ID:
511150
Durata (ore):
40
CFU:
6
SSD:
FISICA APPLICATA (A BENI CULTURALI, AMBIENTALI, BIOLOGIA E MEDICINA)
Anno:
2026
Dati Generali
Periodo di attivitÃ
Primo Semestre (28/09/2026 - 18/12/2026)
Syllabus
Obiettivi Formativi
The course aims to equip future political scientists and prospective policymakers with the conceptual tools needed to recognize, interpret, and respond to complexity in political, social, economic, and infrastructural systems. Students will learn how interactions among many interconnected actors or components can generate emergent collective behaviour, feedback loops, cascading failures, tipping points, and abrupt system-wide changes. Particular attention will be devoted to the implications of these phenomena for decision-making and policy design, including the limits of prediction and control and the possibility of unintended consequences.
By the end of the course, students will be able to:
- explain the main concepts and mechanisms underlying complex systems;
- recognize emergent and collective phenomena in real-world political and socioeconomic settings;
- identify the conditions under which interconnected systems may become fragile, undergo cascading failures, or experience abrupt changes;
- use elementary concepts from network theory and simple formal models to describe interdependence among actors or system components;
- interpret the results of models, simulations, and graphical analyses without requiring advanced mathematical training;
- apply complexity thinking to the analysis of policy problems and reason about possible interventions in systems characterized by feedback, uncertainty, and nonlinearity.
By the end of the course, students will be able to:
- explain the main concepts and mechanisms underlying complex systems;
- recognize emergent and collective phenomena in real-world political and socioeconomic settings;
- identify the conditions under which interconnected systems may become fragile, undergo cascading failures, or experience abrupt changes;
- use elementary concepts from network theory and simple formal models to describe interdependence among actors or system components;
- interpret the results of models, simulations, and graphical analyses without requiring advanced mathematical training;
- apply complexity thinking to the analysis of policy problems and reason about possible interventions in systems characterized by feedback, uncertainty, and nonlinearity.
Prerequisiti
The course assumes familiarity with the main topics covered in "Maths for Social Sciences" and "Data-Driven Approach in Social Sciences". A basic understanding of these subjects is considered preparatory for following the theoretical and quantitative components of the course. No advanced mathematical knowledge is required.
Metodi didattici
The course will be primarily delivered through lectures using slides and/or the blackboard. The models and applications discussed will be accompanied by in-class demonstrations based on computer code, numerical simulations, and interactive widgets. These materials will subsequently be made available to students, allowing them to explore the models independently and consolidate their understanding. Throughout the course, emphasis will be placed on developing an intuitive and conceptual understanding of complexity and its implications, rather than on mastering mathematical or computational details.
Verifica Apprendimento
Assessment will be based on a written examination consisting primarily of multiple-choice questions plus a limited number of open-ended questions.
Testi
Mitchell, M. (2009), Complexity: A Guided Tour, Oxford University Press.
Lecture notes, slides, computer code, and interactive materials will be made available during the course.
Lecture notes, slides, computer code, and interactive materials will be made available during the course.
Contenuti
The topics below represent some of the main areas and applications expected to be covered. The list is not exhaustive and may be complemented by additional topics.
1. Basic notions of network theory
Basic concepts such as nodes and links, degree, centrality, communities, and different network structures will be discussed in order to provide a minimalistic mathematical vocabulary to characterize most complex systems.
2. Fragility and resilience of complex systems
Analysis of how local shocks propagate through interconnected systems, producing cascading failures and systemic collapse. Applications will include power grids and interbank networks, highlighting trade-offs between efficiency and resilience.
3. Strategic behaviour in complex systems
Basic concepts of game theory, including strategies, payoffs, equilibria, coordination problems, and social dilemmas. Evolutionary games will then be used to study how cooperation and other collective behaviours emerge in a variety of populations.
4. Climate change, feedbacks, and collective action
Simple climate models will introduce climate feedbacks, tipping points, and abrupt transitions. Climate policy will be analysed through public-goods and stock-pollutant games, focusing on free-riding, international cooperation, and the tension between individual and collective incentives.
5. Financial markets as complex systems
Financial markets will be studied as systems of interacting and adapting agents. Simple models will illustrate how herding and feedback can generate volatility, crashes, and systemic risk.
1. Basic notions of network theory
Basic concepts such as nodes and links, degree, centrality, communities, and different network structures will be discussed in order to provide a minimalistic mathematical vocabulary to characterize most complex systems.
2. Fragility and resilience of complex systems
Analysis of how local shocks propagate through interconnected systems, producing cascading failures and systemic collapse. Applications will include power grids and interbank networks, highlighting trade-offs between efficiency and resilience.
3. Strategic behaviour in complex systems
Basic concepts of game theory, including strategies, payoffs, equilibria, coordination problems, and social dilemmas. Evolutionary games will then be used to study how cooperation and other collective behaviours emerge in a variety of populations.
4. Climate change, feedbacks, and collective action
Simple climate models will introduce climate feedbacks, tipping points, and abrupt transitions. Climate policy will be analysed through public-goods and stock-pollutant games, focusing on free-riding, international cooperation, and the tension between individual and collective incentives.
5. Financial markets as complex systems
Financial markets will be studied as systems of interacting and adapting agents. Simple models will illustrate how herding and feedback can generate volatility, crashes, and systemic risk.
Lingua Insegnamento
INGLESE
Corsi
Corsi
3 anni
No Results Found