MG310      Half Unit
Analytics for Strategic Decisions

This information is for the 2017/18 session.

Teacher responsible

Dr Valentina Ferretti


This course is available on the BSc in Management. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.


Elementary statistical and mathematical concepts.

Course content

How to choose in tough situations where stakes are high, and there are multiple conflicting objectives? How do we perceive risk, and how to act when there are risks and uncertainties involved in a decision? How can we create options that are better than the ones originally available? Decision making is a central aspect of virtually every management and business activity, including marketing, strategic planning, marketing management, resource allocation, operations management, and investment. Moreover, important decisions are not only made by managers and entrepreneurs, but also by the consumers of their goods and services, and by their business rivals, partners and employees. The ability to make better decisions is an invaluable part of everyone’s toolbox. It is this ability that will be developed in this course, which introduces students to the use of Risk and Decision Analysis as a form of analytics that supports decision making in private, voluntary and public organisations.  The course shows how a consistent and realistic mix of data and judgement can help decision makers to better achieve their objectives. Based on sound theory underlying normative, descriptive and prescriptive decision-making research, the course emphasises the practical application of Risk and Decision Analysis for decision-making across many different contexts.

The course is designed to enhance the students’ decision capabilities when confronted with strategic or operational choices, when searching for decision opportunities, and when designing strategies and policies.  It uses real-world Risk and Decision Analysis applications in organisations and public policy making, and employs several case-studies (supported by specialised decision software) to build students' skills in decision modelling and analysis. It covers modelling and supporting decisions involving multiple stakeholders and conflicting objectives (multi-criteria decision analysis) as well as uncertainty (decision trees, influence diagrams, and risk analysis).


20 hours of lectures and 13 hours and 30 minutes of classes in the LT.

Students on this course will have a reading week in Week 6, in line with departmental policy.

Formative coursework

Two formative assignments:

1. Group project plan presentation (i.e. personal decision context selected, due in week 7)

2. Individual review of an anonymous technical report developed from students who took this course last year. Students will have to review the report by following specific criteria and by completing a set of both descriptive and evaluative tasks (e.g. indicating the strongest part of the report, indicating sentences or paragraphs that seem out of order, incompletely explained or in need of revision, etc.). This review assignment will help students to improve their reading, writing and collaborative skills.

The topic of the project (i.e. a decision making problem to be modelled and analysed by means of Multicriteria Analysis) can be a personal decision (i.e. which job offer to accept when confronted with multiple ones, which master to apply for, etc.). Students will have to collect data, develop and apply a quantitative model, interpret the results and refer to the key scientific literature for the main steps in the development of the model. Students are allowed to work in groups of maximum 4/5 people. In the individual technical report of the group project, students will have to report on the developed process. This assignment will help students develop their operational problem solving skills by demonstrating their ability to apply a quantitative model to solve an operational problem, interpret its results, and develop sound recommendations.

Indicative reading

Belton, V. and Stewart, T. (2002) Multiple Criteria Decision Analysis. London, Kluwer.

Bouyssou, D., Marchant, T., Pirlot, M., Tsoukias, A., and Vincke, P. (2006) Evaluation and Decision Models with Multiple Criteria. Stepping stones for the analyst. Springer, International Series in Operations Research & Management Science, Vol. 86.

Bouyssou, D., Marchant, T., Pirlot, M., Tsoukias, A., and Vincke, P. (2007) Evaluation and Decision Models with Multiple Criteria. A critical perspective. Springer, International Series in Operations Research & Management Science, Vol. 32.

Clemen, R.T. and Reilly, T. (2014) Making Hard Decisions. Pacific Grove: Duxbury.

Edwards W., Miles Jr R.F. and von Winterfeldt D. (eds). Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press: New York.

Eisenführ, F., Weber, M. and Langer, T. (2010) Rational Decision Making, 1st ed. Berlin: Springer.

Goodwin, P. and G. Wright (2014). Decision analysis for management judgement. Chichester, Wiley.

Ishizaka, A. and Nemery, P. (2013) Multi-criteria Decision Analysis: Methods and Software. Wiley

Keeney, R.L. (1992) Value-Focused Thinking: A Path to Creative Decision-making. Cambridge: Harvard Univ. Press. HD30.23 K21 (Course Collection).

Keeney, R. L. and Raiffa, H. (1993) Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge: Cambridge University Press, 2nd ed.

Mcnamee, P. and Celona, J. (2007) Decision Analysis for the Professional. Menlo Park: Smart Org, 4th ed (e-book available in the library).

G.S. Parnell et al. (2013) Handbook of Decision Analysis. Hoboke, Wiley.

Von Winterfeldt, D. & Edwards, W. (1986) Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press.


Presentation (40%) and other (60%).

The presentation is a group project due in Week 11 of Lent Term.

The other assessment is an individual technical report on the group project due in Week 1 of Summer Term.

Teachers' comment

In May 2017 Dr Valentina Ferretti was awarded an LSE Student-led Teaching Excellence Award in the category of Inspirational Teaching for this course

Key facts

Department: Management

Total students 2016/17: 24

Average class size 2016/17: 23

Capped 2016/17: Yes (34)

Value: Half Unit

Guidelines for interpreting course guide information

PDAM skills

  • Team working
  • Problem solving
  • Application of information skills
  • Application of numeracy skills
  • Specialist skills

Course survey results

(2014/15 - 2016/17 combined)

1 = "best" score, 5 = "worst" score

The scores below are average responses.

Response rate: 100%



Reading list (Q2.1)


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