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The Automation of Society is Next: How to Survive the Digital Revolution

The Automation of
Society is Next
How to Survive the Digital Revolution
Version 1.0
Copyright © 2015 Dirk Helbing
All rights reserved.
I would like to dedicate this book to Dietmar Huber
for the incredible support he has given to me over so many years.
A better future or worse?
When systems get out of control
Revealing the causes of success and disaster
The dangerous promise of Big Data
Major socio-economic shifts ahead
Making the invisible hand work
Social order by self-organization
Where human evolution is heading
A participatory market society is born
Taking the future in our hands
I would like to thank the FuturICT community for the many inspiring
discussions and everyone, who was patient with me, including
my parents and whoever might have reasons for complaints.
I am also very grateful to Philip Ball, Stefano Bennati, Anna Carbone,
Andreas Diekmann, Dietmar Huber, Eoin Jones, Caleb Koch, Richard
Mann, Heinrich Nax, Paul Ormerod, Evangelos Pournaras, Kay-Ti Tan,
and others for their valuable feedback on the manuscript
and the many improvements (but don't hold them responsible
for any contents of this book). Petr Neugebauer and Petra Parikova
have been a great help with figures and formatting.
Dirk Helbing is one of the most imaginative experts in the world when it
comes to envisioning the opportunities and risks of the digital revolution.
He is an advocate of responsible innovation and strongly contributed to the
public debate around Big Data. He also coordinates the FuturICT initiative,
which built a global interdisciplinary community of experts at the interface
of complexity, computer and data science. These activities, aimed at
confronting global problems and crises were featured by Scientific
American as the number one world-changing idea and earned him an
honorary doctorate from TU Delft, where he is now an affiliate professor.
Dirk Helbing is Professor of Computational Social Science at the
Department of Humanities, Social and Political Sciences and also affiliated
to the Computer Science Department at ETH Zurich. He has a PhD in
physics, was Managing Director of the Institute of Transport & Economics
at Dresden University of Technology in Germany, and Professor of
Sociology at ETH Zurich.
Helbing is an elected member of the German Academy of Sciences
"Leopoldina" and worked for the World Economic Forum's Global
Agenda Council on Complex Systems. He is also co-founder of the Physics
of Socio-Economic Systems Division of the German Physical Society and
ETH Zurich's Risk Center. Furthermore, he is a board member of the
Global Brain Institute in Brussels and the International Centre for Earth
Simulation in Geneva. In addition, he is a member of various high-level
committees to assess the implications of the digital revolution.
The motivation for his research may be summarized by "What can
complexity science and information systems contribute to saving human
lives?" This ranges from avoiding crowd disasters over reducing crime and
conflict to the reduction of epidemic spreading. His work brings theoretical
studies, data analytics, and lab experiments together with agent-based
computer models, where agents might have cognitive features. His recent
publication on globally networked risks establishes the framework of a
Global Systems Science.
Using the emergent "Internet of Things", his team is now engaged in
establishing the core of a decentralized Planetary Nervous System as a
Citizen Web (see This will be an open, transparent and
participatory information platform to support real-time measurements of
our world, situational awareness, successful decision-making, and self-
organization. The goal of this system is to open up the new opportunities of
the digital age for everyone.
Photo: Sabina Bobst
"We hold these truths to be self-evident, that all men are created equal, that they are
endowed by their Creator with certain unalienable Rights, that among these are Life,
Liberty and the pursuit of Happiness."
The United States Declaration of Independence
after Thomas Jefferson
"Those who surrender freedom for security will not have, nor do they deserve, either one."
Benjamin Franklin
February 23, 2012
"Americans have always cherished our privacy. From the birth of our republic, we
assured ourselves protection against unlawful intrusion into our homes and our personal
papers. At the same time, we set up a postal system to enable citizens all over the new
nation to engage in commerce and political discourse. Soon after, Congress made it a crime
to invade the privacy of the mails. And later we extended privacy protections to new
modes of communications such as the telephone, the computer, and eventually email.
Justice Brandeis taught us that privacy is the "right to be let alone," but we also
know that privacy is about much more than just solitude or secrecy. Citizens who feel
protected from misuse of their personal information feel free to engage in commerce, to
participate in the political process, or to seek needed health care. This is why we have laws
that protect financial privacy and health privacy, and that protect consumers against
unfair and deceptive uses of their information. This is why the Supreme Court has
protected anonymous political speech, the same right exercised by the pamphleteers of the
early Republic and today's bloggers.
Never has privacy been more important than today, in the age of the Internet, the
World Wide Web and smart phones. In just the last decade, the Internet has enabled a
renewal of direct political engagement by citizens around the globe and an explosion of
commerce and innovation creating jobs of the future. Much of this innovation is enabled
by novel uses of personal information. So, it is incumbent on us to do what we have done
throughout history: apply our timeless privacy values to the new technologies and
circumstances of our times.
I am pleased to present this new Consumer Privacy Bill of Rights as a blueprint for
privacy in the information age. These rights give consumers clear guidance on what they
should expect from those who handle their personal information, and set expectations for
companies that use personal data. I call on these companies to begin immediately working
with privacy advocates, consumer protection enforcement agencies, and others to implement
these principles in enforceable codes of conduct. My Administration will work to advance
these principles and work with Congress to put them into law. With this Consumer
Privacy Bill of Rights, we offer to the world a dynamic model of how to offer strong
privacy protection and enable ongoing innovation in new information technologies.
One thing should be clear, even though we live in a world in which we share personal
information more freely than in the past, we must reject the conclusion that privacy is an
outmoded value. It has been at the heart of our democracy from its inception, and we need
it now more than ever..."
Barack Obama, US Consumer Privacy Bill
of Rights
A better future or worse?
Smartphones, tablets and app stores with almost unlimited possibilities have become
symbols of the digital revolution. However, while these innovations make our lives more
comfortable and interesting, they herald a much more fundamental transformation.
Advances in digital technology now affect the way we learn, decide, and interact. By
harnessing "Big Data", the "Internet of Things", and Artificial Intelligence (AI), we
can create smart homes and smart cities. But this is only the tip of the iceberg our entire
economy and society will also dramatically change. What are the opportunities and risks
related to this? Are we heading towards digital slavery or freedom? What forces are at
work and how can we use them to create a smarter society? This book offers a guided tour
through the new, digital age ahead.
After the automation of factories and the creation of self-driving cars,
the automation of society is next. While we were busy with our
smartphones, the world has secretly changed behind our backs. In fact, our
world is changing with increasing speed, and much of that change is being
driven by developments in Information and Communications Technology
(ICT). These technologies, such as laptop computers, mobile phones,
tablets and smart watches, seemed to be about convenience. They came
along and enabled us to calculate, communicate and archive with greater
speed and efficiency than ever before. However, there was very little
recognition that, one day, they would not only facilitate our cultural
discourse and institutions, but also reshape our entire world. Large-scale
mass surveillance, the global spread of Uber taxis and the BitCoin crypto-
currency are just a few of the irritating symptoms of the digital era to come.
Living in the age of "Big Data"
Suddenly, there is also a great hype about "Big Data". No wonder Dan
Ariely compared the frenzy about Big Data with teenage sex: "everyone talks
about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so
everyone claims they are doing it..."
But some are actually doing it. In fact, "Big Data" has already given rise
to many interesting applications, such as real-time language translation. So,
what is "Big Data"? The term refers to massive amounts of data, which
have been collected about technological, social, economic and
environmental systems and activities. To get an idea of "Big Data," imagine
the digital traces that almost all our activities leave, including the data
created by our consumption and movement patterns. Every single minute,
we produce about 700,000 Google queries and 500,000 Facebook comments.
If you add all of the location data of people using smartphones, the
purchasing data of people who buy things, and cookies which track every
click and tap of our online activity, you will begin to comprehend the
enormity of "Big Data".
All the contents collected in the history of humankind until the year
2003 are estimated to amount to five billion gigabytes the data volume
that is now produced approximately every day. While we have been
speaking of an "information age" since the middle of last century, the digital
era started only in 2002. Since then, the digital storage capacity has
exceeded the analog one. Today, more than 95 percent of all data are
available in digital form. Even by avoiding credit card transactions, social
media and digital technologies, it is no longer possible to completely avoid
digital footprints on the Internet.
Data sets bigger than the largest library
The availability of Big Data about almost every aspect of our lives,
institutions and cultures has fueled the hope that we could now solve the
world's problems. Every Internet purchase we make generates data about
our preferences, finances and location that will be stored on a server
somewhere and used for various purposes, possibly without our consent.
Cell phones disclose where we are, and private messages and conversations
are being analyzed. It will probably not be long before every newborn baby
is genome-sequenced at birth. Books are being digitized and collated in
immense, searchable databases of words that are being data-mined to
enable "culturomics", a field which puts history, society, art and cultural
trends under the lens. Aggregated data can be used to reveal unexpected
facts in a way that would never have been possible before the digital age.
For example, an analysis of Google searches can reveal an impending flu
This avalanche of data continues to grow. The introduction of
technologies such as Google Glass encourages people to document and
archive almost every aspect of their lives. Further data sets include credit-
card transactions, communication data, Google Earth imagery, public news,
comments and blogs. These data sources have been termed "Big Data" and
are creating an increasingly accurate digital picture of our physical and social
world, as well as the global economy.
"Big Data" will certainly change our world. The term was coined more
than 15 years ago to describe data sets so big that they can no longer be
analyzed using standard computational methods. If we are to benefit from
Big Data, we must learn to "drill" and "refine" it into useful information
and knowledge. This is a significant challenge.
The tremendous increase in the volume of data is attributable to four
important technological innovations. First, the Internet enables global
communication between electronic devices. Second, the World Wide Web
(WWW) has created a network of globally accessible websites, which
emerged as a result of the invention of the Hypertext Transfer Protocol
(HTTP). Third, the emergence of social media platforms such as Facebook,
Google+, WhatsApp and Twitter has created social communication networks.
Finally, a wide range of previously offline devices such as TV sets, fridges,
coffee machines, cameras as well as sensors, smart wearable devices (such
as activity trackers) and machines are now connected to the Internet,
creating the "Internet of Things" (IoT) or "Internet of Everything" (IoE).
Meanwhile, the data sets collected by companies such as eBay, Walmart or
Facebook, must be measured in petabytes 1 million billion bytes. This
amounts to more than 100 times the information stored in the US Library
of Congress, which is the largest physical library in the world.
Mining Big Data offers the potential to create new ways to optimize
processes, identify interdependencies and make informed decisions.
However, Big Data also produces at least four major new challenges (the
"four V's"). First, the unprecedented volume of data means that we need
immense processing power and storage capacity to deal with the huge
amounts of data. Second, the velocity at which data must be processed has
increased: now, continuous data streams must often be analyzed in real-
time. Third, Big Data is mostly unstructured, and the resulting variety of data
is difficult to organize and analyze. Finally, the veracity of the data may be
difficult to handle because Big Data tends to contain errors and is usually
neither representative nor complete.
Will a digital revolution solve our problems?
Let us see what an evidence-based approach building on the wealth of
today's data can do for us. In the past, whenever a problem had to be
solved, the best course of action was to "ask the experts". These experts
would go to the library, collect up-to-date knowledge, and supervise PhD
students who would help to fill gaps in existing knowledge. But this was a
slow process. Nowadays, whenever people have a question, they ask Google
or consult Wikipedia, for example. This might not always give the definitive
or best answer, but it delivers quick answers. On average, decisions taken in
this way may even be better than many decisions made in the past. It is no
wonder, therefore, that policymakers love the Big Data approach, which
seems to provide immediate answers. Business people sensing the immense
commercial opportunities are getting excited too.
Big Data Gold rush for the 21st century's oil
The fact that we have much more information about our world than
ever before is both a blessing and a curse. The accumulation of socio-
economic data often implies a long-term intrusion into personal privacy and
raises a number of important issues. It cannot be denied that Big Data is a
powerful resource that supports evidence-based decision-making and that it
holds unprecedented potential for business, politics, science and citizens.
Recently, the social media portal WhatsApp was sold to Facebook for $19
billion, when it had 450 million users. This sale price implies that each
employee generated almost half a billion dollars in share value.
There is no doubt that Big Data creates tremendous opportunities, not
just because of its application in process optimization and marketing, but
also because the information itself is becoming monetized. As
demonstrated by the virtual currency BitCoin, it is now even possible to turn
bits into monetary value. It can be literally said that data can be mined into
money in a way that would previously have been considered a fairy tale. For
a time, BitCoins were even more valuable than gold.
Therefore, it is no surprise that many experts and technology gurus
claim that Big Data is the "oil of the 21st century", a new way of making
money big money. Although many Big Data sets are proprietary, the
consultancy company McKinsey recently estimated that the potential value of
Open Data alone is $3-5 trillions per year.
If the worth of this publicly
available information were to be evenly distributed among the world’s
population, every person on Earth would receive an additional $700 per
year. Therefore, the potential of Open Data significantly exceeds the value
of the international free trade and service agreements that are currently
under secret negotiation.
Given these numbers, are we currently setting the
right political and economic priorities? This is a question we must pay
attention to, because it will determine our future.
The potential of Big Data spans every area of social activity, from
processing human language and managing financial assets, to empowering
I recommend the readers to look up Wikipedia to inform themselves about the impending
international agreements coming under the abbreviations TTIP, CETA, TPP, and TISA. It
seems that these would dramatically increase the power of multi-national corporations.
