OVERVIEW

This course offers students the opportunity to delve into the core of economic decision-making by tackling real-world problems involving consumers, investors, and firms operating in the production of goods and services. The aim is to provide practical tools to support complex choices in real economic and social contexts.

After a brief theoretical introduction to the main decision-making models and methods, the course focuses on developing practical skills in problem solving and business analytics through hands-on lab sessions on case studies using specialized software and artificial intelligence tools.

Students will revisit economic problems already encountered in earlier years of the program, but from an applied and practical perspective aimed at finding optimal solutions. Designed for third-year students, this course serves as a bridge to the professional or research world, equipping them with skills that are immediately applicable in analytical and business settings.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide students with a basic knowledge about the main quantitative methods to support decision-making processes in economics, both in certain and uncertain environments. The course also aims to let the students be able to use the reference software environments for solving the different problems faced. From a methodological point of view will be introduced single decision maker optmization problems, in particular using convex programming models to optimize different objective functions (such as profit and utility maximization and cost minimization), decision models under uncertainty and risk and game theory decision methods to deal with situations of strategic interaction.

AIMS AND LEARNING OUTCOMES

This course aims to guide students in discovering decision-making models through a path that integrates theory, methodology, and application. Students will be supported in the analysis and resolution of real-world problems in economic and social contexts, acquiring both technical and operational skills that are valuable in professional and research settings.

From the very first lessons, theoretical insights will alternate with hands-on lab sessions, where students will experiment with Excel, dedicated optimization software, and artificial intelligence tools to tackle concrete decision-making problems. The goal is to develop solid proficiency in the use of mathematical models and digital tools, enabling students to approach complex real-world situations with autonomy and critical thinking.

By the end of the course, students are expected to have acquired the skills to understand, model, and solve various types of real-world problems, using decision models and relevant software environments with growing confidence and competence.

Learning Outcomes

By the end of the course, students will be able to:

• Understand and apply the main tools and methods of decision analysis in economic and social domains.
• Identify the key components of a decision-making problem: decision-makers involved, available data, constraints, objectives, and variables.
• Apply mathematical models and optimization methods to formalize and solve complex problems, including through advanced digital tools.
• Develop original solutions, critically evaluate alternatives, and communicate decisions clearly using appropriate technical language.
• Build critical and analytical thinking, along with strong independent judgment in problem evaluation, modeling, and resolution.
• Strengthen skills relevant for advanced quantitative master’s degrees, particularly in the economics field.
• Improve written and oral communication skills, tailoring messages to different audiences and contexts, and effectively using sources, data, and tools.
• Consolidate soft skills such as time and stress management, concentration, autonomous decision-making, teamwork, and positive interpersonal relationships.
• Develop awareness of one’s own learning strategies, self-assessment capabilities, and the ability to set and pursue personal and professional goals.

Soft Skills and Certifications

The course actively contributes to the development of key soft skills essential for today’s job market, in particular:

• Functional literacy competence (advanced level)
• Personal and social competence (advanced level)
• Learning-to-learn competence (advanced level)

Successful completion of the course entitles students to earn the corresponding Open Badges, which are also valuable additions to their educational and professional portfolios.

PREREQUISITES

Suggested even not mandatory: Mathematics and Statistics

TEACHING METHODS

Lectures, analysis of case studies, exercises and labs using software (Excel and optimization software). Activities with active, interactive and constructive teaching techniques, such as Flipped Classroom, Team Based Learning and Problem based learning, will be proposed. The teaching mode of the calendar classes will be communicated on the Aulaweb page of the teaching (registration required and recommended).

Working students are advised to contact the teacher at the beginning of the course to agree on teaching and examination arrangements so to take into account individual learning patterns, while respecting the teaching objectives.

Students who have valid certification of physical or learning disabilities  and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Elena Lagomarsino elena.lagomarsino@unige.it, the Department's disability liaison.

SYLLABUS/CONTENT

For each part of the course, the theoretical discussion intended to provide basic theory content, will be complemented by the practical/laboratory part in the computer lab using specific software tools (Excel solver and optimization software).

Part I:

• Introduction to decision-making and problem solving: from real problems to mathematical models.
• Decision models classification.
• Introduction to non-linear, convex, linear and integer mathematical programming.
• Resource allocation problems arising in the production of goods and services
• Supply and demand matching problems
• Linear programming, duality, economic interpretation and sensitivity
• Introducing non-linear hypotheses on prices and costs

Part II:

• Study of functions in several variables: unconstrained maxima and minima.
• Constrained optimization, Lagrangian functions and economic interpretation of Lagrange multipliers.
• Consumer choice models and utility maximization.
• Company choice models: cost minimization and profit maximization.
• Models for selecting investments and portfolio oprimization.

Part III: 

• Decision trees and decision theory considering uncertainty and risk.
• Optimization of the expected economic value of decisions.
• Utility Theory.
• Introduction to Game Theory.
• Study and modeling situations of strategic interactions (Pure and mixed strategies). Game theory and optimization.

RECOMMENDED READING/BIBLIOGRAPHY

The slides used by the teacher, text books and other additional handouts for foreign students will be communicated at the beginning of the course and published on the course Aulaweb page

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

I Semester - check the official calendar of the teaching activities in the Department website (Lessons timetable | DIEC)

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The assessment of the learning outcomes is based on a written exam consisting of ten multiple-choice questions and two open-ended questions or exercises, and the completion of a project work (which may also be done in groups) for students who want to to improve their final grade.

For attending students, some of the activities proposed during the lab sessions (such as case studies and Team-Based Learning activities communicated via Aulaweb) will be evaluated in the final grade. In-class activities and asynchronous work completed at home (quizzes or exercises) will be assessed as bonus points contributing to the final grade.

Students with disabilities, with SLD or with SEN are reminded that, to request exam accommodations, they must first upload their certification to the University website at servizionline.unige.it<https://servizionline.unige.it/>, in the “Students” section. The documentation will be checked by the University’s Services for the Inclusion of Students with Disabilities and with SLD.

At the beginning of the course, students are advised to contact the lecturer to agree on exam arrangements which, while respecting the learning objectives of the course, take individual learning needs into account.

To request compensatory tools or dispensatory measures, students with disabilities or SLD must fill in the dedicated Webform available athttps://unige.it/disabilita-dsa, at least 7 working days before the exam.

Students with SEN may instead send their request by e-mail to the lecturer, copying the Department Representative, Prof. Elena Lagomarsino, atinclusione.economia@unige.it<mailto:inclusione.economia@unige.it>, and the Inclusion Office atinclusione.studenti@info.unige.it<mailto:inclusione.studenti@info.unige.it>.

Requests from students will be assessed by the lecturer and may be approved or rejected.

ASSESSMENT METHODS

The written exam is designed to assess the level of knowledge and understanding of the theoretical topics covered in class. In contrast, the ability to evaluate critically and to apply the acquired knowledge is assessed through lab sessions and selected group activities carried out in class, or through the project work.

The grades for the written exam and the lab/project work are expressed in thirtieths, and the final grade will be calculated as their average.

Participation in in-class activities and the completion of assigned work at home may lead to an increase of up to 3 points on the average grade described above.

FURTHER INFORMATION

Not compulsory.

The course is available on aulaweb. All students are invited to periodically consult the page of this course on the AulaWeb portal (http://www.aulaweb.unige.it/), where they will find further information and updates.

It should be noted that to take into account the findings of last year's teaching assessment questionnaires, more time will be devoted to labs and the use of software to solve case studies, Team Based Learning and problem based learning activities.