CO901 Networks, Self Organisation and Emergence (2013-14)
This programme is no longer running.
Taken by students from:
|Code||Degree Title||Year of study||core or option||credits|
|P-F3P4||Complexity Science MSc||
|P-F3P5||Complexity Science MSc+PhD||
|P-F3P6/7||Erasmus Mundus Masters in Complex Systems||
Context: This is the opening module of the Complexity DTC taught programme.
This module aims to introduce some of the basic and most common models used in Complex System Theory to describe the collective features emerging from the interactions in systems of many "agents" (e.g., particles in Physics, brokers in Finance, bacteria in Biology, etc...).
Link to Module Resources
Link to Learning Outcomes
1. Precursors: Equilibrium Statistical Physics; Non-linear dynamical systems; Deterministic Chaos; Self-organised Criticality.
2. Stochastic Models.
3. Networks and Random Graph Dynamics.
4. Emergence from stochastic processes on complex networks.
The syllabus is drawn from a wide range of books - some relevant longer texts include:
"Networks: An Introduction" by MEJ Newman, OUP 2010.
"Information Theory, Inference, and Learning Algorithms" by DJC MacKay, Cambridge, 2003.
"Probability and Random Processes" (3rd ed.) by G Grimmett and D Stirzakek, OUP, 2001.
Useful monographs include:
"Random Graph Dynamics" by R Durrett, Cambridge, 2007.
"Statistical Mechanics of Phase Transitions" by JM Yeomans, OUP, 1992.
Lectures per week
2 x 2 hours
Classwork sessions per week
2 x 2 hours
Total contact hours
Private study and group working
% weighting 4
50 5 Oral Examination 20 mins 31/10/13-1/11/13 50