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

世界的相互联系日益紧密。虽然不是所有的联系都有同样的重要性，甚至意义也不一样，但这些联系在一个人的行为和生活方式选择中起着至关重要的作用。这在个体选择相互依赖的战略场景中尤为突出。这项研究试图模拟网络化的个体在决策过程中如何相互影响，以及这种影响系统可能产生的集体结果。它试图通过使模型更加现实，允许随时间的变化，并考虑潜在的背景，来提高我们对网络中战略行为的科学知识。这些进展之所以重要，部分原因在于它们可能对公共卫生政策、智能电网和金融系统等广泛领域产生影响。此外，该项目将有助于丰富本科生的教育，包括代表性不足的第一代学生。它将把两个不同的学生群体聚集在一起，即文科本科学生和计算机科学研究生，在一个共生合作计划下。研究结果将通过网站广泛传播，并将纳入本科和研究生水平的教育。本项目探讨了网络影响的计算博弈论研究中几个重要的开放方向。它将解决各种基础研究问题，包括识别网络中“最具影响力”个人的挑战。特别是，研究有三个主要部分：(1)复杂性的挑战：设计网络中影响的博弈论模型，以允许(a)行为选择的灵活性（从多个非二进制离散选择到连续的行为选择）和(b)不受极性限制的非线性影响（积极/消极）。(2)动态的现实：影响网络的动态演化模型。(3)情境的力量：对战略行为的情境环境进行建模。在这三个重点中，该项目明显偏离了充分研究的影响最大化方法以及社会网络分析中的传统中心性措施。它寻求设计网络感知算法，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，并确定网络中相对于理想结果最具影响力的个体。最终，该研究旨在为政策分析师提供计算工具，以在社会网络中执行最小的目标干预，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。它将采用、调整或扩展现有的机器学习算法来学习上下文感知模型，而不会对网络结构施加任何限制。该奖项反映了美国国家科学基金会的法定使命，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。

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.