| | Dirk Helbing, Professor of Computational Social Science
Dirk Helbing (ETH Zurich)
Complexity and Global Systems Science
| |
Literature
§ Ball: Why Society Is A Complex Matter § Helbing: Social Self-Organization § Helbing: Managing Complexity § Colander/Kupers: Complexity and the Art of Public Policy § Mitchell: Complexity § Buckley: Society – A Complex Adaptive System § Castellani/Hafferty: Sociology and Complexity Science § Mikhailov/Calenbuhr: From Cells to Society § Mainzer: Thinking in Complexity § Sawyer: Social Emergence § Books published by the Santa Fe Institute Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
Old Problems: External Threats to Society
| |
New Problems: Systemic Instabilities
Examples: 1. Financial, economic and
debt crisis 2. Social and political
instabilities 3. Environmental and
climate change 4. Organized crime,
cybercrime 5. Quick spreading of
emerging diseases © 2010 Paresh Nath We must realize that we are living in a new world.
Dirk Helbing, Professor of Computational Social Science
| |
We Can‘t Anymore Do Business As Usual
“Our financial, transportation, health system are broken.” Sandy Pentland, MIT Media Lab
“We are seeing an extraordinary failure of our current political and economic system.” Geoffrey West, former president of the Santa Fe Institute Dirk Helbing, Professor of Computational Social Science
| |
1. Financial crisis: Losses of 2-20 trillion $ 2. Conflict: Global military expenditures amount to
1.5 trillion $ annually. 3. Terrorism: 9/11 attacks caused 90 billion $ lost
output of the US economy 4. Crime and corruption: 2-5% of GDP, about 2
trillion $ annually. 5. Flu: A true influenza pandemic infecting 1% of
world population would cause losses of 1-2 trillion $ per annum
6. Cybercrime: 750 billion EUR/a in Europe
Collatoral Costs and New Opportunities
§ Even a 1% improvement would create
benefits many times higher than project investments
§ For comparison: Weather forecasts cost 10 CHF/citizen, create a 5 times higher benefit
§ Business opportunities: Facebook’s value, for example, amounts to 60-80 billion $
Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
A Daunting Class of Problems § Large number of interacting system elements
(individuals, companies, countries, cars…)
§ Non-linear or network interactions § Rich system behavior
§ Dynamic rather than static
§ Probabilistic rather than deterministic § Surprising, often paradoxical system behavior
(e.g. slower-is-faster effects) § Hardly predictable
§ Seemingly uncontrollable
§ Challenge our common way of thinking § Almost everywhere around us
§ Currently a nightmare for decision-makers
| | Dirk Helbing, Professor of Computational Social Science
Complicated vs. Complex Systems § Example: A car is a complicated system
§ Example: Traffic flows, involving the interaction of many cars, constitute a complex system
§ Phantom traffic jams, many different kinds of congestion patterns
| |
Lane Formation in Pedestrian Counterflows
| |
Crowd Turbulence During the Love Parade
| | Dirk Helbing, Professor of Computational Social Science
§ Large number of non-linearly interacting system components leads to complex dynamics
§ Example: Weather forecast § Chaotic dynamics/butterfly effect/
sensitivity: Small initial differences can cause very different behavior
Limits of Predictability
Source: J. D. Murray
| | Dirk Helbing, Professor of Computational Social Science
Complex Systems: Limits of Predictability
| |
Counter-Intuitive Behavior of Complex Systems
Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
§ Big changes may have small, no, adverse or unexpected effects § Irreducible randomness: A degree of uncertainty and perturbation
that cannot be eliminated § Principle of Le Chatelier/Goodhart’s law: A system tends to
counteract external control attempts § Delays may cause instabilities § Regime shifts („phase transitions“,
catastrophes): Sometimes small changes have a big impact
§ Unknown unknowns, structural instability
„Cusp catastrophe“
The Illusion of Control
| | Dirk Helbing, Professor of Computational Social Science 15
212022222
2
211011111
1
)(
)(
xxxxcxkdtdx
xxxxcxkdtdx
λ
λ
+−−−=
−−−=
The Lotka-Volterra equations are used to describe, for example, interactions in ecological systems
x1: number of species 1 x2: number of species 2 x0
1,2: attraction point k1,2, c1,2, λ1,2: parameters
The Challenge to Manage Complex Systems
| |
Instability of Traffic Flow: Stop-and-Go Waves
| |
Self-Organized Traffic in Hanoi
Dirk Helbing, Professor of Computational Social Science
| |
Self-Organization as Alternative and Success Principle Self-Organizing Traffic Flow in Kairo
| |
Unstable Dynamics
in Complex Systems
Dirk Helbing, Professor of Computational Social Science
| |
Thanks to "Yuki Sugiyama"
At high densities, free traffic flow is unstable:"Despite best efforts, drivers fail to maintain speed"
Capacity drop, when capacity
is most needed!"
