NetSe: Medium: Network Structure, Incentives, and Outcomes

该项目正在开发和验证互联网及其应用程序设计中的几个核心问题的严格模型，这些问题可以被视为由采用某种算法过程的软件代理组成的不断发展的分布式系统。 在这些系统中，人们可以预测一个特定任务的结果作为一组输入变量的函数：代理通信模式，个人激励和算法策略-以前已经孤立地研究过的方面，但很少一致。 PI正在开发一个分析框架，明确说明网络结构和代理激励。通过考虑简单的任务，如信息扩散，搜索，和领导者选举，他们将构建一个理论，可以应用到实际问题，如互联网路由，拥塞控制，和信誉系统。智力优点：这个项目汇集了一个研究团队的背景跨越经济学，算法，网络开发一个统一的方法来复杂的社会网络，捕捉网络结构和代理人的激励，通过一个严格的算法框架。 本项目采用了人体实验、算法设计、经济分析、大规模模拟等多种研究方法。影响范围更广：本项目的多学科方法有望全面了解网络结构和网络中代理人激励的影响。 该项目正在培训研究生和本科生掌握处理社交网络的必要技能，社交网络正在成为许多应用程序的基础。PI正在根据该项目进行的研究设计两个新课程。

This project is developing and validating rigorous models of several central problems in the design of the Internet and its applications, which can be viewed as evolving distributed systems composed of software agents employing some algorithmic process. In these systems, one can predict the outcome of a particular task as a function of a set of input variables: the agent communication pattern, individual incentives, and algorithmic strategy---aspects that have previously been studied in isolation, but rarely in concert. The PIs are developing an analytic framework that explicitly accounts for both network structure and agent incentives. By considering simple tasks like information diffusion, search, and leader election, they will construct a theory that can be applied to practical problems such as Internet routing, congestion control, and reputation systems.Intellectual Merit:This project brings together a team of researchers with backgrounds spanning economics, algorithms, and networking to develop a unified approach to complex social networking that captures both network structure and agent incentives through a rigorous algorithmic framework. This project employs several research methodologies including human-subject experimentation, algorithm design, economic analysis, and large-scale simulation.Broader Impact:The multidisciplinary approach of this project promises to lead to a comprehensive understanding of the effect of network structure and agent incentives in networks. The project is training graduate and undergraduate students in the skills necessary to deal with social networks, which are emerging as a fundamental underpinning of many applications. The PIs are designing two new courses based upon the research conducted in the project.