Would this be good or bad?
cities to balance energy consumption and production. Big Data also holds
the promise of enabling us to better protect the environment, to detect and
reduce risks, and to discover opportunities that would otherwise have been
missed. In the area of personalized medicine, Big Data will probably make it
possible to tailor medications to patients in order to increase their
effectiveness and reduce their side effects. Big Data will also accelerate the
research and development of new drugs and focus resources on the areas of
greatest need.
It is clear, therefore, that the potential applications of Big Data are
various and rapidly spreading. While it will enable personalized services and
products, optimized production and distribution processes, as well as
"smart cities", it will reveal also unexpected links between our activities. But
beyond this, where are we heading?
Will Artificial Intelligence overtake us?
Today, an average mobile phone is more powerful than the computers
used to send the Apollo rocket to the moon and even the Cray-2
supercomputer thirty years ago, which weighted several tons and had the
size of a building. This amazing progress is a result of "Moore's law", which
posits that computer processing power increases exponentially. But thanks
to powerful "machine learning" methods, information systems are
becoming more intelligent, too. They do calculations faster than us, they
play chess better than us, they remember information longer than us, and
they perform ever more tasks that only humans could do in the past. Will
they soon be smarter than us? Are the days counted when humans were the
"crown of creation"? The famous futurist Ray Kurzweil (*1948), now a
director of engineering at Google, was the first to claim that this moment
(the so-called "singularity") is near.
A few years ago, when I read that Artificial Intelligence (AI) might pose
a serious threat to humanity, I found this hard to imagine. However,
experts now predict that computers will be able to perform most tasks
better than humans in 5 to 10 years, and reach brain-like functionality
within 10 to 25 years. The AI systems of today are no longer expert systems
programmed by computer scientists they are learning and evolving. To
understand the implications, I recommend you to watch some eye-opening
videos on deep learning and artificial intelligence.
These videos
demonstrate that most of the activities we earn our money with today (such
as reading and listening to language, distinguishing different patterns, and
performing routines) can now be done by computers almost as well as
humans, if not better. Jim Spohrer's perspective on IBM's cognitive
computing products is as follows:
The first Artificial Intelligence
applications will be our tools. As they get smarter, they will become our
"partners", and when they overtake us, they will be our "coaches".
Will algorithms, computers, or robots be our bosses in a few decades
from now? The Massachusetts Institute of Technology has started to study
such scenarios.
It is extremely important therefore to realize that the digital
revolution is not just about more powerful computers, better smartphones
or fancier gadgets. The digital revolution will change all our personal lives,
and it will transform entire economies and societies. In fact, in the coming
two or three decades we will see some dramatic changes. A lot of
production and services will become automated, and this will fundamentally
change the way we work in future.
Quite soon, within the next two decades or so, less than 50 percent of
people will have jobs for which they have been trained (i.e. agriculture,
industry or services).
Even highly skilled jobs will be at risk. How will the
masses of personal data collected about each of us then be used?
When Big Data starts to steer our lives
It may sound far-fetched at first, but we must ask this question: "Will we
be remotely controlled by personalized information, or is this happening
already?" It is clear that Google and Facebook know very well what we are
interested in when they place individually tailored ads that often match our
interests and tastes. The smart app Google Now is an example of a smart app
that tells you what to do, if you have signed up for it. For instance, if there
is a traffic jam on the way to your next appointment, Google Now may
suggest you to leave 15 minutes earlier in order to be on time. Similarly,
Amazon suggests what we might want to buy, and Trip Advisor suggests what
destinations to visit and what hotels to book. Twitter tells us what others
think and what we should perhaps think, too. Facebook suggests whom to
be friend with. Apps like OkCupid even suggest whom we might date.
While all these services might certainly be helpful we might ask: what
will be the consequences? Will we end up living in a digital "golden cage"
a "filter bubble" as Eli Pariser calls it?
Will we just execute what our smart
devices tell us to do? Modern learning software already corrects us when we
make mistakes. Smart wristbands tell us how many more steps we should
make in a day. Eye trackers can discover if we are tired or stressed, and
computers can predict when our performance will decrease. In other words,
we are increasingly patronized in our decision making by computer
programs. Will we soon be incompetent to live on our own? And, are we
sliding into a "nanny state", where we don't have a say? Has our decision-
making, has democracy been "hacked"?
Why should we care? Isn't it just great that computers do calculations
for us more quickly than we can do them ourselves? Isn't it fantastic that
our smartphones help us manage our agendas, and that Google Maps tells us
the way to go? Why not ask Apple's Siri to recommend us a restaurant? I
certainly don't object to any of these functions, but it is important to
recognize that this is just the beginning of what is to come. Little by little,
our role as self-determined decision-makers is being eroded. The next
logical step will be the automation of society. How might this look like?
The cybernetic society
This question brings us to an old concept that goes back to Norbert
Wiener (1894-1964)
who was known as the father of control theory
("cybernetics"), Wiener imagined that our society could be controlled like a
huge clockwork, where every company's and every individual's activity
would be coordinated by a giant plan of how to run a society in an optimal
Many decades ago, Russia and other communist countries ran command
economies. However, they failed to be competitive, while the capitalist
approach based on free entrepreneurship thrived. At that time information
systems were much more limited in power and scope than today. This has
changed. Now there is a third approach besides communism and capitalism:
socio- economic systems that are managed in a data-driven way. In the early
70ies Chile was the first country to attempt a "cybernetic society".
established a control center, which collected the latest production data of
major companies every week. This was a truly revolutionary approach, but
despite its obvious advantages, the government was unable to stay in power,
and Salvador Allende (1908-1973), the president of the country, had a tragic
Nevertheless, the dream of a cybernetic society has not ceased to
Today, both Singapore and China are trying to plan social and economic
activities in a top-down way using lots of data, and they enjoy larger growth
rates than Western democracies. Therefore, many economic and political
leaders raise the question: "Is democracy outdated?" Should we run our
societies in a cybernetic way according to a grand plan? Will Big Data allow
us to optimize our future?
Wise kings and benevolent dictators, fueled by Big Data
Given all the data one can now accumulate, is it conceivable that
governments or big companies might try to build "God-like", almost
"omniscient" information systems? Could these systems then make
decisions like a "benevolent dictator" or "wise king"? Will they be able to
avoid coordination failures and irrationality? Would it even become possible
to create the best of all worlds by collecting all data globally and building a
digital "crystal ball" to predict the future, as some people have suggested? If
He committed suicide.
this were possible, and given that "knowledge is power", could a sort of
"magic wand" be created by a government or company to ensure that the
benevolent dictator's master plan remains on course?
What would it take to build such powerful tools? It would require
information systems that knew us so well that they could manipulate our
decisions by stimulating us with the right kind of personalized information.
As I will show in this book, such systems are actually on their way, and to
some extent they already exist.
Do we need to sacrifice our personal freedom?
Establishing a cybernetic society has a number of important
implications. For example, we would need a lot of personal data. In order to
be able to control an entire society, it seems important to understand how
we think, what we feel, and what we plan to do. Large amounts of personal
data are essential to allow artificially intelligent machines to learn what
determines our actions and how to influence them. In fact, while mass
surveillance is surprisingly ineffective in fighting terrorism
and child
it seems to be very useful to establish a cybernetic society.
But as with every technology, there are serious drawbacks. We would
probably lose some of the most important rights and values that have
formed the bedrock of democracies and their judicial systems since the Age
of Enlightenment. Secrecy and privacy would be eroded by information
technologies, and with this, we would lose our security and human values
such as mercy and forgiveness. With the advent of predictive policing and
other proactive enforcement measures, we could see a deviation from the
"presumption of innocence" principle towards the implementation of an
ominous "public interest" policy at the cost of individual rights. Do we
therefore need to worry about the fact that the leading Big Data nation has
more people per thousand inhabitants in prison than any other country,
including Russia and China?
The biggest sexual child abuse scandals revealed in the past years have actually not been
discovered by digital surveillance techniques.
FIGURE 1.1: Illustration of how citizens could be punished for any minor transgression
of law, even if it is entirely harmless to society. Note that the traffic authority figured out
my foreign address to send me this ticket for going 1km/h too fast with my car, while I
was actually not even the driver (which they didn't check)...
With the help of mass surveillance, it is now possible to punish even the
smallest mistakes that everyone makes in an overregulated society.
speeding ticket above for going 1km/h too fast with my car (see Fig. 1.1) is
perhaps a portent of what could become possible on a much larger scale. In
addition to sanctions by public authorities, will insurance companies punish
us in future for eating unhealthy food? Will banks offer us punitive interest
rates on loans simply because we live in the "wrong neighborhood"? Will
we get restricted offers of products or services if we don't fulfill certain
expectations, or will we have to pay higher prices? While this might sound
like a dystopian science fiction fantasy, much of this is already happening.
China is now even planning to rate the behavior of all its citizens on a one-
dimensional scale, including what they do in the Internet.
Opinions that
match the thinking of the communist political party will be rewarded. The
resulting score will be used to determine whether or not a person gets a
particular job or loan.
In this connection I recommend to read J. Schmieder (2013) Mit einem Bein im Knast:
Mein Versuch, ein Jahr lang gesetzestreu zu leben (Bertelsmann).
Can such technology- and data-driven approaches turn a country into a
"perfect clockwork"? And given that every country is exposed to global
competition, will it just be a matter of time until democracies adopt similar
approaches? If you think this is far-fetched, it is probably good to recall that
several influential decision-makers have recently praised China and
Singapore as models for the world.
Such thinking could soon end freedom
and democracy as we know it.
That is why we must pay attention to this
now. Of course, some people might ask why we shouldn't do it, if this
increases the efficiency of our lives and of our society, why shouldn't we do
it? Doesn't history teach us that society evolves over time? Why should we
worry, if companies and governments take care of us?
The crucial question is whether they are doing a good job in satisfying
our needs and interests. In view of financial and economic crises,
cybercrime, climate change and many other problems, it seems, however,
that governments have great difficulties to fulfill this promise. Similarly, if
we consider Silicon Valley as a business-driven vision of society, it also
seems far-fetched to claim that everyone there is well taken care of.
Who will rule the world?
There is no doubt that Big Data has the potential to be totalitarian. But,
in principle, there is nothing wrong with Big Data, Artificial Intelligence,
and cybernetics. The question is only, how to use it? For example, who will
rule the world in future: will it be big business, government elites, or
Artificial Intelligence? Will citizens and experts be no longer relevant to
decision-making processes? If powerful information systems knew the
world and each of us, would they vote or take decisions for us? Would they
tell us what to do or steer our behavior through personalized information?
Or will we instead live in a free and democratic society, in which everyone
makes decisions in an autonomous but well- coordinated way, empowered
by personal digital assistants?
Two scenarios: coercion or freedom
Will we need to sacrifice our privacy, freedom, dignity, and
informational self- determination for a more efficient governance of our
world? We must think about these possibilities now. Due to the important
societal, economic, legal and ethical implications, we must take some crucial
For example, on March 24, 2015, The Economist featured a comment entitled "China
Model. This house believes China offers a better development model than the West".
If you don't like the concepts of freedom and democracy, replace them by the related ones
of innovation and collective intelligence, which are important to find good solutions to
complex problems (see Appendix 4.1).
In the aftermath of September 11, 2001, we have certainly
witnessed increasing attempts to control citizen activities, including a
massive surveillance of our on-line activities. Does the digital revolution
imply that we will lose our human rights? Will we lose our autonomy and
merely obey what powerful information systems tell us to do? Will we end
up with censorship?
The digital revolution implies great opportunities and risks. Digital
technologies enable different ways of running future economies and
societies. If we don't want to lose our jobs, personal freedom, and
democracy, we must carefully consider how to make digital technologies
work for us, rather than against us. There are at least two possibilities by
automate society: we may either run it in a top-down way, trying to
technocratically controlling citizens decisions and actions, using powerful
information technologies, or we may instead support bottom-up self-
organization based on distributed control. The latter would be compatible
with individual freedom, creativity and innovation (see Fig. 1.2 below).
However, the digital revolution enables both. As I will explain further on,
to support largely autonomous decisions and processes and the
coordination of them, we would have to
1. create participatory opportunities,
2. support informational self-control,
3. increase distributed design and control elements,
4. add transparency for the sake of trust,
5. reduce information biases and noise,
6. enable user-controlled information filters,
7. support socio-economic diversity,
8. increase interoperability and innovation,
9. build coordination tools,
10. create digital assistants,
11. support collective ("swarm") intelligence,
12. measure and consider external effects ("externalities"),
13. enable favorable feedback loops,
14. support a fair and multi-dimensional value exchange,
15. increase digital literacy and awareness.
FIGURE 1.2: Schematic illustration of two evolutionary paths, leading to two different
types of a digital society. Path A would undermine individual freedoms, democracy, and
jobs for most of us. Path B corresponds to a society based on self-control and participatory
information systems supporting creative and innovative activities of everyone. Which one
will we choose?
In this book, I am trying to offer concepts and ideas that can contribute
to a smarter and more resilient digital society. Such a framework is needed,
because in many important respects, the world has become quite
unpredictable and unstable. This is partially due to the increasing level of
interdependency of our systems, often driven by advances in Information
and Communication Technology. Therefore, which approach will be
superior in a world characterized by too much data, too much speed, and
too much connectivity? Will it be top-down governance or bottom-up
participation? Or would it be better to combine both approaches? And how
would we do this?
We will see that this is mainly depends on the
complexity of the systems surrounding us.
In his book on The Third Industrial Revolution, Jeremy Rifkin gives interesting answers to
this for the case of smart (energy) grids and some other systems.
