Business sciences

Module 1 – Gamification and experiential learning Module 2 – The basic language of finance: time, risk, and value Module 3 – The firm as a player Module 4 – Markets as game environments Module 5 – Building the “Finance Game” (design and development if skills allow) Module 6 – Presentation and final tournament

No specific prior knowledge of finance or programming is required. The laboratory is designed as an introductory and experimental course intended for first-year students. However, the following basic skills are recommended: ⸻ 1. Basic logical and mathematical skills Ability to understand and apply simple algebraic and percentage formulas. Basic notions of descriptive statistics (mean, variation, probability). Understanding of the concept of a function and its graphical representation (e.g., return over time). ⸻ 2. Basic digital skills Ability to use spreadsheets (Excel or Google Sheets) to calculate, represent, and compare data. Familiarity with online navigation and searching for economic or financial data. ⸻ 3. Curiosity toward the language of finance Interest in the economic behavior of firms, markets, and institutions. Willingness to experiment and actively participate in simulation or game-based contexts. ⸻ 4. (Optional but useful) Introduction to programming Basic knowledge of Python, or willingness to learn its fundamentals during the course. Understanding of the concepts of variable, input/output, loop, and condition, which will be introduced through guided exercises. ⸻ Note for the teaching plan: The laboratory is structured so that even students with no background in computing or economics can understand the fundamental mechanisms through games, exercises, and simulations.

Shiller, R. – Finance and the Good Society. McGonigal, J. – Reality is Broken: Why Games Make Us Better. Zichermann, G. & Cunningham, C. – Gamification by Design.

In person.

Activities and relative weight Active participation and reflections on the games: 30% Group mini-project “Finance Game” (design + Python code): 40% Individual reflective report discussion (economic analysis of one’s own game): 30%

Interactive lectures, practical exercises, group work, and digital simulations. Simple tools (Excel, Python, Dash/Streamlit) will be used to promote experiential learning. Each module includes a debriefing session to connect experience with theory.