Complex System Simulation

Entry requirements: Recommended prior knowledge: Programming, Statistics

Credits: 4

Course: Core

Objectives

The aim of this course is to introduce students to the field of Complex Systems (modelling, syntheses and analysis) and get them directly involved in research. Students will have an opportunity to select the topic for their research mini-project in complex networks, analysing real-world data and developing a model of social networks or biological systems or road networks, etc. 

Contents

Introduction to complexity science and complex systems. Graph theory and network characteristics. Models of complex networks: regular networks, random networks, Watts-Strogatz model, Barabasi-Albert model of dynamic networks with preferential attachment, hierarchical and other advanced models. Scale-free and small-world properties. Assortativity. Centrality. Network robustness to random and distributed attack. Communities, motifs. Signal spreading, percolation.

In practical sessions students will analyse real-world data of their choice (for instance social networks, biological systems or road networks), develop a model of this complex system and conduct research using that simulation. 

Format

Lectures

Assessment

40% mini-project report and presentation, 35% written exam, 25% report on exercises in Netlogo.