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MEng Artificial Intelligence with Industrial Experience / Course details

Year of entry: 2021

Course unit details:
Decision Behaviour, Analysis and Support

Unit code BMAN73272
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Alliance Manchester Business School
Available as a free choice unit? No

Overview

Taking decisions and enhancing decision making ability are important skills to have. Operational, tactical and strategic decisions however, are often complex in organisational and policy making contexts. This course provides a better understanding of soundly-based approaches for structuring and analysing decisions in the face of uncertainty and conflicting objectives (Decision Analysis). It explains how decisions are taken (Decision Behaviour) and how we can improve decision making capabilities at individual, group, organisational and societal levels (Decision Support).

Pre/co-requisites

BMAN73271 Programme Req: BMAN73271 is only available as an elective to students on MSc Business Analytics, MSc Data Science (Business & Management pathway) and MEng (Hons) Computer Science

Aims

The aim of this module is to provide a state-of-the-art overview on decision making in a variety of organisational settings (e.g. private, public and not-for-profit sectors). It explores how decision analysis and decision aiding technologies can help individuals, groups and organisations make better decisions. Drawing from decision theory, behavioural and psychological studies, information systems, artificial intelligence, operational research and organisational studies, the course highlights the multi-faceted challenges of decision making. The main emphasis is on prescriptive theories of decision making.

In summary, you will gain an understanding of the capabilities and types of decision frameworks and decision aiding technologies used in businesses and their impact on business performance and competiveness. You will develop analytical skills for structuring decisions and developing decision models by incorporating data from multiple sources and judgments from experts and stakeholders. You will use decision analytics tools to support your decision analysis and communicate the results.

 

Learning outcomes

By the end of the course you will:

  • Become aware of behavioural, normative and prescriptive models of decision making
  • Develop content and process skills for modelling and analysing critical decisions in prescriptive decision support
  • Understand a range of modelling frameworks, methods and tools for designing prescriptive decision processes and facilitating business decisions
  • Become aware of emerging trends in decision support technology

Teaching and learning methods

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

Assessment methods

Project project presentations: 30%

Examination: 70% (2 hours)

Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.

  • Specific course related feedback sessions.

  • Written and/or verbal comments on assessed or non-assessed coursework.

  • Written and/or verbal comments after students have given a group or individual presentation.

  • Generic feedback posted on Blackboard regarding overall examination performance.

Recommended reading

The main course text is:

Simon French, Nadia Papamichail, John Maule.  ‘Decision Making: Behaviour, Analysis and Support’  Cambridge: Cambridge University Press, 2009

Another suitable text is:

R. Sharda, D. Delen. and E. Turban. ‘Business Intelligence, Analytics and Data Science- A Managerial Perspective’ Upper Saddle River, New Jersey: Prentice Hall, 2017.

 

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 30
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Konstantinia Papamichail Unit coordinator

Additional notes

Informal Contact Method

Office Hours

Drop in Surgeries (extra help sessions for students on material they may be struggling with)

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