by Dirk Helbing
This is second in a series of blog posts that form chapters of my forthcoming book Digital Society. Last week's chapter was titled: GENIE OUT OF THE BOTTLE: The digital revolution on its way.
Financial
crises, terrorism, conflict, crime: it turns out, the conventional ‘medicines’
to tackle global problems are often inefficient or even counter-productive. The
reason for this is surprisingly simple: we approach these problems with an outdated
understanding of our world. While the world might still look similar to how it
has looked for a long time, I will argue that it has, in fact, inconspicuously
but fundamentally changed over time.
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. However, we are increasingly threatened by another kind of problems: those that come from within the system, such as financial instabilities, economic crises, social and political unrest, organized crime and cybercrime, environmental change, and spreading diseases. These threats have become some of our greatest worries. According to the World Economic Forum's Risk Map, the largest risks today are of a socio-economic nature such as inequality or governance failure. These global 21st century problems cannot be solved with 20th century wisdom, because they are of a different scale and result from a new level of complexity in today's socio-economic systems. We must therefore better understand what complex systems are, and what are their properties. To this end, I will discuss the main reasons why things go wrong: unstable dynamics, cascading failures in networks, and systemic interdependencies. I will illustrate these problems by examples such as traffic jams, crowd disasters, blackouts, financial crises, crime, wars, and revolutions.
Phantom traffic jams
Complex
systems include phenomena ranging from turbulent flows and the global weather
system to decision-making, opinion formation in groups, financial and economic markets,
and the evolution and spread of languages. But we must take care to distinguish
complex systems from complicated ones. A car is complicated: it consists of
thousands of parts, yet is easy to control (when it works properly). Traffic
flow, on the other hand, which depends on the interactions of many cars, is a
complex dynamical system, which produces counter-intuitive, individually
uncontrollable behaviors such as "phantom traffic jams" that seem to
have no cause. While many
traffic jams do occur for a specific, identifiable reason, such as an accident or
a building site, everyone has also encountered situations where a vehicle queue
appeared "out of nothing" – and where there is no visible cause - see visualisation
To explore the true reasons for
these phantom traffic jams, Yuki Sugiyama from the Nagoya University in Japan and
his colleagues carried out an experiment, in which they asked many people to
drive their cars around a circular track - see visualisation The task sounds
simple, and indeed all vehicles moved smoothly for some time. But then a random
perturbation in the traffic flow, an unexpected slow-down of a car, triggered
the appearance of “stop-and-go” traffic – a traffic jam that travelled
backwards around the track, against the driving direction.
While we often blame others for poor
driving skills to explain such "phantom traffic jams," studies in complexity
science have shown that they rather emerge as a collective phenomenon
unavoidably resulting from the interactions between vehicles. A detailed
analysis shows that, if the density of cars exceeds a certain "critical"
threshold – that is, if their average separation is smaller than a certain
value – then the smallest perturbation in the speed of any car will be
amplified to cause a breakdown of the entire flow. Because drivers need some
time to respond to such a disturbance, the next driver in line will have to brake
harder to avoid an accident. Then the following driver will have to break even
harder, and so on. This chain reaction amplifies the small initial perturbation
and eventually produces the jam – which of course every individual would prefer
to avoid.
Recessions - traffic jams in the world economy?
Economic supply chains might
exhibit a similar kind of behavior. As known from John Sterman's "beer distribution
game," supply chains are also hard to control. Even experienced managers
will often end up ordering too much beer, or will run out of it. This is a
situation that is as difficult to avoid as stop-and-go traffic. In fact, our scientific
work suggests that economic recessions may be regarded as a kind of traffic jam
in the global supply network (see figure below). This is actually somewhat
heartening news, since it implies that, just as with traffic flow, engineered
solutions may exist that can mitigate economic recessions, provided that we have
access to real-time data on the world's supplies and materials flows. Such
solutions will be discussed later in the chapter on Socio-Inspired
Technologies.