A better future ahead of us
Despite the many challenges, I am still optimistic about our long-term
future on the whole. We have already managed societal transitions several
times in human history and I am sure we can manage this one too.
In the following chapters I hope to make a contribution to a necessary
public debate, by proposing two main possible societal frameworks for the
coming digital age. One of these is based upon the concept of a "big
government", which takes decisions like a "benevolent dictator" or a "wise
king". This framework would be empowered by huge masses of data in the
manner of a digital "crystal ball". This might be seen as a futuristic version
of Thomas Hobbes (1588-1679) "Leviathan", the powerful state. His belief
was that social order can not exist without a powerful state, otherwise we
would all behave like wild beasts.
The alternative framework for the digital society is based on the concept
of self-organizing systems. This vision relates to the concept of the
"invisible hand", for which Adam Smith (1723-1790) is known. He assumed
that the best societal outcomes are reached through self-organization based
on market forces. However, financial meltdowns and "tragedies of the
commons" such as environmental pollution or harmful climate change
suggest that the "invisible hand" cannot be relied on.
But, what if future information and communication technologies would
allow us to reach desirable systemic outcomes through decentralized
decision-making and self-organization? Can distributed control and
coordination mechanisms, empowered by real-time measurements and
feedback, make the "invisible hand" work? The feasibility of this exciting
vision will be explored in the later chapters of this book, in which I will
describe a new paradigm to achieve success and socio-economic order in
the 21st century. Will this lead us into a new era of creativity, participation,
"collective intelligence", and well- being?
In fact, examples from the spheres of traffic management and
production demonstrate that it is possible to manage complex systems from
the bottom-up and to efficiently produce desirable outcomes in this way. In
the following chapters, I will explain the general principle behind such
"magic self-organization" and how it could help us to navigate our way
through a complex future. I will further explain the role of collective
intelligence and how it can help us to cope with the complexity of our
globalized world.
This has been expressed by the Latin phrase "homo hominis lupus", i.e. humans treat
each other like wolves.
Therefore, rather than trying to control or combat the self-organized
dynamics of complex systems such as our economy, financial system, global
trade, and transportation systems, we could learn to harness their
underlying forces to our benefit. Of course, this would involve to locally
adapt the interactions of the components of these systems. But if we could
achieve this, self-organization could be used to produce desirable outcomes,
and this would enable us to create well-ordered, effective, efficient and
resilient systems!
Critics might argue that, just because self-organization has been shown
to work in complex technological systems (such as traffic control or
industrial production lines), this does not necessarily mean that it would
also work for socio-economic systems. After all, the behaviors of people
can be quite surprising. In the light of this counterargument, we will explore
whether and when self- organization can outperform conventional top-
down control in managing complex dynamical systems. It will discuss
institutional settings and interaction rules that will allow self-organizing
systems to be superior. We can now use real-time data to enable adaptive
feedback mechanisms, so that systems behave favorably and in a stable way.
More specifically, I propose that the "Internet of Things", with its vast
underlying networks of sensors, will make socio- economic self-
organization possible in a distributed and bottom-up way. But the crucial
question is how to make a data-oriented approach based on distributed
control work? The solution, as we will see, requires "complexity science.
On the way to a smarter digital society
In the long run, I am confident about our future, mainly because I
believe in the power of ideas in the digital age. However, we must remain
alert to the possibility that we could make serious mistakes along the way.
The financial system may fail, democracies may intentionally or
accidentally turn into surveillance societies, or we may end up fighting
wars. Therefore, with this book, I am trying to explain the opportunities
and risks ahead of us.
Signs of change are everywhere. Information technologies are
transforming the global economy at a rapid pace. In essence, we are
experiencing nothing less than a "third economic revolution",
leading to
an "Economy 4.0". Its effects will be at least as profound as those of the
first (agrarian to industrial) and second (industrial to service) revolutions.
The ubiquity of digital technologies such as social media, smart devices,
the Internet of Things and Artificial Intelligence is giving rise to a digital
society. We can no longer afford to be passive bystanders to this seismic
societal transition. We must prepare for it. But we should not regard these
changes merely as a threat to social and global stability. In fact, we are faced
with a once-in-a-century opportunity!
For example, the digital revolution is not only changing the way we
learn, behave, make decisions and live. It is also altering the way we
produce and consume, and our conception of ownership. Information is a
very interesting resource in that it is the basis of culture, and can be shared
as often as we like. To get more of it, we do not have to take it away from
others. We don't have to fight for it. This of course will depend on how our
economy is organized in future. In particular, in how we reward effort for
the production of data, information, knowledge and creative digital
products. We can either perpetuate the outmoded principles of the 20th
century or open the door to a smarter 21st century society. Why don't we
do the latter?
Many people are now talking about "smart homes", "smart factories",
"smart grids", and "smart cities". It is logical that we will soon have a
"smart economy" and a "smart society". Networked information systems
will enable entirely new solutions to the world's problems. One thing is
therefore clear: the world in the digital age will be very different. But even if
we can't exactly predict what the future will hold, can we at least get a
glimpse of it? I believe we can, at least to some extent, and some trends are
already emerging. Clearly, the characteristics of the future world will result
from the technological, social and evolutionary forces shaping it. The
technological drivers include Big Data, the Internet of Things and Artificial
Intelligence. The social drivers include the increase of information volumes
and networking. In addition, there are evolutionary forces that will lead to
new kinds of incentive systems and decision-making. I will attempt to
evaluate the implications of these forces, and debate the opportunities and
risks they present.
These forces, in turn, are generated by the interactions occurring within
our "anthropogenic systems", i.e. our man-made or human-influenced
techno- socio-economic-environmental systems. In order for interventions
to be beneficial, we must understand how these interactions and the
forces they are creating can be harnessed to our advantage in the same
way as we have learned to harness the forces of nature.
How will the digital revolution reshape our socio-economic institutions?
And what preparations can we make? While addressing these and other
questions, I will pursue a non-ideological approach, oriented neither
politicallyleft nor right, but carefully exploring novel opportunities. I will
try to explainhow we can use the digital revolution to make our society
more innovative, successful and resilient, through understanding the new
logic of the digital era to come. I will discuss how we can adapt our systems
in real-time with novel technologies. Furthermore, I will outline how we
can build an information and innovation ecosystem that can create new
jobs and opportunities for everyone.
We are now ready to dive into the details of why our world is troubled
and how we can fix it by using advanced information and communication
systems in entirely new ways. The following chapters will focus on subjects
such as prediction and control, complexity, self-organization, awareness and
coordination, responsible decision-making, real-time measurement and
feedback, systems design, innovation, reward systems, co-creation, and
collective intelligence. I hope this journey through the opportunities and
risks presented by the emerging digital society will be as exciting for you as
it is for me!
When Systems Get Out Of Control
The digital revolution produces more data, more speed, more connectivity, and more
complexity. Besides creating new opportunities, how will this change our economy and our
societies? Will it make our increasingly interdependent systems easier to control? Or are
we heading towards a systemic collapse? In order to figure out what needs to be done to fix
the world's ills, we must explore why things, as they currently stand, go wrong. The
question is, why haven't we learned how to deal with them yet?
These days, we seem to be surrounded by economic and socio-political
crises, by terrorism, conflict and crime. More and more often, the
conventional "medicines" to tackle these sorts of global problems turn out
to be inefficient or even counter-productive. It is increasingly evident that
we approach these problems with an outdated understanding of our world.
While it may still look more or less as it looked for a long time, the world
has changed inconspicuously but fundamentally.
We are used to the idea that societies must be protected from external
threats such as earthquakes, volcanic eruptions, hurricanes and military
attacks by enemies. Increasingly, however, we are threatened by different
kinds of problems that come from within the system, such as financial
instabilities, economic crises, social and political unrest, organized crime
and cybercrime, environmental change, and the spread of diseases. These
problems have become some of the greatest threats to humanity. According
to the "risk interconnection map" published by the World Economic
Forum, the greatest risks faced by our societies today are of socio-economic
and political nature.
These risks, including factors such as economic
inequality and governance failure, are 21st century problems which cannot
be solved with 20th century wisdom. They are larger in scale than ever
before and result from the complex interdependencies in today's
anthropogenic systems. As a result, it is of paramount importance that we
develop a better understanding of the characteristics of complex dynamical
systems. To this end, I will discuss the main reasons why things go wrong,
such as unstable dynamics, cascading failures in networks, and systemic
interdependencies. And I will illustrate these problems through a large
variety of examples such as traffic jams, electrical blackouts, financial crises,
crime, wars and revolutions.
Phantom traffic jams
Complex systems can be found all around us and include phenomena
such as turbulent flows in our global weather system, decision-making
processes, opinion formation in groups, financial and economic markets,
and the evolution and spread of languages. However, we must carefully
distinguish complex systems from complicated ones. While a car, which
consists of thousands of parts, is complicated, it is easy to control
nevertheless (when it works properly). Traffic flow, on the other hand,
depends on the dynamical interactions of many cars, and forms a complex
dynamical system. These interactions produce counter-intuitive phenomena
such as "phantom traffic jams" which appear to have no cause. Such
"emergent" phenomena cannot be understood from the properties of the
single parts of the system in isolation, here the driver-vehicle units. While
many traffic jams occur for specific, identifiable reasons, such as accidents
or road works, almost everyone has also encountered situations where a
queue of vehicles seems to form "out of nothing" and where there is no
visible cause.
To explore the true reasons for these "phantom traffic jams", Yuki
Sugiyama and his colleagues at Nagoya University in Japan carried out an
experiment in which they asked many people to drive around a circular
The task sounds simple, and the vehicles did in fact flow smoothly
for some time. Then, however, one of the cars caused a minor variation in
the traffic flow, which triggered stop-and-go traffic a traffic jam that
moved backwards around the track.
While we often blame poor driving skills of others for such "phantom
traffic jams", studies in complexity science have shown that they are actually
an emergent collective phenomenon, which is the inevitable result of the
interaction between vehicles. A detailed analysis demonstrates that, if the
traffic density exceeds a certain "critical" threshold that is, if the average
separation of the vehicles is smaller than a certain value then even the
slightest variation in the speed of a car can eventually cause a disruption of
the traffic flow through an amplification effect. As the next driver in line
needs some time to adjust to a change in the speed of the vehicle ahead, he
or she will have to brake a bit harder to compensate for the delay. The then
following driver will have to break even harder, and so on. The resulting
chain reaction amplifies the initially small variation in a vehicle's speed and
this eventually produces a traffic jam which, of course, every single driver
tried to avoid.
Recessions - traffic jams in the world economy?
Economic supply chains might exhibit a similar kind of behavior, as
illustrated by John Sterman's "beer distribution game".
This simulates
some of the challenges of supply chain management. In this experiment,
even experienced managers will end up ordering too much stock, or will run
out of it.
This situation is as difficult to avoid as stop-and-go traffic. In
fact, our scientific research suggests that economic recessions can be
regarded as a kind of traffic jam in the global flow of goods, i.e. the world
economy. This news is actually somewhat heartening, since it implies that
we may be able to engineer solutions to mitigate economic recessions in a
similar way as one can reduce traffic jams by driver assistant systems. The
underlying principle will be discussed later, in the chapter on Guided Self-
Organization. In order to do this, however, one would need have real-time
data detailing the global flow and supply of materials.
Systemic instability
Crowd disasters are another, tragic example of systemic instability. Even
when every individual within a crowd is peacefully minded and tries to
avoid harming others, many people may die nevertheless. Appendix 2.1
outlines why extreme systemic outcomes can even result from normal, non-
aggressive behavior.
What do all these examples tell us? They illustrate that the natural
(re)actions of individuals will often be counterproductive.
Our experience
and intuition often fail to account for the complexity of highly interactive
systems, which tend to behave in unexpected ways. Such complex
dynamical systems typically consist of many interacting components which
respond to each other's behaviors. As a consequence of these interactions,
complex dynamical systems tend to self-organize. That is, a collective
dynamics may develop (such as stop-and-go traffic), which is different from
the natural behavior of the system components when they are separated
from each other (such as drivers who don't like to stop). In other words,
the overall system may have new characteristics that are distinct from those
of its components. This can result, for example, in "chaotic" or "turbulent"
Group dynamics and mass psychology may be viewed as typical
examples of spontaneously emerging collective dynamics occurring in
crowds. What is it that makes a crowd turn "mad", violent, or cruel? For
example, after the London riots of 2011, people have asked how it was
possible that teachers and the daughters of millionaires people you would
not expect to be criminals were participating in the looting? Did they
suddenly develop criminal minds when the demonstrations against police
violence turned into riots? Possibly, but not necessarily so.
The emergence of new properties requires an interaction-oriented
When many components of a complex dynamical system interact, they
frequently create new kinds of structures, properties or functions in a self-
organized way. To describe newly resulting characteristics of the system, the
term "emergent phenomena" is often used. For example, water feels wet,
extinguishes fire, and freezes at a particular temperature. But we would not
expect this based on the examination of single water molecules.
Therefore, complex dynamical systems may show surprising behaviors.
They cannot be steered like a car. In the above traffic flow example,
although the sole aim of participants was to drive continuously at a
reasonably high speed, a phantom traffic jam occurred due to the
interactions between cars. While it is straightforward to control a car, it may
be impossible for individual drivers to control the collective dynamics of
traffic flow, which is the result of the interactions of many cars.
Beware of strongly coupled systems!
