Complexity and Global Systems Science

Main content

Fall Semester 2016

This is the website for the course entitled "Complexity and Global Systems Science" in the fall semester 2016. The lectures take place every Monday from 17.00 to 19.00 in room IFW B42. Course material for students will be published below. More detailed information about the course may be found here.

Course material is intended for personal use in the context of this course only; redistributing, citing or publishing any of the material is strictly prohibited. If prompted, please enter your ETH username and password to download course materials.


This course discusses complex techno-socio-economic systems, their counter-intuitive behaviors, and how their theoretical understanding empowers us to solve some long-standing problems that are currently bothering the world.


Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop models for open problems, to analyze them, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to think scientifically about complex dynamical systems.


This course starts with a discussion of the typical and often counter-intuitive features of complex dynamical systems such as self-organization, emergence, (sudden) phase transitions at "tipping points", multi-stability, systemic instability, deterministic chaos, and turbulence. It then discusses phenomena in networked systems such as feedback, side and cascade effects, and the problem of radical uncertainty. The course progresses by demonstrating the relevance of these properties for understanding societal and, at times, global-scale problems such as traffic jams, crowd disasters, breakdowns of cooperation, crime, conflict, social unrests, political revolutions, bubbles and crashes in financial markets, epidemic spreading, and/or "tragedies of the commons" such as environmental exploitation, overfishing, or climate change. Based on this understanding, the course points to possible ways of mitigating techno-socio-economic-environmental problems, and what data science may contribute to their solution.


Course Material

Date Topic
Lecture 1 (PDF, 8.3 MB); Info and Grading (PDF, 74 KB)
03.10. Lecture 2 (PDF, 9.4 MB)
10.10. Lecture 3 (PDF, 10.9 MB)
Lecture 4 (PDF, 16.5 MB)
Lecture 5 (PDF, 62.2 MB)
Lecture 6 (*see lectures 5 & 8 for slides)
Lecture 7 (PDF, 7 MB)
Student Presentations
Lecture 8 (PDF, 9.3 MB)
28.11. Lecture 9 (PDF, 8.9 MB)
05.12. Student Presentations
12.12. Student Presentations
19.12. Final exam

Further Readings

You may access three collections of further readings here or download specific readings directly from the list of papers ordered by topic below:


D. Helbing (2010) Quantitative Sociodynamics. Stochastic Methods and Models of Social Interaction Processes (Springer, Berlin).
D. Helbing (2001) Traffic and related self-driven many-particle systems Reviews of Modern Physics 73, 1067-1141.
D. Helbing, A. Johansson (2010) Pedestrian, Crowd and Evacuation Dynamics Encyclopedia of Complexity and Systems Science 16, 6476-6495.
D. Helbing and K. Nagel (2004) The physics of traffic and regional development Contemporary Physics 45(5), 405-426.
D. Helbing (2004) Dynamic decision behavior and optimal guidance through information services: Models and experiments Pages 47-95 in: M. Schreckenberg and R. Selten (eds.) Human Behaviour and Traffic Networks (Springer, Berlin).
D. Helbing (2013) Globally networked risks and how to respond Nature 497, 51–59.
D. Helbing, H. Ammoser, and C. Kühnert (2005) Disasters as extreme events and the importance of network interactions for disaster response management Pages 319-348. in: S. Albeverio, V. Jentsch, and H. Kantz (eds.) Extreme Events in Nature and Society (Springer, Berlin).
D. Helbing and S. Lämmer (2005) Supply and production networks: From the bullwhip effect to business cycles Page 33–66 in: D. Armbruster, A. S. Mikhailov, and K. Kaneko (eds.) Networks of Interacting Machines: Production Organization in Complex Industrial Systems and Biological Cells (World Scientific, Singapore).

Pedestrians and Crowds

M. Moussaïd, D. Helbing, and G. Theraulaz (2011) How simple rules determine pedestrian behavior and crowd disasters PNAS 108 (17) 6884-6888.
D. Helbing, A. Johansson, J. Mathiesen, M.H. Jensen, and A. Hansen (2006) Analytical approach to continuous and intermittent bottleneck flows Physical Review Letters 97, 168001.
D. Helbing, R. Jiang, and M. Treiber (2005) Analytical investigation of oscillations in intersecting flows of pedestrian and vehicle traffic Physical Review E 72, 046130.
D. Helbing and T. Vicsek (1999) Optimal self-organization New Journal of Physics 1, 13.1-13.17.
D. Helbing, F. Schweitzer, J. Keltsch, and P. Molnár (1997) Active walker model for the formation of human and animal trail systems Physical Review E 56, 2527-2539.
H. Löwen (2010) Particle-resolved instabilities in colloidal dispersions Soft Matter 6, 3133-3142
J. Dzubiella and H. Löwen (2002) Pattern formation in driven colloidal mixtures: tilted driving forces and re-entrant crystal freezing J. Phys.: Condens. Matter 14, 9383-9395.
K. Aoki (2000) Mathematical model of a saline oscillator Physica D 147, 187-203 or N. Okamura and K. Yoshikawa (2000) Rhythm in a saline oscillator. Physical Review E 61, 2445-2452.