Instability and self-organization in strongly interacting systems
A shocking example for systemic
instabilities discussed later is the occurrence of crowd disasters. Here, even
when everyone is peacefully minded and tries to avoid harming others, many
people might die. What do all these examples tell us? Our experience will often
not inform us well, and our intuition may fail, since complex dynamical systems
tend to behave in unexpected or even counter-intuitive ways. Such systems are
typically made up from many interacting components, which respond to the
behavior of other system components. As a consequence of these interactions,
complex dynamical systems tend to self-organize, i.e. to develop a collective
behavior that is different from what the components would do in separation. Then,
the components’ individual properties are often no longer
characteristic for the system. "Chaotic"
or "turbulent" dynamics are possible outcomes, but complex systems
can show many other phenomena.
When
self-organization occurs, one often speaks of emergent phenomena that are
characterized by new system properties, which cannot be understood from the
properties of the single components. For example, the facts that water feels
wet, extinguishes fires, and freezes at a particular temperature are
properties, which cannot be understood from the properties of single water
molecules.
As
a consequence of the above, we have to shift our attention from the components
of our world to their interactions. In other words, we need a change from a
component-oriented to an interaction-oriented, systemic view, which is at the
heart of complexity science. I claim that this change in perspective, once it
becomes common wisdom, will be of similar importance as the transition from the
geocentric to the heliocentric worldview. The related paradigm shift has
fundamental implications for the way in which complex techno-socio-economic
systems must be managed and, hence, also for politics and our economy. Focusing
on the interactions in a system and the multi-level emergent dynamics resulting
from them, opens up fundamentally new solutions to long-standing problems.
Instability is one possible behavior
of complex dynamical systems, which results when the characteristic system
parameters cross certain critical thresholds. If a system behaves unstable,
i.e. perturbations are amplified, a random, small deviation from the normal system
state may trigger a domino effect that cannot be stopped, even if people have
the best intentions to do so and have enough information, good technology, and
proper training. In such situations of systemic instability, the system will
inevitably get out of control sooner or later, no matter how hard we try to
avoid this. As a consequence, we need to know the conditions under which
systems will behave in an unstable way, in order to avoid such conditions. In many
cases, too strong interactions are a recipe for disaster or other undesirable
outcomes.
Group
dynamics and mass psychology may be seen as typical examples of collective
dynamics. People have often wondered what makes a crowd turn "mad",
violent, or cruel. After the London riots in the year 2011, people asked how it
was possible that teachers and daughters of millionaires – people one would not
expect to be criminals – were participating in the lootings. Did they become
criminal minds when their demonstrations against police violence suddenly turned
into riots? Possibly, but not necessarily so. In the above traffic flow
example, people wanted to do one thing: drive continuously at reasonably high
speed, but a phantom traffic jam occurred instead. We found that, while
individual cars are well controllable, the traffic flow – a result of the
interactions of many cars – is often not. The take home message may be
formulated as follows: complex systems
cannot be steered like a car. Even if everyone has the latest technology, is well-informed and
well-trained, and has the best intentions, an unstable complex system will
sooner or later get out of control.
Therefore, while our intuition
works well for weakly coupled systems, in which the system properties can be
understood as sum of the component properties, complex dynamical systems behave
often in counter-intuitive, hardly predictable ways. Frequently, the
collective, macro-level outcome in a complex system can't be understood from and
controlled by the system components. (Such system components might also be
individuals or companies, for example.)
Beware of strongly coupled systems
Thus,
what tends to be different in strongly coupled systems as compared to weakly
interacting ones? First, the dynamics of strongly connected systems with
positive feedbacks is often faster. Second, self-organization and strong
correlations tend to dominate the dynamics of the system. Third, the system
behavior is often counter-intuitive – unwanted feedback or side effects are
common. Conventional wisdom tends to fail. In particular, extreme events occur
more often than expected, and they may impact the entire system. Furthermore,
the system behavior can be hard to predict, and planning for the future may not
be useful. Opportunities for external control are also typically quite limited,
as the system-immanent interactions tend to dominate. Finally, the loss of
predictability and control may lead to an erosion of trust in private and public
institutions, which in turn can create social, political, or economic
instabilities.
