APPLIED MATHEMATICS

A.A. di erogazione 2019/2020
Insegnamento obbligatorio

(A.A. 2019/2020)
Anno di corso:
1
Tipologia di insegnamento:
Caratterizzante
Settore disciplinare:
METODI MATEMATICI DELL'ECONOMIA E DELLE SCIENZE ATTUARIALI E FINANZIARIE (SECS-S/06)
Attivita' formativa capogruppo:
Lingua:
Inglese
Crediti:
6
Ciclo:
Secondo Semestre
Ore di attivita' frontale:
40
Dettaglio ore:
Lezione (40 ore)

The aim of the “Applied Mathematics” course is to provide students with a first introduction to the topics of business analytics manageable by decision analysis and optimization tools.
Students should achieve the ability to:
1) Model real world problem;
2) Solve basic formulations of deterministic decision problems;
3) Solve basic formulations of decision making problems under uncertainty;
4) Apprise the solution, performing sensitivity analysis;
5) Read a sensitivity analysis report.

Basic knowledge of mathematics (linear functions, differential calculus and analytical geometry) and probability will be helpful.

The course "Basic Math" provides some recap on these subjects.

Decision under uncertainty:
• Influence Diagrams;
• Decision Trees;
• Sensitivity Analysis: one-way and two way;
• The value of information: perfect information and sample information;
• Risk Preferences: Risk Aversion, The Arrow-Pratt risk aversion measure, Exponential Utility.
Deterministic models for Decision making:
• Linear Programming;
• Examples of management problems and their solution through linear programming;
• Problem Solution with Solver;
• Sensitivity Analysis: Graphical approach;
• Sensitivity Analysis: Reading the SOLVER report;
• Integer linear programming.

Other topics could be added during the course.

Teaching and learning activities include face-to-face lectures and practice sessions. The instructor presents management problems modeled through quantitative methods, together with the theoretical background and the solution by dedicated software. Assessment of the solutions obtained is also discussed. In particular, lectures and practice sessions use Excel and its AddIns “Solver” and “TreePlan”. Students are strongly recommended to bring their own PC to classes.
Disclaimer: the course is taught on PC-based machines. The use of Apple machines is possible, but it will be up to students to work out the needed adjustment. Exam requires to be able to use a PC-based machine as those on which practice sessions are taught.

The grade is entirely (100%) awarded by a written exam through an on-line platform (e-learning). The assignment includes both multiple choice questions and open-ended numerical questions, with no specific proportion between them.

The exam aims to certify:
• The ability to identify a model, among those presented in the course, from a simulated real world application.
• The ability to implement the model with the appropriate software.
• The ability to interpret the model’s output.
• The ability to assess the sensitivity of the solutions compared to the input parameters.

Students will take the exam in PC-Labs, using University’s computers.
Exam time is 90 minutes. Total score of the exam is 31. The exam is passed with a score no less than 18. A score of 31 is rewarded by honor grade (30 e lode).

No partial exams will be organized.

Students with learning disability are kindly requested to contact the disability office. Strict observance of University rules are required to access any special option for the exam. Failure to comply will result in standard exam.

George E. Monahan, Management Decision Making (Spreadsheet Modeling, Analysis, and Application) August 2000, ISBN: 9780521781183

“Businesses that use 'data-driven decision-making' enjoy a 5-6% increase in productivity, Big Data for All: Privacy and User Control in the Age of Analytics, O. Teme/J. Polonetsky, Northwestern Journal of Technology and Intellectual Property 2012”.
The European Parliament issued on 2014 July, 2nd the report “Towards a thriving data-driven economy” to highlight the ongoing revolution in business and economics. Job market demands an increase in quantitative skills, especially for descriptive and predictive data analytics, data processing, simulation, visualization, decision support and the integration of results into new products. Through the two parts of the course, we address this need.