Instability is just one possible problem of complex dynamical systems. It
occurs when the characteristic parameters of a system cross certain critical
thresholds. If a system becomes unstable, small deviations from the normal
behavior are amplified. Such amplification is often based on feedback
loops, which cause a mutual reinforcement. If one amplification effect
triggers others, a chain reaction may occur and a minor, random variation
may be enough to trigger an unstoppable domino effect. In case of systemic
instability, as I have demonstrated for the example of phantom traffic jams,
the system will inevitably get out of control sooner or later, no matter how
hard we try to prevent this. Consequently, we need to identify and avoid
conditions under which systems behave in an unstable way.
In many cases, strongly coupled interactions are a recipe for disaster or
other undesirable outcomes.
While our intuition usually works well for
problems that are related to weakly coupled systems (in which the overall
system can be understood as being the sum of its parts and their
properties), the behaviors of complex dynamical systems can change
dramatically, if the interactions among their components are strong. In
other words, these systems often behave in counter-intuitive ways, so that
conventional wisdom tends to be ineffective for managing them.
Unintended consequences or side effects are common.
What further differences do strong interactions make? First, they may
cause larger variability and faster changes, particularly if there are "positive
feedbacks" that lead to mutual acceleration. Second, the behavior of the
complex system can be hard to predict, making it difficult to plan for the
future. Third, strongly connected systems tend to show strong correlations
between the behaviors of (some of) its components. Fourth, the
possibilities to control the system from the outside or through the behavior
of single system components are limited, as the system-immanent
interactions may have a stronger influence. Fifth, extreme events occur
more often than expected and they may affect the entire system.
In spite of all this, most people still have a component-oriented
worldview, centered on individuals rather than groups and interdependent
events. This often leads to "obvious"
but wrong conclusions. For example,
we praise heroes when things go well and search for scapegoats when
something goes wrong. Yet, the discussion above has clearly shown how
difficult it is for individuals to control the outcome of a complex dynamical
system, if the interactions between its components are strong. This fact can
also be illustrated by politics.
Why do politicians, besides managers, have (on average) the worst
reputation of all professions? This is probably because we think they are
hypocritical; we elect them on the basis of the ideas and policies they
publicly voice, but then they often do something else. This apparent
hypocrisy is a consequence of the fact that politicians are subject to many
strong interactions with lobbyists and interest groups, who have diverse
points of view. All of these groups push the politicians into different
directions. In many cases, this forces them to make decisions that are not
compatible with their own point of view a fact, which is hard to accept
for the voters. However, if we believe that democracy is not just about
elections every few years, but also about a continuous consolidation
between citizens and their elected representatives, it could be argued that it
would be undemocratic for politicians to unreservedly place their own
personal convictions above the concerns of private citizens and businesses
in the systems they represent. Managers of companies find themselves in
similar situations. They are exposed to many different factors they must
consider. This kind of interaction-based decision-making extends far
beyond boardrooms and parliaments. Think of the decision-dynamics in
families: if it were easy to control, there would be probably less divorces...
Crime is another example of social systems getting out of control.
can happen both on an individual and collective level. Many crimes,
In social systems, this may lead to an erosion of trust in private and public institutions,
which can create social, political or economic instability.
The traditional explanation is that a crime is committed if the expected reward is larger
than the likely punishment, multiplied by the probability of being caught and convicted.
including murders, are committed by average people, rather than career
criminals (and even in countries with death penalty).
A closer inspection
shows that many crimes correlate with the circumstances individuals find
themselves in. For example, group dynamics often plays an important role.
Many scientific studies also show that the socio-economic conditions are a
strong determining factor of crime. Therefore, in order to counter crime, it
might be more effective to change these socio-economic conditions rather
than sending more people to jail. I say this with one eye on the price we
have to pay to maintain a large prison population a single prisoner costs
more than the salary of a postdoctoral researcher with a PhD degree! It
worries even more that prisons and containment camps have apparently
become places where criminal organizations are formed and terrorist plots
are planned.
In fact, our own studies suggest that many crimes spread by
Cascade effects in complex networks
To make matters worse, besides the dynamic instability caused by
amplification effects, complex dynamical systems may produce even bigger
problems. Worldwide trade, air traffic, the Internet, mobile phones, and
social media have made everything much more convenient and
connected. This has created many new opportunities, but everything now
depends on a lot more things. What are the implications of this increased
level of interdependency? Today, a single tweet can send stock markets
spinning. A controversial Youtube video can trigger a riot that kills dozens of
Increasingly, our decisions can have consequences on the other
side of the globe, many of which may be unintended. For example, the
rapid spread of emerging epidemics is largely a result of the scale of global
air traffic today, and this has serious repercussions for global health, social
welfare and economic systems.
It is being said that "the road to hell is paved with good intentions". By
networking our world, have we inadvertently created conditions in which
Theoretically, therefore, it should be possible to eliminate all crime simply by raising the
punishment. If all criminals were rational egoists, who constantly calculated their expected
payoff from crime, this idea should even work. Assuming sufficiently high conviction rates,
the strong punishment would make crime "unattractive" because of the expectation of
making a "loss". However, empirical evidence questions these simple assumptions. On the
one hand, people don't behave entirely rational. For example, they don't usually pick
pockets, even though they could often escape punishment. On the other hand, deterrence
strategies are surprisingly ineffective in most countries and high crime rates are often
recurrent. For example, even though the USA have 10 times more prisoners than most
European countries, the rates of various crimes are still much higher. Therefore, the
conventional understanding of crime is wrong.
disaster is more likely to spread? In 2011 alone, at least three cascading
failures with global impact were happening, thereby changing the face of
the world and the global balance of power: The world economic crisis, the
Arab spring and the combination of an earthquake, a tsunami and a nuclear
disaster in Japan. In 2014, the world was threatened by the spread of Ebola,
the crisis in Ukraine, and the conflict with the Islamic State (IS). In the
following subsections, I will therefore discuss some examples of cascade
effects in more detail.
Large-scale power blackouts
On November 4, 2006, a power line was temporarily turned off in Ems,
Germany, to facilitate the transfer of a Norwegian ship. Within minutes,
this caused a blackout in many regions all over Europe, from Germany to
Portugal! Nobody expected this to happen. Before the line was switched
off, a computer simulation predicted that the power grid would still operate
well without the line. However, the scenario analysis did not account for the
possibility that another line would spontaneously fail. In the end, a local
overload in Northwest Germany triggered emergency switch-offs all over
Europe, creating a cascade effect with pretty astonishing results. Blackouts
occurred in regions thousands of kilometers away.
Can we ever hope to understand such strange behavior? In fact, a
computer-based simulation study of the European power grid recently
managed to reproduce quite similar effects.
It demonstrated that the
failure of a few network nodes in Spain could create an unexpected
blackout several thousand kilometers away in Eastern Europe, while the
electricity network in Spain would still work.
Furthermore, increasing the
capacity of certain parts of the power grid could worsen the situation and
cause an even greater blackout. Therefore, the weak elements of the system
serve an important function: they are "circuit breakers", which can interrupt
the failure cascade. This is an important fact to remember.
From bankruptcy cascades to financial crisis
The sudden financial meltdown in 2008 is another example of a crisis
that hit many companies and people by surprise. In a presidential address to
the American Economic Association in 2003, Robert Lucas said:
"[The] central problem of depression-prevention has been solved."
Similarly, Ben Bernanke, the former chairman of the Federal Reserve
Board, held a longstanding belief that the economy was both well
understood and in sound financial shape. In September 2007, Ric Mishkin
while other areas close to the region of initial overload were not affected at all
(*1951), a professor at Columbia Business School and then a member of
the Board of Governors of the US Federal Reserve System, made another
interesting statement, reflecting widespread beliefs at this time:
"Fortunately, the overall financial system appears to be in good health,
and the U.S. banking system is well positioned to withstand stressful market
As we all know with the benefit of hindsight, things turned out very
differently. A banking crisis occurred only shortly later. It started locally,
when a real estate bubble burst, which had formed in the West of the USA.
As it was a regional problem, most people thought it could be easily
contained. But the mortgage crises had spillover effects on stock markets.
Certain financial derivatives could not be sold anymore and became "toxic
assets". Eventually, hundreds of banks all over the US went bankrupt. How
could this happen? A video produced by professor Frank Schweitzer and
others presents an impressive visualization of the chronology of
bankruptcies in the USA after Lehman Brothers collapsed.
the default of a single bank triggered a massive cascading failure in the
financial sector. In the end, hundreds of billions of dollars were lost.
The video mentioned above looks surprisingly similar to another one,
which I often use to illustrate cascade effects.
It shows an experiment in
which many table tennis balls are placed on top of mousetraps. The
experiment impressively demonstrates that a single local disruption can
mess up an entire system. The video illustrates chain reactions, which are
the basis of atomic bombs or nuclear fission reactors. As we know, such
cascade effects can be technologically controlled in principle, if a certain
critical mass (or "critical interaction strength") is not exceeded.
Nevertheless, these processes can sometimes get out of control, mostly in
unexpected ways. The nuclear disasters in Chernobyl and Fukushima are
well-known examples of this. We must, therefore, be extremely careful with
large-scale systems featuring cascade effects.
A world economic crisis results
As we know, the cascading failure of banks mentioned above was just
the beginning of an even bigger problem. It subsequently triggered a global
economic and public spending crisis. Eventually, the financial crisis caused
a worldwide damage of $15 trillion
an amount a hundred times as big as
the initial real estate problem. The events even threaten the stability of the
Euro currency and the EU. Several countries including Greece, Ireland,
Portugal, Spain, Italy and the US were on the verge of bankruptcy. As a
consequence, many countries are suffering from unprecedented
unemployment rates, amounting to more than 24 million unemployed
people in Europe. In some countries, more than 50 percent of young
people do not have a job. In many regions, this has caused social unrest,
political extremism and increased rates of suicide, crime and violence.
Unfortunately, the failure cascade hasn't been stopped, yet. There is a
long way to go until we fully recover from the financial crisis and from the
public and private debts accumulated in the past years. If we can't
overcome this problem soon, it even has the potential to endanger peace,
democratic principles and cultural values, as I pointed out in a letter to
George Soros in 2010.
Looking at the situation in Ukraine, we are perhaps
seeing this scenario already.
While all of this is now plausible with the benefit of hindsight, the
failure of conventional wisdom to provide an advanced understanding of
events is reflected by the following quote, made by the former president of
the European Central Bank, Jean-Claude Trichet in November 2010:
"When the crisis came, the serious limitations of existing economic and financial
models immediately became apparent. Arbitrage broke down in many market segments,
as markets froze and market participants were gripped by panic. Macro models failed to
predict the crisis and seemed incapable of explaining what was happening to the economy
in a convincing manner. As a policy-maker during the crisis, I found the available models
of limited help. In fact, I would go further: in the face of the crisis, we felt abandoned by
conventional tools." Similarly, Ben Bernanke summarized in May 2010: “The brief
market plunge was just an example of how complex and chaotic, in a formal sense, these
systems have become… What happened in the stock market is just a little example of
how things can cascade, or how technology can interact with market panic.”
Even leading scientists found it difficult to make sense of the crisis. In a
letter on July 22, 2009 to the Queen of England, the British Academy came
to the conclusion:
"When Your Majesty visited the London School of Economics last November, you
quite rightly asked: why had nobody noticed that the credit crunch was on its way? ... So
where was the problem? Everyone seemed to be doing their own job properly on its own
merit. And according to standard measures of success, they were often doing it well. The
failure was to see how collectively this added up to a series of interconnected imbalances
over which no single authority had jurisdiction. ... Individual risks may rightly have been
viewed as small, but the risk to the system as a whole was vast. ... So in summary ... the
failure to foresee the timing, extent and severity of the crisis … was principally the failure
of the collective imagination of many bright people to understand the risks to the systems
as a whole."
Thus, was nobody responsible for the financial crisis in the end? Or do
we all have to accept some responsibility, given that these problems are
collective outcomes of a huge number of individual (inter)actions? And
how can we differentiate the degree of responsibility of different individuals
or firms? This is certainly an important question worth thinking about.
It is also interesting to ask, whether complexity science could have
forecasted the financial crisis? In fact, I followed the stock markets closely
before the crash and noticed strong price fluctuations, which I interpreted
as advanced warning signals of an impending financial crash. For this
reason, I sold my stocks in late 2007, while I was sitting in an airport
lounge, waiting for my connection flight. In spring 2008, about half a year
before the collapse of Lehman brothers, James Breiding, Markus Christen
and I wrote an article taking a complexity science view on the financial
system. We came to the conclusion that the financial system was in the
process of destabilization. We believed that the increased level of
complexity in the financial system was a major problem and that it made
the financial system more vulnerable to cascade effects, as was later also
stressed by Andrew Haldane, the Chief Economist and Executive Director
at the Bank of England.
In spring 2008, we were so worried about these trends that we felt we
had to alert the public. At that time, however, none of the newspapers we
contacted were ready to publish our essay. "It's too complicated for our readers"
was the response. We responded that "nothing can prevent a financial crisis, if you cannot
make this understandable to your readers". With depressing inevitability, the
financial crisis came. Although it gave us no pleasure to be proved right
when the consequences were so dire, a manager from McKinsey's UK
office commented six months later that our analysis was the best he had
Of course, some far more prominent public figures also saw the
financial crisis coming. The legendary investor Warren Buffet, for example,
warned of the catastrophic risks created by large-scale investment in
financial derivatives. Back in 2002 he wrote:
"Many people argue that derivatives reduce systemic problems, in that
participants who can't bear certain risks are able to transfer them to
stronger hands. These people believe that derivatives act to stabilize the
economy, facilitate trade, and eliminate bumps for individual participants.