In
spite of all this, many people still have a component-oriented and
individual-centric view, which can be quite misleading. We praise heroes when
things run well and search for scapegoats when something goes wrong. But the
discussion above has shown how difficult it is for individuals to control the
outcome of a complex dynamical system, if its components' interactions are
strong. This fact may be illustrated by the example of politics. Why do politicians,
besides managers, have among the worst reputations among all professions? This
is probably because we vote them to make politics according to the positions
they publicly voice, but then we often find them doing something else. This,
again, is a consequence of the fact that politicians are exposed to many strong
interactions due to lobbyists and pressure groups with various points of view.
Each one is trying to push the politician in a different direction. In many
cases, this will force the politician to take a decision that is not compatible
with his or her own points of view, which is hard for the voters to accept.
Managers of companies find themselves in similar situations. But not only they:
think of the decision-dynamics in many families. If it were easy to control, we
would not see so many divorces...
Crime
is another good example for unwanted outcomes of complex dynamics, even though a
controversial one. We must ask ourselves: Are we interested in sustaining
social order, or are we interested in filling prisons? If we decide for the
first option, we must confront ourselves with the question: Should we really
see all crime as deeds of criminal minds, as we often do? Or should we pay more
attention to the circumstances that happen to cause crime? In cases, where individuals
plan crimes such as the theft of a famous diamond, the conventional picture of
crime is certainly appropriate. But do these cases give a representative
picture?
Classically,
it is assumed that crimes are committed, if the expected advantage is larger
than the punishment, multiplied with the probability of being convicted.
Therefore, raising punishments and discovery rates should theoretically
eliminate all crime. Such punishment would make crime a lossful experience and,
therefore, "unattractive." However, empirical evidence questions this
simple picture. On the one hand, people usually don't pick pockets, even though
they could often get away without a 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, rates of various crimes, including
homicides, are still much higher. So, what is wrong with our common
understanding of crime?
Surprisingly,
many crimes, including murders, are committed by average people, not by people
with criminal careers. A closer inspection shows that many crimes result from
situations, over which the involved individuals lose their control. Frequently,
group dynamics plays an important role, and many scientific studies indicate
that the socio-economic context is 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 am
saying this also with an eye on the price we have to pay for this: A single
prisoner costs more than the salary of a postdoctoral researcher with a PhD
degree, some even more than a professor!
Cascade effects in complex networks
Making
things worse, complex systems may show further problems besides dynamic
instabilities based on amplification effects. Thanks to globalization and
technological progress, we have now a global exchange of people, goods, money,
and information. Worldwide trade, air traffic, the Internet, mobile phones, and
social media have made everything much more comfortable – and connected. This
has created many new opportunities, but everything now depends on a lot more
things. What are the implications of this increased interdependency? Today, a
single tweet can send stock markets to hell. A youtube movie can trigger a riot that kills dozens of people. Our
decisions can have impacts on the other side of the globe more easily than ever
– and sometimes unintentionally so. For example,
today’s quick spreading of emerging epidemics is largely a result of global air
traffic, and can seriously affect global health, social welfare, and economic
systems.
By networking our world, have we
inadvertently built highways for disaster spreading? In
2011 alone, three major cascading failures occurred, which are changing the
face of the world and the global balance of power: The financial
crisis, the Arab spring and
the combined earthquake, tsunami and nuclear disaster in
Japan. In the following, I will 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. Before the line was switched off, of course, a computer
simulation was performed to verify that the power grid would still operate
well. But the scenario analysis did not check for the coincidence of a spontaneous
failure of another line. In the end, a local overload of the grid caused
emergency switch-offs in the neighborhood, creating a cascade effect with
pretty astonishing outcomes: some blackouts occurred in regions thousands of
kilometers away, while other areas in the neighborhood were not affected at
all. Is it possible to understand this strange behavior?