Phantom Traffic Jams
| |
Perturbations in demand amplify"
Unstable Supply Chains
| |
| |
| |
| |
| |
Input output matrix! Related delivery network! Resulting oscillations in the gross domestic product!
" """""""""""""""""" "
Economic Instability: Booms and Recessions
| |
Network structure Commodity flow (average of FRA, GER, JAP, UK, USA)
D. H., U. Witt, S. Lämmer, T. Brenner, Physical Review E 70, 056118 (2004).
Global Logistic Networks: Recessions Are Like Traffic Jams of the Economy
Dirk Helbing, Professor of Computational Social Science
| |
At low densities:"self-organized lane formation, "like Adam Smith’s invisible hand" At large densities: coordination breaks down"
Love Parade Disaster in Duisburg, 2010"
Crowd Disasters
| |
Spreading of Crime
| |
Social Dilemma Problem"- Global Warming"- (Financial Crisis)"- Free-Riding"- Tax Evasion"- Environmental Pollution"- Environmental Exploitation"- Overfishing"
Tragedies of the Commons
| |
Intifada"(civil war)"
Armed Conflict
| |
World GNP and fertility
0
5.000
10.000
15.000
20.000
25.000
30.000
35.000
0 1 2 3 4 5 6 7 8fertility f, children per women
GN
P pe
r pe
rson
in U
S $
hierarchies
transition
democracies
transition line
democracies
hierarchies
Source: Jürgen Mimkes"
Revolutions
| |
Financial Meltdowns
The flash crash turned solid assets into penny stocks within minutes. Was an interaction effect, no criminal act, ‘fat finger’, or error.
The Flash Crash on May 6, 2010 evaporated 600 billion dollars in 20 minutes
Dirk Helbing, Professor of Computational Social Science
| | Source: Dirk Brockmann
Epidemic Spreading
| |
Many unsolved problems of the world result from
systemic instabilities"
| |
Cascade Effects
in Complex Networks
Dirk Helbing, Professor of Computational Social Science
| |
How the Interplay of Risk and Complexity Creates Uncertainty
Dirk Helbing, Professor of Computational Social Science
| |
Diffusion of Walmart
Dirk Helbing, Professor of Computational Social Science
| |
Strongly Coupled and Complex System Behave Fundamentally Different
1. Faster dynamics 2. Increased frequency of extreme
events – can have any size 3. Self-organization dominates
system dynamics 4. Emergent and counterintuitive
system behavior, unwanted feedback, cascade and side effects
5. Predictability goes down 6. External control is difficult 7. Larger vulnerability
Change of perspective (from a component- to an interaction-oriented view) will reveal new solutions! Need a science of multi-level complex systems!
Dirk Helbing, Professor of Computational Social Science
| |
Mousetrap fission, by Gerhard G. Paulus, University of Jena, https://www.youtube.com/watch?v=Wiz1VVLYgl4
Loss of Control through Cascade Effects
| |
EU project IRRIIS: E. Liuf (2007) Critical Infrastructure protection, R&D view
Failure in the continental European electricity grid on November 4, 2006
Cascade Effect and Blackout in the European Power Grid
Dirk Helbing, Professor of Computational Social Science
| |
Video by Frank Schweitzer et al. Dirk Helbing, Professor of Computational Social Science
Cascading Effects During Financial Crises
| |
How the Banking Network Changed
From: Haldane
Dirk Helbing, Professor of Computational Social Science
| |
Different recipes, new solutions, and a paradigm shift in our understanding of the world are needed.
Too Much Networking Can Cause Self-Destabilization: Breakdown of Cooperation
Dirk Helbing, Professor of Computational Social Science
| |
Connection density (%)Perc
enta
ge o
f coo
pera
tion
(%)
0.00 0.05 0.10 0.15 0.20 0.25 0.300.0
0.2
0.4
0.6
0.8
1.0
Too Much Connectivity Can Be Bad
Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
Explosive Epidemic Spreading with Budget-Constrained Recovery
| |
Complex Interdependencies
Dirk Helbing, Professor of Computational Social Science
| |
We now have a global exchange of people, money, goods, information, ideas…
Network interdependencies create pathways for disaster spreading! Need adaptive decoupling strategies.
Globalization and technological change have created a strongly coupled and interdependent world
Risk Interconnection Map World Economic Forum
Networking Is Good, But Promotes Cascade Effects
Dirk Helbing, Professor of Computational Social Science
| |
Networking is Good … But Promotes Cascading Effects
§ We now have a global exchange of people, money, goods, information, ideas…
§ Globalization and technological change have created a strongly coupled and interdependent world
Network infrastructures create pathways for disaster spreading! Need adaptive decoupling strategies.
