MATHEMATICAL MODELS FOR DECISION MAKING

The course will cover some theory, mathematical tools and applications of decision models, stressing practical use of the software R (downloadable at http://cran.r-project.org/ or https://www.rstudio.com/ ) to solve exercises and study cases. Topics will include the formal definitions of problems and models used to take decisions in economics and finance.

a) Knowledge and understanding:
- formal definitions of decision problems and their resolution methods
- terminology to formulate optimization problems and their applications;
- methods to rank a set of discrete alternatives;

b) Applying knowledge and understanding:
- be able to solve linear problems with the software R;
- be able to formulate a constrained or unconstrainted optimization problem and find a numerical solution;
- be able to handle a decision problem using decision-making methods and obtain its computational solutions.

c) Making judgments:
- understand and critically assess numerical results obtained solving some decision problems and evaluate potential issues of the tools that were used;
- suggesting, if appropriate, revision and further investigations with respect to the obtained solutions

Students must have knowledge of calculus and the basics of matrices and vectors. There are no prerequisites related to R or programming and no previous experience in decision theory is assumed.

Mathematics tools to support decision theories
Introduction to the R platform
Introduction to the concept of preferences
Optimization problems
Decision-making methods and models

There is no formal textbook. The learning material will be provided online and it will include slides, notes and videos.

A combination of assessments including multiple-choice tests, development of case studies and final written examination

Teaching will take place in the classroom and online with the use of videos, exercises and case studies to be carried out even on multimedia platforms. Forums and online tests will be activated following each topic covered to verify students learning. Students will have the opportunity to interact with the lecturer on a weekly basis to clarify doubts about the material distributed online

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