Indeed, a computer-based simulation study of the
European power grid recently managed to reproduce such effects. It demonstrated
that the failure of a few network nodes in Spain could create a surprising blackout
in Eastern Europe, several thousand kilometers away, while the electricity
network in Spain would still work - see visualisation
Furthermore, increasing the capacities of certain parts of the power grid would unexpectedly make things worse. The cascading failure would be even bigger! Therefore, weak elements in the system have an important function: they act as circuit breakers, thereby interrupting the failure cascade. This is an important fact to remember.
Furthermore, increasing the capacities of certain parts of the power grid would unexpectedly make things worse. The cascading failure would be even bigger! Therefore, weak elements in the system have an important function: they act as circuit breakers, thereby interrupting the failure cascade. This is an important fact to remember.
Bankruptcy cascades
The sudden financial meltdown in 2008 is another
example, which 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 Barnenke, as chairman of the Federal
Reserve Board, long believed that the economy was well understood, and doing
well. In September 2007, Ric
Mishkin, a professor at Columbia Business School and then a member of the Board
of Governors of the US Federal Reserve System, made a 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 conditions."
As we all
know, things came very different. A banking crisis occurred only shortly later.
It started locally, with the bursting of a real estate bubble in the West of
the USA. Because of this locality, most people thought this problem was easy to
contain. But the mortgage crises had spill-over effects to the stock markets,
where certain financial derivatives could not be sold anymore (now called
"toxic assets"). Eventually, more than 400 banks all over the United
States went bankrupt. How could this happen? The video presents an impressive visualisation
of the bankruptcies of banks in the USA after Lehman Brothers collapsed. Apparently,
one bank's default triggered further ones, and these triggered even more. In
the end, hundreds of billion dollars were lost.
The
above video reminds of another video which I often use to illustrate cascade
effects: It
shows an experiment with many table tennis balls placed on top of mouse traps. The
experiment demonstrates impressively that a single local perturbation can mess
up the entire system. It illustrates chain reactions, which are the basis of
atomic bombs or of nuclear fission reactors. As we know, such cascade effects are
technologically controllable in principle, if we stay below the critical
interaction strength (sometimes called the "critical mass"). Nevertheless,
these processes can sometimes get out of control, mostly in unexpected ways.
The nuclear disasters in Chernobyl or in Fukushima are well-known examples for
this. So, we must be extremely careful with systems showing cascade effects.
The financial crisis
As
we know, the above-mentioned cascading failure of banks was just the beginning
of an even bigger crisis. It subsequently caused an economic crisis and a public
spending crisis in major areas of the world. Eventually, the events even
threatened the stability of the Euro currency and the European Union. The
crisis brought several countries (including Greece, Ireland, Portugal, Spain,
Italy and the US) at the verge of bankruptcy. As a consequence, many countries
have seen historical heights in unemployment rates. In some countries, more
than 50 percent of young people do not have a job. In many regions, this has
caused social unrests, political extremism and increased crime and violence.
Unfortunately, it seems that the cascade effect has not 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 has even 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 from hindsight, the
lack of advance understanding by conventional wisdom becomes clear by the
following quote from November 2010, going back to the former president of the
European Central Bank, Jean-Claude Trichet:
"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.”
Leading scientists as well had
problems making sense of the crisis. In a letter dated 22 July 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, nobody was responsible for the financial mess? I don't want to judge,
but we should remember that it's often not possible to point the finger at the exact
person who caused a phantom traffic jam. Therefore, given that these are
collectively produced outcomes, do we have to accept collective responsibility
for them? And how to enumerate everyone's share of responsibility? 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, before
the crash, I followed the stock markets pretty closely, as I noticed strong price
fluctuations, which I interpreted as "critical fluctuations," i.e. an
advance warning signal of an impending financial crash. Therefore, I sold my
stocks in the business launch of an airport in 2007, while waiting for the
departure of my airplane. In spring 2008, about half a year before the collapse
of Lehman brothers, I wrote an article together with Markus Christen and James
Breiding, taking a complexity science perspective on the financial system. We
came to the conclusion that the financial system was in a process of
destabilization. Pretty much as Andrew Haldane, Chief Economist and Executive
Director at the Bank of England, formulated it later, we believed that the
increased level of complexity in the financial system was a major problem. It
made the financial system more vulnerable to cascade effects than most
experts thought.