Vehicular Traffic

D. Helbing (2009) Derivation of non-local macroscopic traffic equations and consistent traffic pressures from microscopic car-following models European Physical Journal B 69(4), 539-548.
D. Helbing and A. Johansson (2009) On the controversy around Daganzo’s requiem for and Aw-Rascle’s resurrection of second-order traffic flow models European Physical Journal B 69(4), 549-562.
D. Helbing and M. Moussaid (2009) Analytical calculation of critical perturbation amplitudes and critical densities by non-linear stability analysis of a simple traffic flow model European Physical Journal B 69(4), 571-581.
D. Helbing, M. Treiber, A. Kesting, and M. Schönhof (2009) Theoretical vs. empirical classification and prediction of congested traffic states European Physical Journal B 69(4), 583-598.
D. Helbing and B. Tilch (2009) A power law for the duration of high-flow states and its interpretation from a heterogeneous traffic flow perspective European Physical Journal B 68(4), 577-586.
M. Treiber and D. Helbing (2009) Hamilton-like statistics in onedimensional driven dissipative many-particle systems European Physical Journal B 68(4), 607-618.
D. Helbing (2009) Derivation of a fundamental diagram for urban traffic flow European Physical Journal B 70(2), 229-241.
D. Helbing and A. Mazloumian (2009) Operation regimes and slower-is-faster effect in the control of traffic intersections European Physical Journal B 70(2), 257–274.
S. Lämmer and D. Helbing (2008) Self-control of traffic lights and vehicle flows in urban road networks JSTAT P04019
S. Lämmer and D. Helbing (2010) Self-stabilizing decentralized signal control of realistic, saturated network traffic, SFI Working Paper 2010-09-019, see
M. Treiber, A. Kesting, and D. Helbing (2007) Influence of reaction times and anticipation on stability of vehicular traffic flow Transportation Research Record 1999, 23-29.
D. Helbing, J. Siegmeier, and S. Lämmer (2007) Self-organized network flows Networks and Heterogeneous Media 2(2), 193-210.
M. Krbalek and D. Helbing (2004) Determination of interaction potentials in freeway traffic from steady-state statistics Physica A 333, 370-378.
D. Helbing (2003) A section-based queueing-theoretical traffic model for congestion and travel time analysis in networks Journal of Physics A: Mathematical and General 36, L593-L598.
R. Kölbl and D. Helbing (2003) Energy laws in human travel behaviour New Journal of Physics 5, 48.1-48.12.
V. Shvetsov and D. Helbing (1999) Macroscopic dynamics of multilane traffic Physical Review E 59, 6328-6339.
M. Treiber, A. Hennecke, and D. Helbing (1999) Derivation, properties, and simulation of a gas-kinetic-based, non-local traffic model Physical Review E 59, 239-253.
D. Helbing and M. Schreckenberg (1999) Cellular automata simulating experimental properties of traffic flows Physical Review E 59, R2505-R2508.
D. Helbing, A. Hennecke, and M. Treiber (1999) Phase diagram of traffic states in the presence of inhomogeneities Physical Review Letters 82, 4360-4363.
D. Helbing (1996) Gas-kinetic derivation of Navier-Stokes-like traffic equations Physical Review E 53, 2366-2381.

