RI:Small:Collaborative Research:Influence Games: A Game-theoretic Approach to Strategic Behavior in Networks

RI：小：协作研究：影响力博弈：网络中战略行为的博弈论方法

• 批准号: 1907553
• 负责人: Luis Ortiz
• 金额: $ 22.89万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907553&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Influence

The world is becoming increasingly interconnected. While not all connections have the same level of importance or even the same meaning, these connections nevertheless play a crucial role in one's behavioral and lifestyle choices. This is particularly striking in strategic scenarios where individual choices are interdependent on each other. This research seeks to model how networked individuals influence each other in their decision making and what collective outcomes may arise from such a system of influence. It seeks to advance our scientific knowledge of strategic behavior in networks by making the models more realistic, allowing for changes over time, and considering the underlying context. These advances are important in part because of their potential impact in a wide range of domains including public health policy, smart power grid, and financial systems. In addition, the project will contribute to the educational enrichment of undergraduate students, including underrepresented and first-generation students. It will bring together two distinct groups of students, namely undergraduate liberal arts students and graduate computer science students, under a symbiotic collaboration plan. The research results will be broadly disseminated through a website and will also be integrated into undergraduate- and graduate-level education.This project investigates several important open directions in the computational game-theoretic study of influence in networks. It will address a variety of fundamental research problems, including the challenge of identifying "most influential" individuals in a network. In particular, the research has three major parts: (1) The challenge of complexity: design game-theoretic models of influence in networks to allow (a) flexibility in behavioral choices (from multiple, non-binary discrete choices to a continuum of behavioral choices) and (b) non-linear influences without any restriction on polarities (positive/negative). (2) The reality of dynamics: model dynamic evolution of influence networks. (3) The power of context: model the contextual environment of strategic behavior. In these three thrusts, the project significantly departs from the well-studied approaches to influence maximization as well as the traditional centrality measures in social network analysis. It seeks to design network-aware algorithms, including provable approximation algorithms and practical heuristics, for computing stable outcomes and identifying most influential individuals in a network relative to a desirable outcome. Ultimately, the research seeks to provide computational tools for policy analysts to perform minimal targeted interventions in a social network for achieving a desirable social outcome. To that end, the project will use real-world behavioral data. It will employ, adapt, or extend existing machine learning algorithms to learn context-aware models without imposing any restriction on the structure of the networks.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.

世界变得越来越互连。尽管并非所有连接都具有相同的重要性甚至相同的含义，但这些连接在人的行为和生活方式选择中仍然起着至关重要的作用。在彼此相互依存的战略场景中，这尤其令人惊讶。这项研究旨在模拟网络个人如何在决策制定中相互影响，以及这种影响系统可能产生的集体成果。它试图通过使模型更加现实，允许随着时间的推移并考虑基本环境来提高我们对网络战略行为的科学知识。这些进步很重要，部分原因是它们在包括公共卫生政策，智能电网和金融系统在内的广泛领域的潜在影响。 此外，该项目将有助于本科生的教育丰富，包括代表性不足和第一代学生。根据共生的合作计划，它将汇集两个不同的学生，分别是本科文科学生和研究生计算机科学专业的学生。研究结果将通过网站广泛传播，还将集成到本科和研究生级教育中。该项目研究了网络影响力的计算游戏理论研究中的几个重要开放方向。它将解决各种基本研究问题，包括确定网络中“最有影响力”的个体的挑战。特别是，这项研究有三个主要部分：（1）复杂性的挑战：网络中影响力的设计理论模型，以允许（a）行为选择的灵活性（从多个，非二元离散选择到行为选择的连续性）和（b）非线性影响，而无需任何限制对极性的影响（正/负面）。 （2）动态的现实：影响网络的模型动态演变。 （3）上下文的力量：建模战略行为的上下文环境。在这三个推力中，该项目显着脱离了研究最大化以及社交网络分析中传统的集中度测度的方法。它试图设计网络感知算法，包括可证明的近似算法和实践启发式方法，以计算稳定的结果并确定相对于理想结果的网络中最有影响力的个体。最终，研究试图为政策分析师提供计算工具，以在社交网络中执行最小的目标干预措施，以实现理想的社会成果。为此，项目将使用现实世界行为数据。它将采用，适应或扩展现有的机器学习算法来学习上下文感知的模型，而无需对网络结构施加任何限制。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的。

项目成果

Correlated Equilibria for Approximate Variational Inference in MRFs

• 发表时间: 2016-04
• 期刊: J. Artif. Intell. Res.
• 作者: Luis E. Ortiz;Ze Gong