Quantitative Methods in Complexity Research

About

The course will focus on an overview of various quantitative methods of complexity research. We will begin with the mathematical framework of stochastic processes and dynamical systems, commonly used to represent complex systems in mathematical terms. Then we will proceed to more directly applied subjects: Monte Carlo methods, used to simulate processes too complex to be mathematically tractable; graph theory and network analysis, used to describe and analyze systems decomposable into discrete interacting components; and machine learning. The latter will be discussed both as a tool for analysis of complex systems, in particular for classification and pattern detection, and as a tool for processing of data unsuitable for direct statistical analysis (such as natural language data). The focus of the course will be on which method to choose for a particular analysis task (what kind of questions the method is designed to answer, what kinds of data it requires, what are its assumptions, etc.) rather than how to apply it.

Course leader

Magdalena Kossowska-Lai, PhD, Jagiellonian University (Poland)

Target group

The course is intended for all students of all levels.

Fee info