In spring 2008, we were so worried about this that we felt we had to alert the
public, but none of the newspapers we contacted was ready to publish our essay
at that time. "Too complicated for our readers" was the response,
while we replied "if you cannot make this understandable to your readers,
then there is nothing that can prevent the financial crisis." And so the
financial crisis came! Six month after the crisis, a manager of McKinsey in the
United Kingdom commented on our analysis that it was the best he had ever seen.
But there were much more prominent
people who saw the financial crisis coming. For
example, legendary investor Warren Buffet warned of mega-catastrophic risks
created by large-scale investments into 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, causing losses of trillions of dollars to our economy.
Fundamental uncertainty
In liquid financial markets and
many other hardly predictable systems such as the weather, we can still determine
the probability of events, at least approximately. Thus, we make a
probabilistic forecast similar to: "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, etc." It is then possible to
determine the expected loss (or gain) implied by the likely actions and events.
For this purpose, the damage or gain of each possible event is multiplied with
its probability, and the numbers are added up to give the expected damage or
gain. In principle, one could do this for all actions we might take, in order
to determine the one that minimizes the damage or maximizes the gain. The only
problem involved in this exercise seems to be the practical determination of
the probabilities and of the likely damages or gains involved. With the
increasing availability of data, this problem might, in fact, be attacked, but it
will remain difficult or impossible to determine the probabilities of
"extreme events," as the empirical basis for rare events is too small.
It turns out, however, that there
are problems where the expected damage in large (global) systems cannot be
determined at all for principal
reasons. Such "fundamental" or "radical" uncertainty can
occur in case of cascade effects, where one failure is likely to trigger other
failures, and where the damage related to subsequent events times their
likelihood is increasing. In such cases, the sum of losses may be unbounded, in
principle, i.e. it may not be possible anymore to enumerate the expected loss.
In practice, this means that the actual damage can be small, big, or practically
unbounded, where the latter might lead to the collapse of the entire system.
Explosive pandemic outbreaks
The threat by cascade effects
might be even worse if the damage occurring in an early phase of the cascade
process reduces the probability of resisting failures that are triggered later.
A health system, in which financial or medical resources are limited, may be
considered as an example for this. How will this system deal with emergent
diseases? A computer-based study that I performed together with Lucas Böttcher,
Nuno Araujo, Olivia Woolley Meza and Hans Hermann shows that the outcome very much
depends on the connectivity between people who may infect each other. A few
additional airline connections can make the difference between a case, where
the disease will be contained, and where it turns into a devastating global
pandemics. The problem is that crossing a certain connectivity threshold will
change the system dynamics dramatically and unexpectedly. Thus, have we built
global networks that behave in unpredictable and uncontrollable ways?
Systemic interdependencies
Recently,
Shlomo Havlin and others made a further important discovery: they revealed that
networks of networks can be particularly vulnerable to failures. A typical
example is the interdependency between electrical and communication networks. Another
example, which illustrates the global interdependencies between natural,
energy, climate, financial, and political systems is the following: In 2011,
the Tohoku earthquake in Japan caused a tsunami that triggered chain reactions
and nuclear disasters in several reactors at Fukushima. Soon after this,
Germany and Switzerland decided to exit nuclear power generation over the next
decade(s). However, alternative energy scenarios turn out to be problematic as
well. European gas deliveries depend on some regions, which we cannot fully rely
on. Likewise, Europe’s DESERTEC project, a planned 1000 billion Euro investment
into infrastructure to supply solar energy for Europe – has an uncertain future
due to another unexpected event, the Arab Spring. This was triggered by high
food prices, which were no longer affordable to many people. These high food
prices, in turn, resulted partly from biofuel production, which intended to
improve the global CO2 balance, but competed with food production.
