International Forecasting and Simulation 2104-M-D1PISM

Course Content Description: The course focuses on developing analytical skills and future-oriented thinking within the dynamics of the international environment. Basic analytical categories are discussed, such as trends, megatrend, weak signals, and psychological factors in decision-making (heuristics, overconfidence). Students learn methodical tools including game theory, simulations, and alternative scenario building, and then apply these tools in developing a practical forecasting project. Student Workload Balance (2.0 ECTS = 50 hours):Participation in seminar classes in the classroom: 20 hours. Academic consultations and project evaluation: 2 hours. Literature study and class preparation: 13 hours. Teamwork, preparation, and development of the forecasting project: 15 hours. TOTAL STUDENT WORKLOAD / ECTS CREDITS: 50 h / 2.0 ECTS.

1. Introduction to international forecasting. Course organization, aims, and learning outcomes.
2. Variable, variable distribution: central tendency and dispersion measures. Correlation and cause-effect relation.
3. a/ Methods and techniques of international forecasting - main concepts, classifications, methods: trend extrapolation, analogy, indicators, time series analysis, barometers, econometric models.
b/ small groups formation and the rules of case studies preparation
4. a/ Heuristic methods and working in small groups.
b/ discussion of case studies proposals provided by small groups
5. Examples of international forecasts. Critical analysis, discussion.
a/ The Limits of Growth
b/ Global Trends 2015
6. Game theory in international forecasting, part 1.
7. Game theory in international forecasting, part 2.
8. Presentation of drafts of forecasts developed by students, part 1.
9. Presentation of drafts of forecasts developed by students, part 2.
10. Heuristics and the most typical errors in inference.
11. Excessive self-confidence as a challenge in forecasting.
12. "Big data" and international forecasting.
13. Presentation of final versions forecasts developed by students, part 1.
14. Presentation of final versions forecasts developed by students, part 2.
15. Wrap-up.

Course coordinators

Term 2025Z:

Beata Górka-Winter

Term 2026Z:

Beata Górka-Winter
Michał Nadziak
Anna Wróbel

Type of course

obligatory courses

Mode

Classroom

Prerequisites (description)

A student starting the course should possess basic knowledge of the theory and practice of international relations, general knowledge of contemporary global political, economic, and social processes, as well as the ability to acquire and critically analyze information from written and digital sources at the first-cycle (Bachelor's) degree level.

Learning outcomes

Category: KNOWLEDGE (K_W):K_W01: Explains in an in-depth manner the specifics of international relations studies as well as the methodologies and theories used in forecasting socio-political processes. K_W05: Classifies in an in-depth manner international forecasting and simulation methods and characterizes cognitive biases, heuristics, and decision-making mechanisms affecting analytical research. Category: SKILLS (K_U):K_U01: Conducts a critical analysis of international reality using advanced forecasting methods, game theory, and scenario analysis. K_U02: Formulates forecasting hypotheses and selects appropriate research sources and tools to develop an original forecasting project. K_U03: Conducts academic debate, communicates conclusions from forecasting analyses, and substantively defends their position in academic discussion. K_U04: Leads a task team in developing a forecasting project and collaborates effectively within a group. Category: SOCIAL COMPETENCES (K_K):K_K01: Critically assesses possessed knowledge and results of their own forecasting analyses, demonstrating a self-critical attitude. K_K02: Recognizes the importance of expertise and consults experts when encountering difficulties in independently resolving a research problem. K_K05: Responsibly performs team tasks while adhering to principles of scientific integrity, academic ethics, and intellectual property protection.

K_W01, K_W05, K_U01, K_U02, K_U03, K_U04, K_K01, K_K02, K_K05

Assessment criteria

Formal Conditions and Attendance Policy:Attendance at seminars is mandatory. Students are entitled to 1 unexcused absence per semester. Subsequent absences (excused by medical certificates or unforeseen events) require completion of a make-up assignment specified by the Instructor (e.g., preparing a written case study analysis or discussing reading assignments during office hours). Rules on Artificial Intelligence (AI) tools: The use of AI tools is permitted strictly in a supporting role (e.g., preliminary literature search or language proofreading). Generating project content using AI is prohibited. The scope of AI usage must be explicitly described in the project documentation. Final Grade Components (Point System: max 100 points / 100%):Development and presentation of a group forecasting project (max 60 pts / 60%): Evaluation includes methodological correctness, critical selection of sources, structure of forecasts/scenarios, and quality of presentation and defense of assumptions before the class. Assigned learning outcomes: K_W05, K_U01, K_U02, K_U04, K_K01, K_K02, K_K05. Participation in class discussions and workshops (max 40 pts / 40%): Continuous assessment of activity, preparation based on assigned readings, and substantive contribution to discussions. Students experiencing difficulty with public speaking may compensate for part of the points by submitting written micro-assignments/analytical notes. Assigned learning outcomes: K_W01, K_U03, K_K01.

Bibliography

M. Sułek, „Prognozowanie i symulacje międzynarodowe”, Warszawa 2010.
A. Wojciuk, Metody twórczej pracy grupowej w analizie polityki zagranicznej i dydaktyce stosunków międzynarodowych, w: "Stosunki międzynarodowe", nr 1 -2 (t.43), 2011.
D. Kahneman, "Pułapki myślenia. O myśleniu szybkim i wolnym", Media Rodzina, Warszawa, 2012.
B. Bueno de Mesquita, „The Predictioneer’s Game. Using the Logic of Brazen Self-Interest to See and Shape the Future”, New York: Random House, 2009.
G. Wieczorkowska, A. Wierzbiński, Statystyka. Analiza Badań społecznych, (all editions)
A. Dixit, S. Skeath, „Games of Strategy”, New York/London: Norton & Co. (all editions)
K. Cukier, V. Mayer-Schoenberger, "The Rise of Big Data. How It's Changing the Way We Think About the World", Foreign Affairs, May/June 2013.

Additionally:
G. Allison, P. Zelikow, „Essence of Decision. Explaining the Cuban Missile Crisis”, 2nd edition, Longman, 1999.
N. N. Taleb, „The Balck Swan. The Impact of Highly Improbable”, New York: Random House, 2007.
M. Granger Morgan, M. Henrion, „Uncertainty. A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis”, Cambridge University Press, 1990.
R. Heuer, R. Pherson, „Structured Analytic Techniques for Intelligence Analysis”, CQ Press, 2010.
R. Lempert, S. Popper, S. Bankes, Shaping the next one hundred years : new methods for quantitative, long-term policy analysis, RAND, 2003.
S. Renshon, D. Welch Larson, „Good Judgment in Foeign Policy. Theory and Application”, Oxford: Rowman, 2003.

Notes

Term 2026Z:

AI integration up to the level 4.