• Gain an overview of the wide set of issues to consider when engaging on a modelling project.
  • Ensure that the models built are as effective as possible in decision support.
  • Create models which are flexible, transparent, and robust.


  • OverviewThis course covers the issues to consider when planning and designing models, in order to achieve the maximum efficacy in decision support, as well as the principles of best practices in model implementation, to increase the flexibility and transparency of the models.
  • Practical work, exercises and quizzesStudents are expected to build simple examples for themselves as they follow the text, and in order to take the quizzes.


  • Principles of decision design. Addressing the right decision. Avoiding the choice fallacy. Decision scope, objectives, and criteria. Challenges in making good decisions. The meaning of decision quality.
  • Frameworks and tools. Frameworks and tools to analyse decision context and business strategy. Macro economics, five forces analysis, portfolio composition analysis.
  • Mapping the decision to a model. Criteria and model outputs. Sensitivity and scenario analysis. Introduction to risk, uncertainty and optimisation analysis.
  • In-depth planning. Data versus formula-driven structures.  Data sources and structures. Defining logic, variables, flow and granularity. Historical versus forecasted analysis. Role of inflation. Updating requirements. Horizontal or vertical time axis. Structural limitations versus parameter flexibility. Benefits of modularity. Approaches to link modules. Decision sequences. Logic reversals.
  • Best practices in model building. Principles for design, layout, structure, and formatting. Principles of transparency. Complexity reduction and optimisation. Circularities and other issues.

Take the course

III.1 Model Planning, Design and Best Practices

The issues to consider when planning and building models, in order to maximize their effectiveness.

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