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

世界正变得越来越相互关联。虽然并非所有的连接都具有相同的重要性，甚至相同的意义，但这些连接在一个人的行为和生活方式选择中起着至关重要的作用。在个人选择相互依赖的战略情景中，这一点尤其突出。本研究旨在模拟网络化个体如何在决策过程中相互影响，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，允许随着时间的推移而变化，并考虑潜在的背景，来提高我们对网络中战略行为的科学认识。这些进展之所以重要，部分原因在于它们在公共卫生政策、智能电网和金融系统等广泛领域的潜在影响。 此外，该项目将有助于丰富本科生的教育，包括代表性不足和第一代学生。它将汇集两个不同的学生群体，即本科文科学生和计算机科学研究生，根据共生合作计划。研究成果将通过一个网站广泛传播，并将纳入本科和研究生教育。本项目调查了网络影响力的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，包括识别网络中“最有影响力”的个人的挑战。具体而言，该研究有三个主要部分：（1）复杂性的挑战：设计网络中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连续的行为选择）和（B）不受极性（积极/消极）限制的非线性影响。(2)动态的现实：影响力网络的动态演化模型。(3)情境的力量：塑造战略行为的情境环境。在这三个方面，该项目显著偏离了影响力最大化的研究方法以及社会网络分析中的传统中心性措施。它旨在设计网络感知算法，包括可证明的近似算法和实用算法，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，以在社会网络中进行最小的有针对性的干预，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。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.