Taken by students from:

Code	Degree Title	Year of study	core or option	credits
P-F3P4	Complexity Science MSc	1	core	12 CATS
P-F3P5	Complexity Science MSc+PhD	1	core	12 CATS
P-F3P6/7	Erasmus Mundus Masters in Complex Systems	1	optional core	6 ECTS

 

Context: This is the opening module of the Complexity DTC taught programme.

Module Aims:

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

Syllabus:

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.

Illustrative Bibliography:

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.

Teaching:

Lectures per week	2 x 2 hours
Classwork sessions per week	2 x 2 hours
Module duration	5 weeks
Total contact hours	40
Private study and group working	60

Assessment Details:

Week	Assessment	Type/Length	date/deadline	% weighting
4	Written Work	Report/5-10 pages	21/10/13	50
5	Oral Examination	20 mins	31/10/13-1/11/13	50