Dirk Helbing, Professor of Computational Social Science
| |
blackout
gas stations out of order
traffic lights off
public and private transport down
pumps without power
use of candles
wired phone network busy
advisory to boil water
no electricity to boil water
service disruptions of cellular phones
firefighting required
Causality Network for the Blackout in North America 2003 (Detail)
Dirk Helbing, Professor of Computational Social Science
| |
Secondary and Tertiary Disasters
Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
Causality Network for Earthquakes
| |
Interdependencies in Energy Supply
Dirk Helbing, Professor of Computational Social Science
| |
Nuclear Power
Dirk Helbing, Professor of Computational Social Science
| |
Solar Power
Dirk Helbing, Professor of Computational Social Science
| |
Gas Supply
Dirk Helbing, Professor of Computational Social Science
| |
Biofuels
Dirk Helbing, Professor of Computational Social Science
| |
Food Prices and Social Unrests
Dirk Helbing, Professor of Computational Social Science
| |
Arab Spring
Dirk Helbing, Professor of Computational Social Science
| |
The Arab Spring
Dirk Helbing, Professor of Computational Social Science
| |
transition
democracy
hierarchy
Transition from hierarchies to democracies (source: Jürgen Mimkes)
World GNP and fertility
0
5 . 0 0 0
1 0 . 0 0 0
1 5 . 0 0 0
2 0 . 0 0 0
2 5 . 0 0 0
3 0 . 0 0 0
3 5 . 0 0 0
0 1 2 3 4 5 6 7 8
fe rt i li t y f, c h i ld re n p e r w o m e n
GN
P p
er
pe
rso
n i
n U
S $
h iera rc hies
tran sition
d em oc ra cies
tran sition lin e
de m o c rac i es
h ie ra rc h ies
Political Cascading Effects
Dirk Helbing, Professor of Computational Social Science
| |
Could Europe’s History Be Simulated?
Dirk Helbing, Professor of Computational Social Science
| |
Visualizing Empires Decline
Dirk Helbing, Professor of Computational Social Science
| | http://www.sueddeutsche.de/politik/streit-beigelegt-einigung-ueber-foederalismusreform-1.413140"
Source: ddp images/Marcus Brandt"
The Problems of the World Are Complex"
| |
Cascading Effects During Financial Crises"
Social Dilemma Problem"- Global Warming"- (Financial Crisis)"- Free-Riding"- Tax Evasion"- Environmental Pollution"- Environmental Exploitation"- Overfishing"
Climate Change"
| |
Financial Crisis"
| |
War in Ukraine"
| |
"
PERSPECTIVEdoi:10.1038/nature12047
Globally networked risks and howto respondDirk Helbing1,2
Today’s strongly connected, global networks have produced highly interdependent systems that we do not understandand cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even whenexternal shocks are absent. As the complexity and interaction strengths in our networked world increase, man-madesystems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have alldata and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign isneeded. A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking.
G lobalization and technological revolutions are changing our pla-net. Today we have a worldwide exchange of people, goods,money, information, and ideas, which has produced many new
opportunities, services and benefits for humanity. At the same time,however, the underlying networks have created pathways along whichdangerous and damaging events can spread rapidly and globally. This hasincreased systemic risks1 (see Box 1). The related societal costs are huge.
When analysing today’s environmental, health and financial systemsor our supply chains and information and communication systems, onefinds that these systems have become vulnerable on a planetary scale.They are challenged by the disruptive influences of global warming,disease outbreaks, food (distribution) shortages, financial crashes, heavysolar storms, organized (cyber-)crime, or cyberwar. Our world is alreadyfacing some of the consequences: global problems such as fiscal andeconomic crises, global migration, and an explosive mix of incompatibleinterests and cultures, coming along with social unrests, internationaland civil wars, and global terrorism.
In this Perspective, I argue that systemic failures and extreme events areconsequences of the highly interconnected systems and networked riskshumans have created. When networks are interdependent2,3, this makesthem even more vulnerable to abrupt failures4–6. Such interdependenciesin our ‘‘hyper-connected world’’1 establish ‘‘hyper-risks’’ (see Fig. 1). Forexample, today’s quick spreading of emergent epidemics is largely a resultof global air traffic, and may have serious impacts on our global health,social and economic systems6–9. I also argue that initially beneficialtrends such as globalization, increasing network densities, sparse use ofresources, higher complexity, and an acceleration of institutional decisionprocesses may ultimately push our anthropogenic (man-made or human-influenced) systems10 towards systemic instability—a state in which thingswill inevitably get out of control sooner or later.
Many disasters in anthropogenic systems should not be seen as ‘bad luck’,but as the results of inappropriate interactions and institutional settings. Evenworse, they are often the consequences of a wrong understanding due to thecounter-intuitive nature of the underlying system behaviour. Hence, conven-tional thinking can cause fateful decisions and the repetition of previousmistakes. This calls for a paradigm shift in thinking: systemic instabilitiescan be understood by a change in perspective from a component-oriented toan interaction- and network-oriented view. This also implies a fundamentalchange in the design and management of complex dynamical systems.
