| Unit name | Evolutionary Game Theory 3 |
|---|---|
| Unit code | MATH30050 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2D (weeks 19 - 24) |
| Unit director | Professor. McNamara |
| Open unit status | Not open |
| Pre-requisites |
MATH11300 Probability 1 |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Unit aims
To introduce evolutionary game theory; a modelling framework in biology which can be used to analyse optimal decision making by organisms when the fitness of an organism depends on the behaviour of others.
General Description of the Unit
Behavioural ecology is a branch of biology which is concerned with the natural behaviour of organisms, the evolution of this behaviour and its ecological consequences. Activities which are important for reproduction and survival will be shaped by natural selection so that behaviour is approximately optimal given the animal's environment and constraints. It is thus possible to explain much behaviour in terms of maximisation of fitness.
The fitness of one organism in a population often depends on the behaviour of other population members. When this is the case we can model the outcomes of the process of natural selection using evolutionary game theory. This course will concentrate exclusively on evolutionary game theory. It will introduce basic concepts and basic examples. It will then go on to outline a variety of conceptual issues, ranging from deciding the sex of offspring, to the conflict between parents over care of common young and the evolution of cooperation.
Although the concepts in this course are motivated by biology, many are relevant to other areas. In particular, many of the concepts are common to both evolution and economics.
Although the course will be mathematical, using fairly simple results from probability, the emphasis will be on concepts and their application rather than mathematical proofs.
Relation to Other Units
The units Financial Mathematics, Introduction to Queuing Networks, and Evolutionary Game Theory apply probabilistic methods to problems arising in various fields.
Further information is available on the School of Mathematics website: http://www.maths.bris.ac.uk/study/undergrad/
Learning Objectives
After taking this unit, the student should:
have developed skills in constructing mathematical models of biological and other systems;
Lectures, lecture notes and material on problems sheets cover the syllabus. Problem sheets with full solutions are supplied.
100% Examination
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
As there is no ideal course book lecture notes will be available on Blackboard. An overview written for biologists will also be available on Blackboard.
The following also contains useful background:
J Maynard Smith, Evolution and the Theory of Games, Cambridge University Press (1982).
