COMP6216 Simulation Modelling for Computer Science
Module Overview
Simulation modelling plays an increasingly significant role across modern science and engineering, with the development of computational models becoming established practice in industry, consulting, and policy formulation. Computer scientists are often employed as modellers or software engineers to help in the model development & maintenance cycle. Therefore this is a current and future need for computer science graduates to have a grounding in both the philosophy of modelling in science and various modelling techniques. This module will familiarise students with general knowledge about the role of modelling in science (with a particular emphasis on computational modelling), will discuss the process of model development and best practice in various stages in the model development cycle. A second (and larger) part of the module will provide a broad survey of the central modelling paradigms. Throughout the module we will demonstrate how computer science techniques are used to develop models in the following domains: - Information networks - Design and management of infrastructure - Epidemics - Natural resource management - Computational economics - Collective robotics - Online trading systems - Climate and Earth system processes
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Recognise the main elements of scientific methods - what is a model, what is a computational model?
- Detail the role of a computer science in the development of scientific models
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Discriminate between different modelling approaches and evaluate their pros and cons
- Evaluate and present the output of a computational model
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Design and implement a computational model
Syllabus
- Modelling platforms and environments (Stella, Netlogo, Repast) - Dynamical systems modelling (introduction to numerical integration schemes) - Systems dynamics - Agent Based Models - General equilibrium modelling - Finite elements - Networks - Monte Carlo methods - Scientifc computing using Python
Learning and Teaching
| Type | Hours |
|---|---|
| Tutorial | 12 |
| Lecture | 24 |
| Preparation for scheduled sessions | 12 |
| Follow-up work | 12 |
| Wider reading or practice | 27 |
| Completion of assessment task | 63 |
| Total study time | 150 |
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Project | 70% |
| Project | 30% |
Repeat
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External