The FuturICT community11 (see http://www.futurict.eu), which involvesthousands of scientists worldwide, is now engaged in establishing a
‘Global Systems Science’, in order to understand better our informationsociety with its close co-evolution of information and communicationtechnology (ICT) and society. This effort is allied with the ‘‘Earth systemscience’’10 that now provides the prevailing approach to studying thephysics, chemistry and biology of our planet. Global Systems Sciencewants to make the theory of complex systems applicable to the solutionof global-scale problems. It will take a massively data-driven approachthat builds on a serious collaboration between the natural, engineering,and social sciences, aiming at a grand integration of knowledge. Thisapproach to real-life techno-socio-economic-environmental systems8 isexpected to enable new response strategies to a number of twenty-firstcentury challenges.
1ETH Zurich, Clausiusstrasse 50, 8092 Zurich, Switzerland. 2Risk Center, ETH Zurich, Swiss Federal Institute of Technology, Scheuchzerstrasse 7, 8092 Zurich, Switzerland.
BOX 1
Risk, systemic risk and hyper-riskAccording to the standard ISO 31000 (2009; http://www.iso.org/iso/catalogue_detail?csnumber543170), risk is defined as ‘‘effect ofuncertainty on objectives’’. It is often quantified as the probability ofoccurrence of an (adverse) event, times its (negative) impact(damage), but it should be kept in mind that risks might also createpositive impacts, such as opportunities for some stakeholders.
Compared to this, systemic risk is the risk of having not juststatistically independent failures, but interdependent, so-called‘cascading’ failures in a network of N interconnected systemcomponents. That is, systemic risks result from connections betweenrisks (‘networked risks’). In such cases, a localized initial failure(‘perturbation’) could have disastrous effects and cause, in principle,unbounded damage as N goes to infinity. For example, a large-scalepower blackout can hit millions of people. In economics, a systemicrisk could mean the possible collapse of a market or of the wholefinancial system. The potential damage here is largely determined bythe size N of the networked system.
Even higher risks are implied by networks of networks4,5, that is, bythe coupling of different kinds of systems. In fact, new vulnerabilitiesresult from the increasing interdependencies between our energy,food and water systems, global supply chains, communication andfinancial systems, ecosystems and climate10. The World EconomicForum has described this situation as a hyper-connected world1, andwe therefore refer to the associated risks as ‘hyper-risks’.
2 M A Y 2 0 1 3 | V O L 4 9 7 | N A T U R E | 5 1
Macmillan Publishers Limited. All rights reserved©2013
Global Systems Science
| |
Humans have created tightly connected systems and networked risks, which has led to a world we do not understand and cannot control well. Systemic risks and extreme events are consequences of this. " "However, systemic instabilities can be understood by a change in perspective from a component-oriented to an interaction- and network-oriented view. This also entails a fundamental change in the design and management of complex dynamical systems. Establishing a "Global Systems Science" will allow us to better understand our information society with its close co-evolution of information and communication technology (ICT) and society. This effort is allied with the "earth system science" that now provides the prevailing approach to studying the physics, chemistry and biology of our planet." "Global Systems Science makes current theories of crises and disasters applicable to the solution of global-scale problems, taking a massively data-driven approach that builds on a serious collaboration between the natural, engineering, and social sciences, i.e. a grand integration of knowledge."
Global Systems Science
| |
Global Participatory
Platform
Living Earth
Simulator create new technology provide data
Pluralistic Reputation
System
Planetary Nervous System
Create systems to sense & understand
Turn data into information
What is?
Develop models to simulate &
predict
Turn information into knowledge
What if?
Build Platforms to Explore & Interact
Turn knowledge into wisdom What for?
Dirk Helbing, Professor of Computational Social Science
| | Dirk Helbing, Professor of Computational Social Science
§ Complex systems are difficult to understand, predict, and control"
§ But they tend to self-organize emergent structures, properties, and functions"
§ The interactions within the system determine the outcome of self-organization"
§ With the right kinds of interaction rules, everything works wonderfully and efficiently (“invisible hand”)"
§ How to find suitable interaction rules? (computer simulations, experiments, interactive virtual worlds, exploratories)"
§ How to change them? (real-time data and real-time feedback)"
§ How to gain these data? (Nervousnet)"§ How to produce the feedbacks? (multi-
dimensional finance/value exchange)" multi-dimensional real-time feedback"
real-time measurement"
How to Make Complex Systems Work
| | (Image: dmd/ADAC)
| |
| |
Any Questions?
Dirk Helbing, Professor of Computational Social Science