Studying at York
Decision & Information Analysis - MAN00018H
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2024-25 |
Module aims
The module starts with an introduction into decision analysis, covering basic concepts and some simple non-compensatory heuristic decision-making ideas. We then move on to more sophisticated compensatory, although deterministic, methods, and consider multiple decision makers and the beginnings of sensitivity analysis.
Uncertainty is introduced, and the idea of risk-averse or risk-seeking behaviour, as encoded in the concept of utility. Bayes’ theorem appears to allow us to evaluate the value of imperfect and perfect information, before we move on to some simple data-mining techniques including classification trees and clustering.
Later, the module covers linear programming. We investigate what problems are suitable, and look at how to solve them, both by hand and on computer. Finally, we look at what linear programming can tell us about the context of the problem we’ve investigated.
Module learning outcomes
A student completing this module should:
1. have a multi-disciplinary understanding of behavioural and normative theories of decision making, the value to individuals and organisations of decision support systems and be aware of current practice in the use of decision support systems.
2. have a knowledge of information and decision analytic techniques and be able to solve simple decision problems.
3. have a knowledge of various advanced decision support approaches,
4. be able to work effectively as a member of a group to evaluate decision support systems and also to analyse decision problems more generally.
Module content
Overview of different types of decision making: strategic, tactical and operational. Multi-criteria decision making. Introduction to data analytics. OR modelling systems and simulation, decision analytic systems onto activities within an organisation. Application of these techniques in solving complex decision making problem.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 80 |
| Groupwork | 20 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 80 |
| Essay/coursework | 20 |
Module feedback
Feedback will be given in accordance with the University Policy on feedback in the Guide to Assessment as well as in line with the School policy.
Indicative reading
- Goodwin, P. and Wright, G. (2014). Decision Analysis for Management Judgment. 5th Edition. Chichester: Wiley.
Provost, F. and Fawcett, T. (2013). Data science for business. Sebastopol, CA: O'Reilly.
Ragsdale, C. T. (2015). Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. Cengage Learning