The increasing food prices were further amplified by financial speculation. Hence,
the energy system, the political system, the social system, the food system,
the financial system – they have all become closely interdependent systems,
which makes our world ever more vulnerable to perturbations.
Have humans unintentionally created a "complexity time bomb"?
We have seen that, when systems are
too much connected, they might get out of control sooner or later, despite
advanced knowledge and technology, and best intentions to keep things under
control. Therefore, as we have created more and more links and interdependencies
in the world, we must ask ourselves: have humans inadvertently produced a "complexity
time bomb", a system that will ultimately get out of control?
For
a long time, problems such as crowd disasters and financial crashes have been
seen as puzzling, ‘God-given’ phenomena or "black swans" one had to
live with. However, problems like these should not be considered “bad luck.”
They are
often the consequence of a flawed understanding of counter-intuitive system
behaviors. While conventional thinking can cause fateful
decisions and the repetition of previous mistakes, complexity science allows us to understand the mechanisms that cause complex
systems to get out of control.
Amplification effects can result and promote failure
cascades, when the interactions of system components become stronger than the
frictional effects or when the damaging impact of impaired system components on
other components occurs faster than the recovery to their normal state. That
is, time scales of processes largely determine the controllability of a system
as well. Delayed adaptation processes are often responsible for systemic
instabilities and losses of control (see the related Information Box at the end).
For
certain kinds of networks, the similarity of related cascade effects with those
of chain reactions in nuclear fission is quite disturbing. Such processes are difficult
to control. Catastrophic damage is a realistic scenario. Therefore, given the
similarity of the cascading mechanisms, is it possible that our worldwide
anthropogenic system will get out of control sooner or later? When analyzing
this possibility, one must bear in mind that the speed of destructive cascade
effects might be slow, and the process may not appear like an explosion.
Nevertheless, the process may be hard to stop and lead to an ultimate systemic
failure. For example, the dynamics underlying crowd disasters is slow, but
deadly. So, what kinds of global catastrophic scenarios might we face in
complex societies? A collapse of the global information and communication
systems or of the world economy? Global pandemics? Unsustainable growth,
demographic or environmental change? A global food or energy crisis? A cultural
clash? Another global-scale war? A societal shift, driven by technological
innovations? Or, more likely, a combination of several of these contagious
phenomena? The World Economic Forum calls this the "perfect storm,"
and the OECD has formulated similar concerns.
Unintended wars and revolutions
Last but not least, it is
important to realize that large-scale conflicts, revolutions, and wars can also
be unintended results of systemic instabilities and interdependencies.
Interpreting them as deeds of historical figures personalizes these phenomena
in a way that distracts from their true, systemic nature. It is important to
recognize that complex systems such as our economy or societies usually resist
attempts to change them at large, namely when they are close to a stable
equilibrium. This is also known as Goodhart's law (1975), principle of Le
Chatelier (1850-1936), or as "illusion of control." Individual
factors and randomness can only have a large impact on the path taken by the complex
system, when the system is driven to a tipping point (also known as
"critical point"). In other words, instability is a precondition for
individuals to have a historical impact. For example, the historical sciences
increasingly recognize that World War I was pretty much an unintended, emergent
outcome of a chain reaction of events. Moreover, World War II was preceded by a
financial crisis and recession, which destabilized the German economic, social,
and political system. This finally made it possible that an individual could
become influential enough to drive the world to the edge.
Unfortunately, civilization is
vulnerable, and a large-scale war may happen again – I would say, it is even
likely. A typical unintended path towards war looks as follows: The resource
situation deteriorates, for example, because of a serious economic crisis. The resulting
fierce competition for limited resources lets competition, violence, crime, and
corruption rise, while solidarity and tolerance go down, so that the society is
fragmented into groups. This causes conflict, further dissatisfaction and
social turmoil. People get frustrated about the system, calling for leadership
and order. Political extremism emerges, scapegoats are searched, and minorities
are put under pressure. As a consequence, socio-economic diversity is lost, which
further reduces the economic success of the system. Eventually, the
well-balanced "socio-economic ecosystem" collapses, such that the
resource situation (the apparent "carrying capacity") deteriorates. This
destabilizes the system further, such that an external enemy is "needed"
to re-stabilize the country. Finally, nationalism rises, and war may seem to be
the only "solution" to keep the country together.
