The world we live in consists of evolving structures and patterns. The application of quantitative methods and computational tools to study complex, evolving systems reflects an ongoing shift in the intellectual landscape of the natural and social sciences and also parts of the humanities. Each domain is undergoing a revolution sparked by the dramatic increase in the availability of data and in methodologies that allow for deeper interpretation of the patterns present in data. What many arts and sciences disciplines seek, more than ever before, is underlying theory – mathematical in nature in some fields, qualitative in others – for the dynamical processes that produce data, i.e., a framework to draw inferences from data without being overwhelmed by the amount of data collected. Many also wish to master and use new technologies capable of conducting “big data” analyses. This initiative recognizes that these enterprises are linked, requiring expertise for navigating scholarship as well as infrastructure for managing large data repositories.
Our unique strengths across SAS in the quantitative exploration of evolving systems hold great collective potential to provide intellectual leadership. The School will tap the deep reservoir of expertise in this area to aid scholars throughout the University, from undergraduates to faculty, to tackle disparate problems through a common understanding of the dynamic and path-dependent processes responsible for generating data. Problems that can be approached in this way span the natural and social sciences and the humanities. Just a few examples are the evolution of social networks as studied in Psychology and Biology, bioinformatics, studies of the evolution of publishing through customizable search algorithms in English, the dynamics of knowledge production in Philosophy, the evolution of language studied through Linguistics and Biology and enabled by our pioneering Linguistic Data Consortium, evolution of prices in Economics and Computer and Information Science, and the dynamics of crime with Criminology and Statistics. Expertise spans not just SAS but Annenberg, Wharton, Medicine, and Engineering. Although some faculty and students have already formed collaborative links across fields to study evolving systems, in many cases they have been unaware of the vast potential for synergy within the School. In addition, little effort has been made to expose undergraduates to the value of analyzing evolving systems in a data-rich world.
The breadth of interdisciplinary research coupled with the depth of existing strengths in theory and analysis within SAS calls for a centralized body to serve as a focal point for research and education in evolving systems. The primary aim of this function will be to foster interdisciplinary interactions that generate novel and unexpected intellectual pursuits through sponsorship of intensive “boot camps” in quantitative modeling and data analysis across diverse disciplines for faculty, post-doctoral fellows, and graduate students; support of post-doctoral scholars who undertake research projects that bridge the interests of two faculty sponsors from different SAS departments; an annual symposium on a theme chosen each year to highlight an application of evolving systems to two or more disciplines; and the development of three undergraduate courses, to be taken in a series, that introduce model-based thinking, statistical programming, and cross-disciplinary topics in data analysis. This focus calls for expertise among scholars not presently on campus; hence the School will seek opportunities to build its faculty resources in these areas when authorizing regular faculty searches in Mathematics, the social sciences, and the humanities. Such appointments would emphasize faculty whose strengths are in “big data,” or whose theoretical work is on complex evolving systems, or whose interests enhance bridges between subject matter experts in SAS and data-science experts in other schools at Penn. They would facilitate the development and use of technological infrastructures needed for the University to lead in the quantitative evaluation of evolving systems.