On a micro level, what they say is often true. I believe, however, that the
macro picture is dangerous and getting more so. ... The derivatives genie is
now well out of the bottle, and these instruments will almost certainly
multiply in variety and number until some event makes their toxicity clear.
Central banks and governments have so far found no effective way to
control, or even monitor, the risks posed by these contracts. In my view,
derivatives are financial weapons of mass destruction, carrying dangers that,
while now latent, are potentially lethal."
As we know, it still took five years until the "investment time bomb"
exploded, but then it caused trillions of dollars of losses to our economy.
Fundamental ("radical") uncertainty
In liquid financial markets and many other systems, which are difficult
to predict such as our weather, we can still determine the probability of
close enough events, at least approximately. Thus, we can make
probabilistic forecasts such as "there is a 5 percent chance to lose more
than half of my money when selling my stocks in 6 months, but a 70
percent chance that I will make a good profit..." It is then possible to
determine the expected loss (or gain) implied by likely actions and events.
For this purpose, the damage or gain of each possible event is multiplied by
its probability, and the numbers are added together to predict the expected
damage or gain. In principle, we could do this for all the actions we might
take, in order to determine the one that minimizes damage or maximizes
gain. The problem is that it is often impractical to calculate these
probabilities. With the increasing availability of data, this problem may
recede, but it will remain difficult or impossible to determine the
probabilities of "extreme events". By their very nature, there tends to be
little precedent for such rare events, which means that the empirical basis is
too small to determine their probabilities.
In addition, it may be completely impossible to calculate the expected
damage incurred by a problem in a large (e.g. global) system. Such
"fundamental" or "radical" uncertainty can result from cascade effects,
whereby one problem is likely to trigger other problems, leading to a
progressive increase in the overall damage. In principle, the overall losses
may not be quantifiable in such situations at all. This means in practice that
the actual damage might be either insignificant (in the best case) or
unbounded (in the worst case), or anything in between. In an extreme case,
this might lead to a collapse of the entire system, as we know it from the
collapse of historical empires and civilizations.
Explosive epidemics
When studying the spread of diseases, the outcome is highly dependent
on the degree of physical interactions between people who may infect each
other. A few additional airline routes might make the difference between a
case in which a disease is contained, and a case which develops into a
devastating global pandemic.
The threat of epidemic cascade effects might
be even worse if damage which occurs early on reduces the ability of the
system to withstand problems later. For example, assume a health system in
which the financial or medical resources are limited by the number of
healthy individuals who produce them. In such a case, it can happen that
the resources needed to heal the disease are increasingly used up, such that
the epidemic finally spreads explosively. A computer-based study, which
Lucas Böttcher, Olivia Woolley-Meza, Nuno Araujo, Hans Hermann and I
performed, shows that the dynamics in such a system can change
dramatically and unexpectedly.
Thus, have we built global networks which
we can neither predict nor control?
Systemic interdependence
Recently, Shlomo Havlin and others made a further important
discovery. They revealed that networks of networks can be particularly
vulnerable to disruptions.
A typical example of this is the interdependence
between electrical and communication networks. Another example, which
illustrates the global interdependence between natural, energy, climate,
financial and political systems is provided by the Tohoku earthquake in
Japan in 2011. The earthquake caused a tsunami which triggered a chain
reaction and a nuclear disaster in several reactors at Fukushima. Soon
afterwards, Germany and Switzerland decided to exit nuclear power
production over the next decade(s). However, alternative energy sources are
also problematic, as European gas supply depends on geopolitical regions
which might not be fully reliable.
Likewise, Europe’s DESERTEC project a planned €1000 billion
investment in solar energy infrastructure has been practically given up due
to another unexpected event, the Arab Spring. This uprising was triggered
by high food prices, which in turn was partially caused by biofuel
production. While biofuels were intended to improve the global CO2
balance, their production decreased the production of food, making it more
expensive. The increased food prices were further amplified by financial
speculation. Hence, the energy system, the political system, the social
system, the food system and the financial system have all become closely
interdependent, making our world increasingly vulnerable to disruption.
Have humans created a "complexity time bomb"?
For a long time, problems such as crowd disasters and financial crashes
have puzzled humanity. Sometimes, they have even been regarded as "acts
of God" or "black swans"
that we had to endure. But problems like these
should not be simply put down to "bad luck". They are often the
consequence of a flawed understanding of the counter-intuitive way in
which complex systems behave. Fatal errors and the repetition of previous
mistakes are frequently the result of an outdated way of thinking. However,
complexity science allows us to understand how and when complex
dynamical systems get out of control.
If a system is unstable, we will see amplification effects, such that a local
problem can lead to a cascading failure, which creates many further
problems down the line. For this reason, the degree of interaction between
the system components is crucial. Overall, complex dynamical systems
become unstable, if the interactions between their components get stronger
than frictional effects, or if the damage resulting from the degradation of
system components occurs faster than they can recover. As a result, the
timing of processes can also play a key role in determining whether the
overall system will remain stable. This means that delays in adaptation
processes can often lead to systemic instabilities and loss of control (see
Appendix 2.2).
We have further seen that an unstable complex system will sooner or
later get out of control, even if everyone is well-informed and well-trained,
uses advanced technology and has the best intentions. And finally, we have
learned that complex dynamical systems with strong internal interactions or
a high level of connectivity tend to be unstable. As our increasingly
interdependent world is characterized by a myriad of globalized links, it is
necessary to discuss the potential consequences. We must raise a
fundamental question which has mammoth implications for the viability of
our current economic and political systems: have humans inadvertently
produced a "complexity time bomb", i.e. a global system which will
inevitably get out of control?25
In fact, for certain kinds of networks, the potential chain reaction of
cascading failures bears a disturbing and ominous resemblance to that of
nuclear fission. Such processes are difficult to control and catastrophic
damage is a realistic scenario. Given this similarity with explosive processes,
is it possible that our global anthropogenic systems will similarly get out of
control at some point? When considering this possibility, we need to bear in
mind that the speed of a destructive cascade effect might be slow, and the
process may not remind of an explosion. Nevertheless, the process may be
hard to stop and may ultimately lead to a systemic failure.
So, what kinds of global catastrophes might complex societies face? A
collapse of the global information and communication system or of the
world economy? Global pandemics? Unsustainable growth, demographic or
environmental change? A global food or energy crisis? A clash of cultures?
Another world war? A societal shift, triggered by technological innovation?
In the most likely scenario, we will witness a combination of several of
these contagious phenomena. The World Economic Forum calls this the
"perfect storm"19, and the OECD has expressed similar concerns.
Unintended wars and revolutions
It is important to realize that large-scale conflicts, revolutions and wars
can also be unintended outcomes of systemic interdependencies and
For example, the underlying processes which cause crowd disasters are slow, but deadly
instabilities. Remember that phantom traffic jams were unintended
consequences of interactions. Similarly, wars and revolutions can happen
even if nobody wants them. There is a tendency to characterize these events
as the deeds of particular historical figures. But this trivializes and
personalizes such phenomena in a way which distracts from their true,
systemic nature.
It is essential to recognize that complex dynamical systems usually resist
change if they are close to a stable equilibrium. This effect is known as
Goodhart's law (1975), Le Chatelier’s principle (1850-1936) or "illusion of
control". Individual factors and randomness only affect complex dynamical
systems if they are driven to a tipping point,
where they become unstable.
In other words, the much-heralded individuals to whom so much
attention is afforded in our history books were only able to influence
history because much bigger systems beyond their control had already
become critically unstable.
For example, historians now increasingly
recognize that World War I was a largely unintended consequence of a
chain of events. Moreover, World War II was preceded by a financial crisis
and recession, which destabilized the German economic, social and political
system. Ultimately, this made it possible for an individual to become
influential enough to drive the world to the brink of extinction.
Unfortunately, civilization is still vulnerable today and a large-scale war may
happen again. This is even likely if we don't quickly change the way we
manage our world.
Typically, the unintended path towards war is as follows. Initially,
resources become scarce due to a disruption such as a serious economic
crisis. Then, the resulting competition for limited resources leads to an
increase in conflict, violence, crime and corruption. Human solidarity and
mutual tolerance are eroded, creating a polarized society. This causes
further dissatisfaction and social turmoil. People get frustrated with the
system, calling for leadership and order. Political extremists emerge, who
scapegoat minorities for social and economic problems. This decreases
socio-economic diversity, which reduces innovation and further hinders the
economy. Eventually, the well-balanced "socio-economic ecosystem"
collapses, such that an orderly system of resource allocation becomes
impossible. As resources become scarcer, this creates an increasing "need"
for nationalism or even for an external enemy to unify the fractured society.
In the end, as a result of further escalation, war seems to be the only viable
"solution" to overcome the crisis, but instead, it mostly leads to large-scale
Such tipping points are also often called "critical points".
Revolutionary systemic shifts
Note that a revolution, too, can be the result of systemic instability.
Hence, a revolution is not necessarily set in motion by a "revolutionary
leader", who challenges the political establishment. The breakdown of the
former German Democratic Republic (GDR) and some Arab Spring
revolutions have shown that uprisings may even start in the absence of a
clearly identifiable political opponent. On the one hand, this is the reason
why such revolutions cannot be stopped by killing or imprisoning a few
individuals. On the other hand, the Arab Spring took secret services
throughout the world by surprise precisely because there were no
revolutionary leaders. This created complications for countries which
wished to assist these uprisings, as they did not know whom to interact with
for international support.
It is more instructive to imagine such revolutions as a result of situations
in which the interests of government representatives and those of the
people (or particular societal groups) have drifted away from each other.
Similarly to the tensions created by a drift of the Earth's tectonic plates,
such an unstable situation is sooner or later followed by an "earthquake-
like" release of tension (the "revolution"), resulting in a re-balancing of
forces. To reiterate, contrary to the conventional wisdom which assumes
that revolutionary leaders are responsible for political instability, it is due to
an existing systemic instability that these individuals can become influential.
To put it succinctly, in most cases, revolutionaries don't create revolutions,
but systemic instabilities do. These instabilities are typically created by the
politics of the old regime. Therefore, we must ask ourselves how well our
society balances the interests of different groups today and how well it
manages to adapt to a world, which is rapidly changing due to demographic,
environmental and technological change?
It is obvious that there are many problems ahead of us. Most of them
result from the complexity of the systems humans have created. But how
can we master all these problems? Is it a lost battle against complexity, or
do we have to pursue a new, entirely different strategy? Do we perhaps
even need to change our way of thinking? And how can we innovate,
before it is too late? The next chapters will try to answer these questions...
APPENDIX 2.1: How harmless behavior can become critical
In the case of traffic flow, we have seen that a system can get out of
control when the interaction strength (e.g. the density) is too high. Why can
a change in the density make normal and "harmless" behavior become
uncontrollable? To understand this better, Roman Mani, Lucas Böttcher,
Hans J. Herrmann, and I studied collisions in a system of equally sized
particles moving in one dimension,
which is similar to Newton's Cradle.
We assumed that the particles tended to oscillate elastically around equally
spaced equilibrium points, while being exposed to random forces generated
by the environment.
If the distance between the equilibrium points of neighboring particles is
large enough, each particle oscillates around its equilibrium point with
normally distributed velocities, and all particles have the same small
variance in speed. However, when the separation between the equilibrium
points reaches the diameter of the particles, we find a cascade-like
transmission of momentum between particles.
Surprisingly, the variance
of particle speeds rapidly increases towards the boundaries it could even
go to infinity with increasing system size. Due to cascading particle
interactions, this makes their speeds unpredictable and uncontrollable.
While every particle in separation performs a normal dynamics, which is not
excessive at all, their interactions can cause an extreme behavior of the
APPENDIX 2.2: Loss of synchronization in hierarchical systems
When many socio-economic processes are happening simultaneously
while having feedbacks on each other, a puzzling kind of systemic instability
can occur, which is highly relevant for our complex societies, since many
socio-economic processes happen at an increasing pace.
For the sake of illustration, let us first discuss hierarchically organized
systems in physics. There, elementary particles form atoms, atoms form
chemical compounds, these form solid bodies, and together they may form
a planet, which is part of a planetary system, and a galaxy. Similarly, we
know from biology and the social sciences that organs are constituted of
cells, which collectively may form a human body. In turn, humans tend to
organize themselves into groups, cities, organizations and nations.
Importantly, the stability of such hierarchies is based on two important
principles. First, the forces are strongest at the bottom, and second, the
changes are slowest at the top. In other words, adjustment processes in
these systems are faster at lower hierarchical levels (such as the atoms) as
compared to higher ones (such as planetary systems). This means that lower
level variables can adjust quickly to the constraints set by the higher level
variables. As a result, the higher levels basically control the lower levels and
the system remains stable. Similarly, social groups tend to take decisions
more slowly than the individuals who form them. Likewise, organizations
and states tend to change more slowly than the individuals who form them
(at least this is how it used to be in the past).
Such "time-scale separation" implies that the dynamics of a system are
determined only by relatively few variables, which are typically located at
the higher levels of the hierarchy. Monarchies and oligarchies are good
examples for this. In current-day socio-political and economic systems,
however, the higher hierarchical levels sometimes change so fast that the
lower levels have difficulties to keep pace. Laws are now often enacted
more quickly than companies and people can adapt. In the long run, this is
likely to cause systemic instability, as time-scale separation is destroyed, so
that many more variables begin to influence the dynamics of the system.
