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CO901 Networks, Self Organisation and Emergence (2013-14)

Module Leader: Dr Thomas House (Mathematics and Complexity)

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