花板效应等社会学现象的统计准确估计？ 本项目的理论主张和发现将通过对真实世界社交网络数据集的广泛分析来验证。本项目开发新的工程模型、算法和分析，以了解统计信号处理、行为经济学（人类决策）和网络科学（社交网络中的信息流）之间的相互作用。该项目在三个相互关联的主题进行基础研究。 第一个主题研究多智能体信息融合和变化检测与行为经济学约束（预期决策和理性疏忽），目标是了解信息融合是如何实现人类决策者。第二个主题探讨了复杂的代理人如何在社会网络上互动，产生了重要的社会学现象的玻璃天花板效应和隔离。玻璃天花板效应是指阻碍某些群体上升到社会有影响力的职位的障碍，无论他们的资格如何。在社交网络的背景下，研究人员探讨了偏好依恋和同质性等个人特征如何导致玻璃天花板效应。 第三个主题是研究统计上有效的网络轮询算法的设计。在大规模的社交网络中，只有一小部分节点可以被轮询以确定它们的决策。应该对哪些节点进行民意调查，以实现对玻璃天花板效应等社会学现象的统计准确估计？一些节点可能不愿意透露他们的真实意见。这可能导致不正确的投票估计。这怎么补偿？ 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。

项目成果

Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach

基于李雅普诺夫的人机交互随机稳定性：一种量子决策系统方法

• DOI: 10.1109/cdc51059.2022.9992472
• 发表时间: 2023
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC
• 作者: Snow, Luke;Jain, Shashwat;Krishnamurthy, Vikram
• 通讯作者: Krishnamurthy, Vikram

Adaptive Filtering Algorithms For Set-Valued Observations-Symmetric Measurement Approach To Unlabeled And Anonymized Data

集值观测的自适应过滤算法-未标记和匿名数据的对称测量方法

• DOI: 10.1109/icassp49357.2023.10094833
• 发表时间: 2023
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Krishnamurthy, Vikram

Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation

• DOI: 10.1109/tcss.2021.3091168
• 发表时间: 2020-12
• 期刊: IEEE Transactions on Computational Social Systems
• 影响因子: 5
• 作者: Rui Luo;Buddhika Nettasinghe;V. Krishnamurthy

Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner

• DOI: 10.1109/cdc51059.2022.9992959
• 发表时间: 2022-05
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: Kunal Pattanayak;V. Krishnamurthy;C. Berry

Quickest Change Detection using Time Inconsistent Anticipatory and Quantum Decision Modeling

使用时间不一致的预期和量子决策模型进行最快的变化检测

• DOI: 10.1109/allerton49937.2022.9929427
• 发表时间: 2022
• 期刊: and Computing
• 作者: Krishnamurthy, Vikram;Snow, Luke
• 通讯作者: Snow, Luke