Game Theory

T. Grund, C. Waloszek and D. Helbing (2013) How natural selection can create both self- and other-regarding preferences, and networked minds Scientific Reports, 2, 1480.
C. P. Roca and D. Helbing (2011) Emergence of social cohesion in a model society of greedy, mobile individuals Proceedings of the National Academy of Sciences USA (PNAS) 108(28), 11370-11374.
D. Helbing and A. Johansson (2010) Cooperation, norms, and revolutions: A unified game-theoretical approach PLoS ONE 5(10), e12530. D. Helbing and A. Johansson (2010) Evolutionary dynamics of populations with conflicting interactions: Classification and analytical treatment considering asymmetry and power Physical Review E 81, 016112.
D. Helbing, A. Szolnoki, M. Perc, and G. Szabó (2010) Evolutionary establishment of moral and double moral standards through spatial interactions PLoS Computational Biology 6(4), e1000758.
D. Helbing and S. Lozano (2010) Phase transitions to cooperation in the prisoner's dilemma Physical Review E 81(5), 057102.
D. Helbing and W. Yu (2009) The outbreak of cooperation among success-driven individuals under noisy conditions Proceedings of the National Academy of Sciences USA (PNAS) 106(8), 3680-3685.
D. Helbing and W. Yu (2008) Migration as a mechanism to promote cooperation Advances in Complex Systems 11(4), 641-652.
D. Helbing, M. Schönhof, H.-U. Stark, and J. A. Holyst (2005) How individuals learn to take turns: Emergence of alternating cooperation in a congestion game and the prisoner's dilemma Advances in Complex Systems 8, 87-116.
D. Helbing and T. Platkowski (2000) Self-organization in space and induced by fluctuations International Journal of Chaos Theory and Applications 5(4), 47-62.
D. Helbing and T. Platkowski (2002) Drift- or fluctuation-induced ordering and self-organisation in driven many-particle systems Europhysics Letters (EPL) 60, 227-233.
D. Helbing, M. Treiber, and N. J. Saam (2005) Analytical investigation of innovation dynamics considering stochasticity in the evaluation of fitness Physical Review E 71, 067101.
D. Helbing (1994) A mathematical model for the behavior of individuals in a social field Journal of Mathematical Sociology 19(3), 189-219.
D. Helbing (1993) Boltzmann-like and Boltzmann-Fokker-Planck equations as a foundation of behavioral models Physica A 196, 546-573.
D. Helbing (1996) A stochastic behavioral model and a `microscopic' foundation of evolutionary game theory Theory and Decision 40, 149-179.
D. Helbing (1993) Stochastic and Boltzmann-like models for behavioral changes, and their relation to game theory Physica A 193, 241-258.

Disaster Spreading and Recovery Management

G. Tedeschi, A. Mazloumian, M. Gallegati and D. Helbing (2012) Bankruptcy cascades in interbank markets PLoS ONE 7(12): e52749.
I. Simonsen, L. Buzna, K. Peters, S. Bornholdt and D. Helbing (2008) Transient dynamics increasing network vulnerability to cascading failures Physical Review Letters 100, 218701.
K. Peters, L. Buzna, and D. Helbing (2008) Modelling of cascading effects and efficient response to disaster spreading in complex networks
Int. J. Critical Infrastructures 4(1/2), 46-62.
L. Buzna, K. Peters, H. Ammoser, C. Kühnert, and D. Helbing (2007) Efficient response to cascading disaster spreading Physical Review E 75, 056107.
D. Helbing, U. Witt, S. Lämmer, and T. Brenner (2004) Network-induced oscillatory behavior in material flow networks and irregular business cycles Physical Review E 70, 056118.
D. Helbing, S. Lämmer, T. Seidel, P. Seba, and T. Platkowski (2004) Physics, stability and dynamics of supply networks Physical Review E 70, 066116.
D. Helbing and C. Kühnert (2003) Assessing interaction networks with applications to catastrophe dynamics and disaster management Physica A 328, 584-606.
A. Krawiecki, J. A. Holyst, and D. Helbing (2002) Volatility clustering and scaling for financial time series due to attractor bubbling Physical Review Letters 89, 158701.

Network science

S. V. Buldyrev, R. Parshani, G.Paul, H. E. Stanley and S. Havlin (2010), Catastrophic cascade of failures in interdependent networksNature 464, 1025-1028
B. Barzel and A.-L. Barabasi (2013), Universality in network dynamics, Nature Physics 9, 673–681
A. R. Benson, D. F. Gleich, and J. Leskovec (2016), Higher-order organization of complex networks, Science Vol. 353, Issue 6295, pp. 163-166
N. Antulov-Fantulin, A. Lancic, T. Smuc, H. Stefancic and M. Sikic (2015), Identification of Patient Zero in Static and Temporal Networks - Robustness and Limitations, Phy. Rev. Lett. 114, 248701
D. Brockmann and D. Helbing (2013), The Hidden Geometry of Complex, Network-Driven Contagion Phenomena, Science Vol. 342, Issue 6164, pp. 1337-1342
Y.-Y. Liu, J.-J. Slotine and A.-L. Barabasi (2011), Controllability of complex networks, Nature 473, 167–173
F. Morone and H. A. Makse (2015), Influence maximization in complex networks through optimal percolation, Nature 524, 65–68J. P. Gleeson (2013), Binary-State Dynamics on Complex Networks: Pair Approximation and Beyond, Phys. Rev. X 3, 021004
A. Vespignani (2012), Modeling Dynamical Processes in Complex Socio-technical Systems, Nature Physics 8, 32-39

Page URL:
Mon Feb 20 05:01:02 CET 2017
© 2017 Eidgenössische Technische Hochschule Zürich