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363-0541-00L 3 Credits BSC , MSC , NDS D-BAUG , D-MAVT , D-INFK , D-MTEC , D-MATH , D-PHYS , D-USYS

Economic Dynamics and Complexity

Lecturers & Examiners: Dr. Luca Verginer
VVZ CR n/a

Last Updated: 2026-06-03 00:07:20

Abstract

How do economies grow and occasionally tip into crisis? How does a technology spread, or a shock cascade through a production network? This course develops a unified toolkit for understanding economic dynamics and complexity. We study continuous and discrete systems, diffusion and contagion, endogenous cycles, and the structure and formation of economic networks.

Objective

By completing this course, participants will be able to: - Analyse continuous and discrete dynamical systems, characterise their equilibria, and assess stability - Model diffusion processes — technological adoption, contagion, network diffusion — and interpret their dynamics - Recognise sources of complexity: nonlinearity, feedback, heterogeneity, and emergent behavior - Apply coupled-system models (Lotka-Volterra, Goodwin, Kaldor) to business cycles and macroeconomic dynamics - Use network-theoretic tools — centrality, spectral methods, community detection — to analyse economic systems - Understand how strategic incentives shape network formation and when stability and efficiency diverge - Implement and simulate dynamical and network models in Python

Content

The course is organized in two parts. The first covers dynamical systems; the second turns to networks. Part I — Dynamical Systems and Complexity - Motivation — Sources of complexity: interconnectedness, feedbacks, nonlinearity, and heterogeneity as drivers of emergent behavior; case studies from pharmaceutical supply chains and epidemic spread. - Growth models — Continuous-time growth dynamics; ODEs, logistic growth, harvesting models, and phase-line analysis. - Diffusion — Modelling technological diffusion; Bass model and epidemic-style diffusion processes; empirical applications. - Coupled dynamics — Systems of ODEs; predator-prey models (Lotka-Volterra); the Goodwin model of distributional cycles. - Discrete dynamics — Difference equations, the cobweb model, the logistic map, bifurcations, and deterministic chaos. - Endogenous cycles — Coupled markets, the Kaldor business cycle model, hysteresis, and limit cycles. Part II — Economic Networks - Introduction to networks — Graph-theoretic foundations; adjacency matrices; directed, weighted, and multi-layer networks. - Diffusion on networks — Network diffusion ODEs, the Laplacian matrix, spectral decomposition, and algebraic connectivity. - Network centrality and community detection — Degree, eigenvector, Katz, and PageRank centrality; the Fiedler vector; spectral graph bisection. - Strategic network formation — The connections model (Jackson-Wolinsky); efficiency and pairwise stability; externalities and the co-author model. - Dynamics of network formation — Path dependence, spatial networks, long transients, and complexity in evolving networks. Throughout, students complete weekly Python-based exercises applying these tools to simulated and real-world economic data.

Resources

Lecture Notes

Lecture slides and materials will be provided to registered students via Moodle. Details will be explained in the first lecture.

General Information

Language
English
Levels
BSC , MSC , NDS
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 90 minutes
Aids
None
Digital
The exam takes place on devices provided by ETH Zurich.
The end-of-semester examination will account for 70% of the grade.Weekly self-studies, the continuous performance assessment (obligatorisches Leistungselement), accounts for 30% to the final grade.

Course Components

Type Title Time & Place Hours
lecture with exercise Economic Dynamics and Complexity
Lecture: Tuesday, 10-12 h Exercises: Tuesday, 12-13 h
No time listed 3 h weekly

Offered In