ENG3002 - Decision Making Systems & Decision Theory (2023)

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MODULE TITLEDecision Making Systems & Decision Theory CREDIT VALUE15
MODULE CODEENG3002 MODULE CONVENERDr Baris Yuce (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 35
DESCRIPTION - summary of the module content

In today’s business environment, procedural decision-making is a routine daily activity for managers. Decisions must be made in dynamic and complex environments and factor in many risks and uncertainties. It is therefore crucial for managers and policy makers to understand the nature of decision-making processes and to develop strategies for choosing best alternatives among all possible options. In this module, you will learn about the theories and motivations behind decision-making processes, individual and group decision-making, and descriptive and prescriptive approaches. Moreover, decision analysis will be conducted via modelling the uncertainty and risks on daily examples and solution approaches using machine learning techniques such as Bayesian Statistics, Decision Trees, Game Theory, Monte Carlo simulation. Further, theories and techniques for multi-criteria decision-making processes will be demonstrated. Finally, you will explore applied decision support systems through case study analyses.

AIMS - intentions of the module

This module aims to provide the theories and motivations behind decision-making process, individual and group decisions. The intended learning outcome from this module is to demonstrate rational decision-making models and techniques, and related factors such under uncertain and risk conditions. This module examines the principles and algorithms for making decisions. It provides a more precise and systematic study of the formal or abstract properties of decision-making scenarios. The module considers decisions of a single individual and situations where the decisions of more than two parties are involved. Topics covered in the first part include: subjective probability, rational preferences and utility, expected utility, risk aversion, objections and alternatives to expected utility theory, and group decisions. In addition, the module will also cover several decision-making algorithms for complex and multi criteria multi decision making problems.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

ILO #1 understanding decision process and decision theory E1

 ILO #2 comprehend the probability concept, Bayes theorem, Game theory and the decision modelling under certain, uncertain and risk involved conditions E1

ILO #3 understand the Utility theory and decision functions (loss function, minimax and more), Pareto optimality and related concepts E1

 ILO #4 grasp the Multiple-Criteria Decision Making E1

 ILO #5 understand Decision Trees E1

 ILO #6  understand Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), TOPSIS ELECTRE methods E1

ILO #7 grasp other decision-making approaches E1

ILO #8 apply popular and modern decision-making technologies in industrial and commercial environment E1

ILO #9 demonstrate modelling capability of the advanced decision theory in complex industrial and engineering problems E1

ILO #10 analyse the decision-making requirements in industrial and engineering problems E1

ILO #11 apply enhanced problem-solving ability in the fields of decision theory and multi criteria decision making problems E1

ILO #12 develop communication skills E1

ILO #13  demonstrate report writing skills, project management and organisational skills E1

 AHEP ILOs - BEng 

 All attribute values mapped against this module: SM1p, EA1p, EA3p, D1p, D3p, EP1p, D4p, EP4p, ET6p, G1p, G2p, G3p, G4p,

AHEP ILOs - MEng 

All attribute values mapped against this module: SM1m, EA1m, EA3m, D1m, D3m, EP1m, SM5m, D4m, EP4m, ET6m, G1m, G2m, G3m, G4m.

SYLLABUS PLAN - summary of the structure and academic content of the module

1: Introduction to decision theory and decision matrix and decision-making process:

2: Decision modelling, utility theory, under deterministic, uncertain and risk circumstance:

 3: Pareto optimality:

4: Probability concept, Bayes theorem, Game theory: 

 5: Multi-criteria multi decision making:

 6: Decision trees:

 7: AHP and ANP method: 

 8: TOPSIS method:

9: ELECTRE method:

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 22.00 Guided Independent Study 128.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning & teaching activities 22 Lecture
Guided independent study 128 Independent Study

 

 
ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
SUMMATIVE ASSESSMENT (% of credit)
Coursework 0 Written Exams 100 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written Exam - closed note (E1) 100 2 hours 1-13 Oral, by request

 
        

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Written Exam - closed note (E1)  Written Exam - closed note (2 hours) (E1)  1-13 August Ref/Def period
       

 

RE-ASSESSMENT NOTES

Reassessment will be by written exam. For deferred candidates, the module mark will be uncapped. For referred candidates, the module mark will be capped at 40%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

 

ELE:

 

Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Berger, J. O. Statistical Decision Theory and Bayesian Analysis Springer-Verlag 1985 [Library]
Set Fishburn, P. C. Utility theory for decision making John Wiley & Sons 1970 [Library]
Set Gilboa, I. and Schmeidler, D. A Theory of Case-Based Decisions Cambridge University Press [Library]
Set Howard, R. Applied statistical decision theory Wiley Classics Library 2000 [Library]
Set Lee, P. T. W. and Yang, Z. Multi-Criteria Decision Making in Maritime Studies and Logistics Applications and Cases Springer International 2018 [Library]
Set Munier, N. Hontoria, E. Jimenez-Saez, F. Strategic Approach in Multi-Criteria Decision Making: A Practical Guide for Complex Scenarios Springer 2019 [Library]
Set Munier, N. A Strategy for Using Multicriteria Analysis in Decision-Making: A Guide for Simple and Complex Environmental Projects Springer 2011 [Library]
Set Peterson, M. An Introduction to Decision Theory Cambridge University Press 2009 [Library]
Set Rapoport, A. Decision Theory and Decision Behaviour Palgrave Macmillan 1998 [Library]
Set Smith, J. Q. Bayesian Decision Analysis: Principles and Practice Cambridge University Press 2010 [Library]
Set Spires, E. E. Using the Analytic Hierarchy Process to Analyze Multiattribute Decisions. Multivariate Behavioral Research, v. 26, n.2, pp.345-61 1991 [Library]
Set Triantaphyllou, E. Multi-Criteria Decision Making Methods: A Comparative Study Springer 2000 [Library]
Set Weirich, P. Models of Decision-Making: Simplifying Choices Cambridge University Press 2015 [Library]
Set Weirich, P. Realistic Decision Theory: Rules for Nonideal Agents in Nonideal Circumstances Oxford University Press 2004 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 14 May 2019 LAST REVISION DATE Wednesday 18 January 2023
KEY WORDS SEARCH Defined Statistical analysis, decision theory