Note that a revolution, too, can
be the result of systemic instability. Hence, it does not need to be initiated
by an individual, "revolutionary" leader, who challenges an
established political system. The breakdown of the former German Democratic
Republic (GDR) and some Arab spring revolutions (for example, in Libya) have
shown that revolutions may start even without the existence of a clearly
identifiable political opponent leading the revolution. On the one hand, this
is the reason, why such revolutions cannot be stopped by targeting a few
individuals and sending them to jail. On the other hand, the absence of
revolutionary leaders has puzzled secret services around the world – the Arabic
spring took them by surprise. It was also irritating for sympathetic countries,
which could not easily provide support for democratic civil movements. Whom
should they have talked or given money to?
It provides a better picture to
imagine such revolutions as a result of situations, in which the interest of
government representatives and the people (or the interests of different
societal groups) have drifted away from each other. Similar to tensions created
by the drift of the Earth's tectonic plates, this would sooner or later lead to
an unstable situation and an "earthquake-like" stress release (the
"revolution"), resulting in a re-balancing of forces. Again, it is a
systemic instability, which allows individuals or small groups to become influential
eventually, while the conventional picture suggests that the instability of a
political regime is caused by a revolutionary leader. Putting it differently, a
revolution isn't usually the result of the new political leaders, but of the
politics that was made before, which destabilized the system. So, we should ask
ourselves, how well are our societies doing in terms of balancing the different
interests in our societies, and in terms of adapting to a quickly changing
world, due to demographic change, environmental change, technological change?
Conclusion
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
generate the innovations needed, before it's too late? The next chapters will
let you know...
Information Box: How harmless behavior can turn critical
In the traffic flow example and for the case of crowd disasters, we have seen that a system can get out of control when the interaction strength (e.g. the density) is too large.
How a change in density can turn harmless behavior of system components uncontrollable, is illustrated by the following example: Together with Roman Mani, Lucas Böttcher, and Hans J. Herrmann, I studied collisions in a system of equally sized particles moving in one dimension, similar to Newton's Cradle see video. 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 was large enough, each particle oscillated around its equilibrium point with normally distributed speeds, and all particles had the same small variance in speeds.
However, as the separation of equilibrium points approached the particle diameter, we found a cascade-like transmission of momentum between particles see video. Surprisingly, towards the boundary particles, the variance of speeds was rapidly increasing. In energy-conserving systems, the speed variance of the outer particles would even tend towards infinity with increasing system size. Due to cascading particle interactions, this makes their speeds unpredictable and uncontrollable, even though every particle follows a simple and harmless dynamics.
Information Box: Loss of Synchronization
There is another puzzling kind of systemic instability that is highly relevant for our societies, as many socio-economic processes accelerate. It occurs when the separation of time scales gets lost. For example, hierarchical systems in physics and biology are characterized by the fact that adjustment processes on higher hierarchical levels are typically much slower than on lower hierarchical levels. Therefore, lower level variables adjust quickly to the constraints set by the higher level ones, and that is why the higher levels basically control the lower ones. For example, groups tend to take decisions more slowly than the individuals forming them, and the organizations and states made up from them change even more slowly (at least it has been like this in the past).
Time scale separation implies that the system dynamics is determined by a few variables only, which are typically related to the higher hierarchy levels. Monarchies and oligarchies are good examples for this. In current socio-political and economic systems, however, we observe the trend that higher hierarchical levels show accelerating speeds of adjustment, such that the lower levels can no longer adjust more quickly than the higher levels. This may eventually destroy time scale separation, such that many more variables start to influence the system dynamics. The result of such mutual adjustment attempts on different hierarchical levels could be turbulence, "chaos," or a breakdown of synchronization. In fact, systems often get out of control, if the adjustment processes are not quick enough and responses to changed conditions are delayed.
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