Such attempts to make mutual adjustments on different hierarchical levels
could potentially lead to turbulence, "chaos", breakdown of
synchronization, or fragmentation of the system. In fact, while it is known
that delays in adaptation can destabilize a system, we are putting many of
our problems on the long finger (e.g. public debts, implications of
demographic change, nuclear waste, or climate change). This creates a
concrete danger that our society will eventually get out of control.
Revealing the causes of success and disaster
Complex systems can behave in unpredictable ways and cause a lot of trouble. But it
doesn't have to be like this. Their behavior depends on the interactions between the system
components, the strength of these interactions, and the institutional settings. Consequently,
for a complex system to work well, it is important to understand the factors that drive its
dynamics. In physics, many phenomena have been understood in terms of forces, which can
be measured by suitable procedures. In a similar way, the success or failure of socio-
economic systems depends on hidden forces, too. Thanks to new data about our world we
can now measure the forces driving socio-economic change. This will allow us to act more
successfully in future.
Societies around the world are suffering from financial crises, crime,
conflicts, wars, and revolutions. These "societal ills" do not occur by
chance, but for a reason. The fact that they are happening time and again
proves that there are hidden causes, which we haven't understood
sufficiently well. This is why we keep failing to cope with these problems.
In future, however, we will be able to understand societal problems and
cure them. This will be akin to the discovery of the x-ray by Wilhelm
Conrad Röntgen (1845-1923), which helped to reveal the causes of many
diseases and to cure billions of people.
Given that we are now living in a Big Data age, will we soon be able to
answer all our questions and find the best possible course of action in every
situation? Of course, it's far from clear that this dream will ever come true.
Nevertheless, it is important to avoid an overdose of radiation, which can be very harmful.
In a similar way, mass surveillance can be harmful to society, as I will show later. When
measuring what's going on in society, it is therefore, important to respect privacy and the
fundamental right of informational self-determination. The Nervousnet platform featured in
this chapter is taking this into account.
However, the growing amount of data about our world will certainly allow
us to measure the hidden forces behind our global technological, social,
economic and environmental systems, just as microscopes and telescopes
enabled us to discover and understand the micro- and macro-cosmos
from cells to stars in the past. Similarly to how we have built elementary
particle accelerators to discover the forces that keep our world together, we
can now create "socioscopes" to reveal the principles that make our society
succeed or fail.
Measuring the world 2.0
It's a sad but well-known fact that the loss of control over a system
often results from a lack of knowledge about the rules governing it.
Therefore, it is important that we learn to measure and understand the
hidden "forces" determining changes in the world around us. This will
eventually put us in a position where we can harness these forces to
overcome systemic instability and create complex dynamical systems with
particular structures, properties and functions.
Remember that some of the greatest discoveries in human history were
made by measuring the world. We have discovered new continents and
cultures. We have reached out to the skies and explored our universe to
discover black holes, dark matter, and new worlds. Now, the Internet is
offering entirely new ways to quantify what's happening on our Earth. By
analyzing the sentiment of blogs, Facebook posts, or tweets, we can visualize
human emotions such as happiness.
Furthermore, it is possible to get a
picture of the social, economic, and political "climate", by identifying the
subjects that people publicly discuss.
By mining data on the Web, we can
also map social and economic indicators. This includes quantities such as
the gross domestic product per capita
or the levels of violence and crime,
highly resolved according to geographic location and time.
It is even
feasible to digitally re-construct our three-dimensional world based on the
photos that people upload on platforms such as flicker.
We can now create "Financial Crisis Observatories" to detect the
likelihood of financial bubbles and crashes.
We can map crises and risks to
help first-aid teams in regions struck by disasters.
We can analyze and
visualize the production of knowledge and the spread of scientific concepts,
as I did it with Amin Mazloumian, Katy Börner, Tobias Kuhn, Christian
Schulz and others
(see Fig. 3.1). Furthermore, it is fascinating to examine
the way culture has spread across the world over the centuries, as
Maximilian Schich Laszlo Barabasi and I did it together with others.
Inspired by Wikipedia and OpenStreetMap, we might now create an
OpenResourcesMap to visualize the resources of the world and who uses
them. This could help, for example, to reduce undesirable shortages. In
addition, we could produce an OpenEcosystemsMap to depict
environmental change and who causes it. What else can we do? Let me
elaborate a health-related example.
FIGURE 3.1: Illustration of scientific productivity and impact.
Monitoring the flu and other diseases
Pandemics are a major threat to humanity. Some of them have killed
millions of people. The Spanish flu in 1918 was a shocking example of this.
In fact, such pandemics are expected to happen time and again because
viruses keep mutating, such that our immune systems might be unprepared.
For instance, the world was caught by surprise by the recent Ebola
To contain the spread of epidemics, the World Health Organization
(WHO) is continuously monitoring emerging diseases. It takes about two
weeks to collect the data from all the hospitals in the world, meaning that
each overview of the current situation is two weeks out of date. However,
Google Flu Trends pioneered an approach called "nowcasting", which was
celebrated as major success of Big Data analytics. It was claimed that it is
possible to estimate the number of infections in real-time, based on the
search queries of Google users. The underlying idea was that queries such as
"I have a headache" or "I don't feel well" or "I have a fever", and so on,
might indicate that the user has the flu. While this makes a lot of sense, the
Google Flu approach was recently found to be unreliable, partly because
Google constantly changes its search algorithms and also because
advertisements bias people's behavior.
Flu prediction better than Google
Fortunately, a model which uses much less data can be used to analyze
how a disease spreads, namely by augmenting data of infections with a
model based on air travel data. Dirk Brockmann and I found this approach
in 2012/13. About ten years back, Dirk started to investigate the spread of
disease by analyzing the time and geographic location of infections using
computer simulations. He also analyzed the paths of dollar bills in his
famous "Where is George?" study.
But when visualizing the spatio-
temporal spread of epidemics, the patterns looked frustratingly chaotic and
unpredictable. The relationship between the arrival time of a new disease as
a function of the distance from the place where it originated was so
scattered that it was hard to make much sense of the data. Eventually,
however, it became clear that this problem resulted from the high volume
of passenger air travel. Thus, Dirk had the idea to define an "effective
distance", based on the volume of travel between all airports in the world,
and to study the spread of disease as a function of this alternative measure
of distance.
In effective distance, two airports such as New York City and
Frankfurt are close to each other because of the large passenger flows
connecting them, while two nearby cities without any direct flights between
them might be largely separated.
Dirk Brockmann and I started to collaborate in 2011, when Germany
was witnessing the spread of the deadly, food-borne EHEC epidemic. I got
in touch with Dirk and suggested that we could combine a model of the
spread of epidemics with a model of food supply chains. In this way, we
wanted to identify the location where the disease originated, which was
unknown at that time. Unfortunately, we could not obtain proper supply
chain data at that time. But our discussion triggered a number of important
ideas. In particular, the research activities shifted from predicting the spread
of diseases toward detecting the locations where they originate.
In fact, when analyzing empirical data of infections as a function of
effective distance from the perspective of all airports worldwide, we found
that the most circular spreading pattern identifies the most likely origin of
Independently of Dirk Brockmann's activities, I became interested in the modeling of
epidemic spread back in 2002. In the wake of the September 11 attacks the year before, there
were fears that terrorists could use anthrax or other deadly germs to threaten the USA and
the rest of the world. At this time, I proposed to Otto Schily, the then German Minister of
Internal Affairs, to build a self-calibrating epidemic simulator to predict the spread of
pandemics. Directly after the outbreak of a disease, accurate data about infection and
recovery rates is often not available. Thus, the idea was that a self-adaptive calibration model
could produce increasingly accurate predictions, as more data became available. At that time,
I received a letter stating that such an approach was not feasible. But of course, it was!
the disease. More importantly, however, once the location of origin of a
disease is known, one can use the circular spreading dynamics as a function
of effective distance to predict the order in which cities will be hit by a
This helps to put medical drugs (such as immunization shots)
and doctors in place where they are most effective in countering the impact
and spread of the disease.
When Ebola broke out, Dirk furthermore used the method discussed
above to make early predictions about possible cases in other countries.
This helped to inform international preparations to contain the virus.
However, I would also like to highlight here the fantastic research teams of
Alessandro Vespignani and Vittoria Colizza, both partners of the FuturICT
initiative. To predict the spread of diseases, they have built a very detailed
and sophisticated simulator. Whenever a disease breaks out, this simulator
can be used to test the effectiveness of countermeasures and inform policy-
makers around the world.
It was found, for example, that closing down
some airline connections can only delay the spread of the disease, while the
best way for industrialized countries to protect themselves from diseases
such as Ebola is to spend their money on fighting the disease in those
countries that are suffering from the disease first.
Creating a Planetary Nervous System as a Citizen Web
Very soon, we will not only have maps, which aggregate data from the
past and represent them as a function of space, time or network
interdependencies. We will also have systems, which deliver real-time
answers. We will be able to ask questions, which trigger tailored
measurements to answer them. "How is the traffic on Oxford Street in
London?" "How is the weather in Moscow?" "How could investment
decisions and consumer choices be affected?" "How happy are people in
Sydney today, and how much money will they spend in shops?" "What
worries people in Paris at the moment?" "How many people are up
between 3am and 4am on Sunday nights around Manhattan's Central
Square, and is it worth selling pizza at that time?" "How noisy is it in the
part of town I am considering to move to?" "What's the rate of flu
infections in the region where I wish to spend my holidays?" "Where are
the road holes in my city located?" "When did we have the last significant
see the movie . It turns out that this
technique can be successfully applied even in cases where certain key information (such as
the infectiousness of the disease and recovery rate) is not well-known, which is typical after
the outbreak of a new disease. The only data besides the outbreak location which is
important for our analysis is the volume of passenger air traffic between all airports. This is
needed to specify the effective distance.
earthquake within a range of 500 kilometers?" Answers to questions like
these would help us to be more aware of the world around us, to make
better decisions, and act more effectively. But how will we get all this real-
time information?
The sensor networks, on which the "Internet of Things" is based, will
enable us to perform real-time measurements of almost everything. They
can be used to build a "Planetary Nervous System" (PNS), an intelligent
information platform proposed by the FuturICT project
The In fact, my team has started to develop
such an information platform, called Nervousnet.
Nervousnet will harness the
power of the Internet of Things for everyone's benefit and will be built and
managed in a participatory way, as a "Citizen Web".
We Similar to
OpenStreetMap, we will develop this system together with an emerging
network of volunteers, who are committed to developing the project
This collaborative approach will give citizens control over their personal
data, in accordance with their right of informational self-determination, and
create new opportunities for everyone. Nervousnet will not only offer the
possibility to contribute to the measurement of our world, in order to
jointly create something like a real-time data Wikipedia. Nervousnet will also
establish a social mining paradigm, where users are given freedom and
incentives to collect, share and use data in ways that do not aim to
undermine privacy. Appendix 3.1 provides further information on the
platform. With your help, it may very well become a cornerstone of the
public information infrastructure of the emergent digital society. So why
don't you join us in building the Nervousnet platform or in measuring the
world around us?
Sociophysics: Revealing the hidden forces governing our society
But it takes more than data to understand the world and its problems.
Measuring, analyzing and visualizing data is just the first step, because data
mining alone may not lead to a good understanding of unstable system
dynamics, which produces most of the good and bad surprises in the world.
In order to help us when we really need it, we must find explanatory
models, which can predict situations that haven't occurred before and
cannot be understood by extrapolation.
In fact, some "social mechanisms" influence human behavior in a similar
way to how the gravitational force determines planetary motion
. Just think
You can get in touch with us through
even though there is more diversity and randomness to human interaction (which mostly
of the way social norms determine our roles and behaviors.
The scientific
approach of "agent-based simulations" aims to formalize these rules and
turn them into computer codes. Complementary, the research field of
sociophysics tries to express the corresponding interactions and their
outcomes using mathematical formulas.
In this chapter, I will particularly
discuss the powerful concept of "social forces", which enables researchers
to understand the link between micro-level interactions of individuals and
the often unexpected macro-level outcomes in socio-economic systems.
The concept of forces is one of the main pillars of physics. In order to
discover it, the old geocentric worldview, in which the Earth was assumed
to be the center of the universe, first had to be replaced by the heliocentric
worldview, which recognized that all the planets in our solar system revolve
around the sun. Later, the new understanding of the planetary system
allowed Isaac Newton (1642-1727) to interpret measurement data about the
planets in a new way, which led him to formulate a simple and plausible
model of planetary motion based on the concept of gravitational forces.
Now, most concepts used in modern physics are formulated in terms of
forces and the way they influence the world. The predictive power of these
models is striking and has been impressively demonstrated by the Apollo
moon shot and every single satellite launch.
Another foundation of the success of physics is the tradition of building
instruments to measure the forces that would otherwise be imperceptible to
our senses. This has enabled physicists to explore a vast array of questions,
spanning from the early stages of our universe to the exploration of
elementary particles and the study of fundamental processes in biological
cells. Therefore, the next logical frontier of science is to build
"Socioscopes" that can reveal the hidden forces behind the dynamics of
socio-economic systems. In this way, we will eventually learn to understand
the counterintuitive behaviors of complex dynamical systems.
I believe
that we will soon be able to diagnose emergent societal problems such as
financial crashes, crime, or wars before they happen.
This will empower us
to avoid or mitigate these problems similarly to the way medical diagnostic
instruments have helped us to prevent or cure diseases. Isn't that an
exciting prospect?
