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.

世界正变得越来越相互联系。虽然不是所有的联系都具有相同的重要性，甚至是相同的含义，但这些联系在一个人的行为和生活方式选择中扮演着至关重要的角色。在个人选择相互依赖的战略情景中，这一点尤其引人注目。这项研究试图对联网的个人如何在他们的决策中相互影响以及这种影响系统可能产生的集体结果进行建模。它寻求通过使模型更现实、允许随时间变化并考虑潜在背景来推进我们对网络中战略行为的科学知识。这些进展之所以重要，部分是因为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具有潜在影响。此外，该项目将有助于丰富本科生的教育，包括代表不足的学生和第一代学生。它将在一个共生合作计划下将两个不同的学生群体聚集在一起，即本科生文科学生和计算机科学研究生。研究成果将通过一个网站广泛传播，也将被整合到本科生和研究生教育中。这个项目调查了网络影响力计算博弈论研究中的几个重要开放方向。它将解决各种基础研究问题，包括确定网络中“最有影响力的”个人的挑战。具体地说，这项研究有三个主要部分：(1)复杂性的挑战：设计网络影响力的博弈论模型，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体)和(B)不受任何极性(正/负)限制的非线性影响。(2)动力学的真实性：影响网络的动态演化模型。(3)语境的力量：对战略行为的语境环境进行建模。在这三个推力中，该项目明显背离了广泛研究的影响力最大化的方法以及社会网络分析中的传统中心性衡量标准。它试图设计网络感知算法，包括可证明的近似算法和实用的启发式算法，用于计算稳定的结果，并识别网络中相对于理想结果最有影响力的个人。最终，这项研究试图为政策分析师提供计算工具，以便在社交网络中执行最低限度的有针对性的干预，以实现理想的社会结果。为此，该项目将使用真实世界的行为数据。它将使用、调整或扩展现有的机器学习算法来学习情景感知模型，而不会对网络结构施加任何限制。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

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