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.