Social forces between pedestrians
To demonstrate the feasibility of this vision, let me first discuss the
example of pedestrian and crowd dynamics to underline the usefulness of
force models in the social sciences. Starting in 1990, when I wrote my
benefits our economy and society)
diploma thesis,
I noticed that pedestrian paths around obstacles looked
similar to the streamlines of fluids. So, I decided to formulate a fluid-
dynamic theory of pedestrian flows. I derived it from a model for the
motion of individual pedestrians, which was inspired by Newton's force
FIGURE 3.2: Illustration of the various "social forces" acting upon a pedestrian.
This "social force model" assumes that the acceleration, deceleration an
directional changes of pedestrians can be approximated by the sum of a
number of different forces, each of which captures a specific desire or
"interaction effect". For example, each pedestrian likes to move with a
certain desired speed into a preferred direction of motion. This can be
represented by a simple "driving force", which captures how the person's
velocity is gradually adapted. Moreover, each pedestrian seeks to avoid
collisions and to respect a certain personal "territory" of others. This is
reflected by a "repulsive interaction force" between pedestrians which
increases with proximity. Repulsive interactions with walls or streets can be
described by similar forces. The attraction of tourist sites and the tendency
for friends and family members to stay together can be represented by
"attractive forces".
Finally, a random force may be used to reflect the
individual behavioral variability.
Despite its simplicity, computer simulations of this model match many
empirically observed phenomena surprisingly well. For example, it is
possible to understand the emergence of river-like flow patterns through a
standing crowd of people, the wave-like progression of individuals waiting
in queues, or the lower density of people on a dance floor compared to the
surrounding spectators watching them.
I would like to thank Mehdi Moussaid for providing this graphic.
Self-organization of unidirectional lanes in pedestrian counter-
There are also various self-organization phenomena that lead to
fascinating collective patterns of motion. For example, when people enter a
corridor on two sides, we observe the formation of lanes of unidirectional
(see Fig. 3.3). That is, people walking in opposite directions
automatically coordinate each other so that they are hardly obstructed by
the respective counter-flow. This makes transit more efficient for everyone.
While it seems as if the "invisible hand" is at work here, we can actually
explain how social order is created and how collectively desirable outcomes
occur from local interactions: whenever an encounter between two
pedestrians occurs, the repulsive interaction force between them pushes the
pedestrians a bit to the side. These interactions are more frequent between
opposite directions of motion, due to the higher relative velocity. This is the
main reason why people walking in opposing directions tend to separate
into lanes of unidirectional flow. To explain the phenomenon, we don't
need to assume that pedestrians prefer to walk on a certain side of the
In conclusion, complexity science can explain the formation of
lanes of pedestrians as a so called "symmetry breaking" phenomenon,
which occurs when a mixture of different directions of motion becomes
FIGURE 3.3: Illustration of the formation of lanes of uniform walking direction in
pedestrian counterflows.
Walking through a "wall" of people without stopping
Surprisingly, the very same force model also reproduces a number of
other interesting findings in pedestrian crowds, such as oscillatory changes
in the flow direction of pedestrians at bottlenecks. Therefore, when a crowd
of people builds up at a junction, the pressure tends to be relieved
alternately on either side of the bottleneck. Another example of self-
organization is the amazing phenomenon of "stripe formation", which
allows pedestrians to cross another pedestrian stream without having to
stop (see Fig. 3.4). This is almost akin to walking through a wall! Using the
social force model, it's possible to understand how this is possible. The
formation of stripes which occur for similar reasons as the lanes discussed
before allows pedestrians to move forward with the stripes and sideways
within stripes that are forming in the intersecting pedestrian flow. In
combination, this enables a continuous collective motion through a
pedestrian stream moving in another direction.
FIGURE 3.4: Illustration of the phenomenon of "stripe formation" in two crossing
pedestrian flows.
Measuring forces
In physics, forces are experimentally determined by measuring the
trajectories of particles, especially changes in their speed and direction of
motion. It would be natural to do this for pedestrians, too. At the time
when we developed the social force model for pedestrians, I could not
imagine that it would ever be possible to measure social forces
experimentally. But a few years later, we actually managed to do this. In
around 2006, the advent of powerful video camera and processing
technologies put my former PhD student, Anders Johansson, into the
position to detect and analyze the trajectories of pedestrians from filmed
footage. Using this data, we adapted the parameters of the social force
model in such a way that it optimally reproduced the trajectories of the
observed pedestrians.
In 2006/07, similar tracking methods became
important for the analysis of dense pedestrian flows and the avoidance of
crowd disasters.
Later, in 2008, Mehdi Moussaid and Guy Theraulaz set up a pedestrian
experiment in Toulouse, France, under well-controlled lab conditions.
This finally allowed us to perform data-driven modeling. While before, we
had to make assumptions about the functional form of pedestrian
interactions, it then became possible to determine the functional
dependencies directly from the wealth of tracking data generated by the
pedestrian experiment. After fitting the social force model to individual
pedestrian data, it was finally used to simulate the flows of many
pedestrians. To our excitement, the computer simulations yielded a
surprisingly accurate prediction of the pedestrian flows observed in a wide
pedestrian walkway.
So, pedestrian modeling can be considered a great success of
sociophysics. One can say that, over time, pedestrian studies have turned
from a social to a natural science, bringing theoretical, computational,
experimental and datadriven approaches together. This has even led to
practical and surprising lessons for the design of pedestrian facilities and for
the planning of largescale public events such as the annual pilgrimage in and
around Mecca, as we will discuss now.
Most pedestrian facilities are inefficient
Back in 1994/95, Peter Molnar and I compared a range of different
designs of pedestrian facilities. Surprisingly, we found that obstacles, if
properly placed, can make pedestrian counter-flows more efficient (see Fig.
3.5). In fact, all of the conventional design elements of pedestrian facilities
such as corridors, bottlenecks, and intersections turn out to be ill-designed
and can be considerably improved! In many cases, "less is more" in that
providing less space for pedestrians can produce a better flow. This
surprising discovery can be best understood for bottlenecks such as doors.
Here, a funnel-shaped design can reduce disturbances in the pedestrian
flow, which otherwise result when the directions of motion are not well
enough aligned (e.g. when some people approach the door from the front
and others from the side).
In the case of busy bi-directional pedestrian flows, the efficiency of
motion can be improved by a series of pillars in the middle. These pillars
help to stabilize the interface between the opposite flow directions, thereby
reducing disturbances. The effectiveness of the design becomes particularly
clear in subway tunnels, where pedestrians move both ways and pillars exist
for static reasons.
FIGURE 3.5: Illustration of conventional and improved elements of pedestrian
Finally, an obstacle in the middle of a pedestrian intersection may also
improve the flow. When Peter Molnar and I discovered this, it took us
some time to understand this unexpected finding. Eventually we noticed
that, at intersections, many different collective patterns of motion can
emerge, for example, clockwise or counter-clockwise rotary flows, or
oscillatory patterns of the crossing flows. The problem is that the different
collective patterns of motion conflict with each other, so that none of them
are stable. Putting a column in the center increases the likelihood of rotary
flows and thereby increases the overall efficiency of pedestrian traffic. The
flow can be improved even more by replacing a four-way intersection with
four bidirectional intersections, which can be achieved by placing railings in
suitable locations. This encourages a rotary flow pattern, which greatly
reduces disturbances.
Crowd disasters
Unfortunately, pedestrian flow doesn't always self-organize in an
efficient way. Sometimes, terrible crowd disasters happen, and hundreds of
people may be injured or killed, even when everyone has peaceful
intentions and does not behave aggressively. How is this possible?
When I got interested in the problem in 1999, crowd disasters were
often regarded as "acts of God" similar to natural disasters that are beyond
human control. However, the root cause of the breakdown of social order
in crowds is similar to the reason behind "phantom traffic jams". If the
density of people gets too high, the flow of pedestrians becomes unstable.
The resulting crowd dynamics can be uncontrollable for individual people
and even for hundreds of security guards. Nevertheless, crowd disasters can
be avoided, if their causes are well enough understood and proper
preparations are made.
Crowd disasters have happened since at least Roman times. That's why
building codes were developed for stadiums, as exemplified by the
Coliseum in Rome. The Coliseum had 76 numbered entrances and could
accommodate between 50,000 and 73,000 visitors, who would exit through
the same gate through which they had entered. These rules and the
generous provision of exits meant that the Coliseum could be evacuated
within five minutes. Modern stadiums, which generally have a smaller
number of exits, can rarely match this performance.
Despite the frequent and tragic occurrences of crowd disasters in the
past, they continue to happen due to common misperceptions. Media
reports often suggest that crowd disasters occur when a crowd panics,
causing a stampede in which people are crushed or trampled. Therefore,
crowd disasters are claimed to be the result of unreasonable or aggressive
behavior, with some individuals pushing others relentlessly as they try to
escape. But why would people panic? My colleague Keith Still put it like
this: "People don't die because they panic, they panic because they die."
In fact, studies that I conducted with Illes Farkas, Tamas Vicsek, Mehdi
Moussaid, Guy Theraulaz and others revealed that many crowd disasters
have physical rather than psychological causes.
They may occur even if
everybody behaves reasonably and tries not to harm anyone else. Therefore,
the common view that crowd disasters are mostly a result of panic is
outdated. I don't negate that people are in a danger to be crushed when the
inflow of people into a spatially constrained area exceeds the outflow for an
extended period of time. Certainly, a high density crowd can become life-
threatening under such conditions, as more and more people accumulate in
too little space. However, most of the time crowd disasters occur for a
different reason.
For example, during the annual Muslim pilgrimage around Mecca in
2006, a crowd disaster occurred on a large plaza. Anders Johansson and I
were asked to evaluate video footage of this accident. Initially, due to the
high density of pilgrims, we could not see anything else than very slow
motion, a few centimeters per second. However, when I asked Anders to
play the videos 10 or even 100 times faster, we made some surprising
FIGURE 3.6: Time-lapse photograph of stop-and-go flows in dense pedestrian crowds.
The accelerated videos showed some striking phenomena. First, we
observed an unexpected, sudden transition from smooth pedestrian flows
to stop-and-go flows (see the long-term photograph above).
In contrast to
highway traffic, however, these stop-and-go waves were previously
unknown and unlikely to result from delayed adaptation. Eventually, we
discovered that these waves were caused by a competition of too many
pedestrians for too few gaps in the crowd, i.e. by a coordination problem.
The stop-and-go movement emerged when the overall flow suddenly
dropped to lower values, similar to the capacity drop phenomenon in
vehicle traffic. As a consequence, the outflow from the area drastically
decreased, while the inflow stayed the same. Thus, the density increased
quickly, but while this certainly created a dangerous situation, it was not the
ultimate cause of the tragedy!
FIGURE 3.7: Illustration of the phenomenon of crowd turbulence under
extremely crowded conditions.87
To our further surprise, some minutes later we witnessed another
unexpected transition from stop-and-go flows to a phenomenon that we
call "crowd turbulence" (see Fig. 3.7).
In this situation, people were
pushed around in random ways. So, Anders Johansson and I discovered
that it was not the density, but the density multiplied by the variability of
speed the so-called "crowd pressure" which triggered the disaster at a
certain time and location. Moreover, we found that any body movement
even unintentional could exert a force on the bodies of pedestrians in the
immediate neighborhood, when the crowd density exceeded a certain
critical threshold. These forces could add up from one body to the next,
meaning that the resulting force quickly changed in strength and direction.
As a consequence, people were pushed around in unpredictable and
uncontrollable ways.
It was just a matter of time until someone lost balance, stumbled, and
fell to the ground. This produced a "hole" in the crowd, which unbalanced
the forces acting on the surrounding people, because the counter-force
from where the person stood before was missing. Therefore, the
surrounding people tended to fall on top of those that had previously fallen
or they were forced to step on them. The situation ended with many people
piled up on top of each other, suffocating the people on the ground. Similar
observations were made in other crowd incidents, such as the Love Parade
disaster in Duisburg, Germany, for example.
Countering crowd disasters
Can we use the above knowledge to avoid crowd disasters in future?
The answer is yes! Some years back, together with several colleagues, I
became involved in a project aiming to improve the flow of pedestrians
during the annual Muslim pilgrimage to Mecca. We were asked to find a
better way of organizing the crowd movements around the Jamarat Bridge,
a focal point of the pilgrimage, where thousands of pilgrims had died in the
past due to a number of tragic crowd disasters. How could we avoid them?
This was a challenge that was not simply related to technical matters
such as crowd densities. We also needed to take dozens of religious,
political, historical, cultural, financial and ethical factors into account. Our
previous experience of modeling crowds led us to propose a range of
measures including the counting of crowds through a newly developed
video analysis tool, the implementation of time schedules for groups of
pilgrims, re-routing strategies for crowded situations, contingency plans for
possible incidents, an awareness program to inform pilgrims in advance
about the procedures during the Hajj, and an improved information system
to guide millions of pilgrims who spoke 200 different languages.
implementing many of our proposals, the Hajj (in 1427H) was indeed
performed safely without any incident in 2007.
The principle which
underpinned the success of this approach was to gather real-time
information and suitably adapt to it.
Since then, the principle of providing real-time feedback to crowds has
become a trend. An interesting example is an app to improve the safety of
mass events, which was developed by Paul Lukowicz, a member of the
FuturICT project, and a number of other scientists such as Ulf Blanke.
using this app, festivalgoers at a number of festivals in London, Vienna and
Zurich voluntarily provided GPS data about their locations, which was used
to determine their speeds and directions. This data was then returned to the
festivalgoers to give them a picture of the areas which they should better
avoid due to over-crowding.
Forces describing opinion formation and other behaviors
Is the usefulness of the concept of social forces restricted to pedestrian
flows (and traffic flows
), or can it also be applied to various other kinds of
social phenomena such as crime and conflict as well? The success of force
models in describing pedestrian flows is related to the fact that pedestrians
are moving continuously in space. Therefore, the dynamics of a pedestrian
can be represented by an equation of motion, which states that the change
of his/her spatial position over time is given by their velocity. An additional
equation expresses that the change in velocity over time (i.e. the
acceleration) can be modeled by a sum of forces. But can we also
understand how people form opinions or other behavioral changes based
on social forces? Surprisingly, the answer is "yes", if the changes in opinion
are more or less gradual on a continuous opinion scale or in a continuous
opinion space.
Otherwise, generalized models would have to be used,
which exist as well.
After formulating the social force model for pedestrians in Göttingen,
Germany, in 1990, I joined Professor Wolfgang Weidlich's team at the
University of Stuttgart. He was probably the only physicists working on
socio-economic modeling at that time. So, Professor Weidlich might be
seen as grandfather of sociophysics.
When I joined his team, my plan at
this time was to learn how to model opinion formation and decision-
making. Since my work on pedestrians, I had the idea that both individual
In the meantime, three further levels of the new Jamarat Bridge were completed, and the
organization of the pilgrim flows was changed several times. However, since 2007 I haven't
been involved in this anymore. To the best of my knowledge, the Hajj was safe as long as the
recommendations of the international expert panel were approximately implemented. It also
seems that the international expert panel, which is now in charge, has not been responsible
for the sad crowd disaster in 2015.
and collective human behavior could be understood as a result of social
forces, and I formulated a corresponding theory (see Appendix 3.1).
Interestingly, it is possible to develop social force models for migration,
too, if one assumes that people relocate within a certain (not too large)
geographic range. A model that I formulated in 2009 examines "success-
driven migration".
According to this, individuals try to avoid locations
where they expect bad outcomes and are attracted to locations that appear
to be favorable. Bad neighborhoods (in which people were uncooperative)
were found to have a repulsive effect, whereas good neighborhoods (where
people were cooperative) attracted migrants. It is even possible to calculate
the direction and strength of this repulsion and attraction effects, i.e. the
forces which imply the average direction and speed of motion in a certain
In general, a great advantage of using the concept of "social forces" is
that it can help us to develop a better idea of the complex processes
underlying social change. Movements towards a subject or object are
reflected by attractive forces, while movements away from a subject or
object are reflected by repulsive forces. It is also important to recognize that
such forces may not be attributable to individuals, but rather to groups of
individuals, companies or institutions. In other words, social forces may be
a collective effect. Group dynamics or "group think", as a result of the
emergence of a particular group identity is probably a good example of this.
individuals creates a collective "group" perspective, which in turn changes
the opinion and behavior of individuals. In fact, the theory of social milieus
posits that the behavior of individuals is largely influenced by their social
environment. Very soon, it will be possible to quantify the underlying forces
and to derive mathematical formulas for them. But what is more powerful,
physical or social forces?
Culture: More persistent than steel
It has often been claimed that civilizations were born out of war, and
that the world is ultimately ruled by those with the greatest military power.
However, I don't buy this. Even though war certainly played a role in
establishing the modern world, I believe the main mechanisms underlying
the spreading of civilization are migration and the exchange of goods and
ideas. Today, the Internet can certainly advance civilizations in ways that
don't need to be paid by human lives.
But what is the basis of civilizations? It's culture, and to a large extent,
culture is the result of numerous sets of rules, such as social conventions,
values, norms, roles, and routines.
These rules determine the success or
failure of societies and guide their evolution. Just take religious values for
instance, which can determine the behavior of millions of people for
thousands of years. It is not an exaggeration, therefore, to say that culture is
more persistent than steel
and probably also more relevant to the success
of civilizations than weapons.
In other words, social forces can be stronger
than physical forces. A good example of this is ancient Greek culture,
which spread to the Roman occupants because it was more advanced.
Reducing conflict
While we all learned about physical forces at school, very few people
have an explicit understanding of the social forces, which determine the
behavior of socio-economic systems. This has to change, if we want to
overcome or at least mitigate the problems we are faced with. Conflicts,
wars and revolutions can be understood as a result of, too. Certain forces
can destabilize systems and cause them to disintegrate. There are at least
three types of conflict situations: (1) An encounter (say, between two
countries) causes losses on both sides. This might be avoided by increasing
the awareness of the likely outcomes of such an encounter in advance. (2)
The encounter is beneficial to one party but unfavorable to the other, and
causes damage overall. Here, the second party needs to be protected from
exploitation (e.g. through solidarity from third parties, or by separating the
disputing parties). (3) The encounter is advantageous for one side and
undesirable for the other, but the overall outcome is positive. In such
situations, the benefits can be redistributed to make the interaction
beneficial for both sides, i.e. it's possible to align interests to create a win-
win situation.
Would it also be possible to actually measure the forces creating
conflict? Yes, I think one can build a ConflictMap, which illustrates regional
and international tensions and explains how they come about. In fact, when
working in my team, Thomas Chadefaux mined millions of news articles
over a period of more than 100 years and performed a sentiment analysis
for words indicating conflict. This allowed him to quantify the level of
tension between countries in the world. Moreover, he could show that the
level of tension could be used to predict the likelihood of outbreaks of war
within a 6-12 months time period.
Such advance warning signals might
give politicians enough time to engage in diplomacy to peacefully resolve
the tensions before it's too late. Our analyses also revealed how tension
spreads from one country to the next, destabilizing a huge region, as it
It is surprising enough that one can cut steel with light (i.e. a mighty laser beam), but the
persistence of culture is even more astonishing.
even though protection from aggression and exploitation is needed, of course.
I have recently proposed "Social (Information) Technologies" that aim to reduce the
occurrence of conflict between people and companies.
happened after the war in Iraq. This might also have produced fertile
ground for the rise of the Islamic State (IS).
What we can learn from Jerusalem
Another data-driven study analyzed a problem that has worried the
world for many decades: the conflict in the Middle East. Why haven't we
been able to end this conflict yet? A classical Big Data approach, even if we
knew the trajectories of all the bullets shot, couldn't really reveal the causes
of this conflict. Nevertheless, it is possible to understand the roots of the
conflict. A few years ago, I got involved in a study with Ravi Bhavnani, Dan
Miodownik, Maayan Mor, and Karsten Donnay, which lead to an
empirically grounded, agent-based model.
Our model suggests that
intercultural distance is the main driver of the conflict.
A further analysis reveals that violent events are correlated with each
So, there is a responsive dynamic, whereby each side retaliates for
previous attacks by the other side.
For example, Palestinians retaliate to
Israeli violence and vice versa. What does this tell us? Basically, both sides
punish each other for violence that they suffered before. It seems that each
party tries to send the message: "Stop being violent to us or you will have to
pay a high price!" From the point of view of rational choice theory, this
should stop the chain of violence. As one event triggers another, usually
bigger one, or even several, the conflict becomes increasingly costly for
both sides over time. However, rather than creating peace, a deadly spiral of
violence sets in. An Israeli documentary film entitled "The Gatekeepers",
which interviewed previous chiefs of the secret service, came to a
remarkable conclusion: "We have won every battle, but we are losing the
war." In other words, it doesn't pay to be violent, quite the opposite!
So why does such retaliation cause an escalation rather than a calming of
hostilities? This occurs because both sides think their actions are right. In
fact, they are applying sanctioning mechanisms that are intended to create
social order, but these mechanisms are only suited for a mono-cultural
Punishment doesn't always work
To understand the problem better, we must ask the question: "Why do
we punish others?" This has a simple reason: we have learned that
punishment can establish and stabilize social norms. Therefore, who doesn't
follow our norms is usually sanctioned. But such punishment is only
effective, if it is accepted by the punished party. Otherwise this party will
strike back and inflict revenge, which escalates the conflict. Therefore, it is
important to recognize that punishment is only effective if people share the
same values, norms and culture.
In multi-cultural settings, punishment is often not suited to create social
order. Under such circumstances, however, it might be possible to reduce
the level of conflict by physically separating the opposing parties so that
they live in different areas.101 Another option is to develop a culture of
tolerance, understanding and respect. In fact, as we will see later, there are
many social mechanisms that foster social order, such as reputation
systems, for example. I am confident, therefore, that a deeper
understanding of the mechanisms and forces producing conflicts will
eventually help us to overcome or mitigate them. In a multi-cultural world,
I would strongly recommend to move away from a punitive culture.
Instead, it seems more promising to engage with each other in a
differentiated, reputation-based culture in which diversity is welcomed.
This brings us to another important set of invisible factors, which
determines the success of societies, namely "social capital".
Why "social capital" is so terribly important
Most of us have probably heard the proverb that "money makes the
world go round", but there are other, intangible factors that matter. For
example, human capital (like education) can boost individual careers, and
social capital can act like a catalyst of socio-economic success. But what is
social capital? I define it as everything that results from interactions within a
social network which could potentially (be used to) create benefits.
Examples include cooperativeness, public safety, a culture of punctuality,
reputation, trust, power, and respect. However, while our own actions
influence our social capital, we can't fully control it. This is in contrast to
money. In many cases, we can't buy social capital (or only to a limited
extent), but social capital creates added value.
Moreover, we cannot automatically generate a certain amount of social
capital by doing certain things. Similarly to reputation and respect, these
things are given to us by others. They depend on the effects of social
interactions. Note that the amount of social capital within a system also
determines how resilient the system is, and how likely it is to fail. Social
capital influences both the probability and extent of damage. This became
clear to me at a seminar at ETH Zurich's Risk Center,
when we discussed
the disproportional effect of large disasters on public opinion. Plane crashes
and terror attacks, for example, matter a lot to people, while they seem to
feel less threatened by everyday risks such as car accidents or fatalities
We will discuss this further in Chapter 8: How Society Works
The Risk Center brings together experts in probability theory and experts in complexity
and network theory.
caused by smoking. Therefore, it is often believed that "size matters", in the
sense that large disasters make people respond irrationally or even in panic.
However, having studied the phenomenon of panic for some time, I
came to a different conclusion. People realize that the damage is not just
physical in nature. Social capital can be damaged, too. For example, a large-
scale disaster often reduces public trust in the risk management of
companies or public authorities, particularly if it was caused by
unprofessional conduct or corruption. While people care about such things,
no insurance company covers damage to social capital.
Hence, we must protect social capital similarly to how we protect
economic capital or our environment. Social capital can be destroyed or
exploited, but this should be prevented. In order to do this, we must learn
to measure social capital and to quantify its value. Quantifying the value of
our environment also helped to protect it.
Trust and power
To stress the importance of social capital, it is important to acknowledge
that the financial crisis resulted from a loss of trust. Banks did not trust
other banks anymore and did not want to lend out money; customers did
not trust their banks anymore and emptied their bank accounts; banks did
not want to give loans to companies anymore; people did not want to invest
in financial derivatives anymore the list goes on. In the end, the resulting
financial meltdown cost an estimated $15 trillion at least.
So, trust is
highly valuable and when it erodes, the economic losses are tremendous. To
give another example, the recent loss of trust in US cloud storage
companies due to the revelations about mass surveillance by the National
Security Agency (NSA) has substantially reduced their business volume.
Trust is also the basis of power and legitimacy. When I studied in
Göttingen, Germany, a deadly car accident caused by a mistake of the
police triggered a large public outcry and massive demonstrations. This was
the first time when I noticed that public institutions can easily lose public
support. In other words, they can easily lose social capital. Trust is eroded
whenever authorities do something that is contrary to the moral beliefs of
the public. I made a similar observation in Zurich, Switzerland, when many
people complained about the policies of the migration office. During this
time, the windows of the migration office were repeatedly smashed in, but
when the director was replaced, the problem disappeared.
FIGURE 3.8: Illustration of how power results from trust and transparency.
It is very interesting that "soft" factors such as credibility and trust are
the basis of power! For example, the London Riots of 2011 occurred after a
person was shot by the police, but no sufficient justification was given to
the public. Something similar happened in 2014 in the city of Ferguson,
Missouri, and 2015 in the city of Baltimore, Maryland. In fact, riots in many
other countries were triggered by events where public authorities lost
legitimacy in the eyes of the public. The Arab spring, for example, started in
Tunisia, after Mohamed Bouazizi burnt himself to death to protest against
police corruption and brutality.
In other words, both legitimacy and power are contingent on the
authorities doing what the people regard to be "the right thing". If people
withdraw their support, authority and power vanish. While the state can
purchase weapons and, with this, acquire destructive power, constructive
power depends on the trust and support of the people. While weapons
might create fear, this is not a good substitute for genuine legitimacy. As the
situation becomes increasingly unacceptable, more and more people will
lose their fear, and start to resist the previously respected authorities. Some
may even be willing to sacrifice their own lives. Remember that many
extremists and terrorists previously led normal family lives. But even the
passiveness of citizens can make a country fail within just a few years. This
could be observed, for example, in the former German Democratic
Republic, which had a horrible surveillance system. For these reasons, I am
convinced that trust is the only sustainable basis of power and social
APPENDIX 3.1: Nervousnet: A decentralized digital nervous
The open standards of the World Wide Web (WWW) have unleashed a
digital economy worth many billion dollars, and participatory projects such