Implementing Change in a Complex World

Dirk Helbing
Computational Social Science @ ETH Zürich, Switzerland

Responding to complexity in socio-economic systems:
How to build a smart and resilient society?

The world is changing at an ever-increasing pace.

And it has changes in a much more fundamental way that one would think, primarily 
because it has become more connected and interdependent than in our entire history.

Every new product, every new invention 
can be combined with those that existed before,
thereby creating an explosion of complexity: structural complexity, 
dynamic complexity, functional complexity, and algorithmic complexity.

How to respond to this challenge?  And what are the costs?

The exponential increase in cybercrime 
is certainly just one of the undesirable effects.
It now causes damages of the order 
of 3 trillion dollars each year. (US$ 3 x 10ˆ12)

The financial crisis is another example.
Its damage is estimated to amount
to approximately 14 trillion dollars.  (US$ 1.4 x 10ˆ13)

The increase in the level of global terrorism
and international conflict is another problem
we must pay attention to.

There are further issues related with globalization,
such as climate change and international migration.

The vulnerability of energy supply 
and critical infrastructures
(e.g. by means of cyber warfare) produces 
further headaches, and global pandemics, too.

Many of the problems are caused by systemic instabilities,
which lead to outcomes that individual actors usually cannot control,
despite large amounts of data, advanced technology, 
and best efforts to keep everything under control.

The failure of control typically results from cascade effects,
where an incidental anomalous behavior of a system component
triggers anomalous behaviors of other system components and so on.

Depending on the details of the underlying dynamics,
the resulting damage may grow linearly or exponentially.

In particular cases, the damage may be even unbounded [1].

Many of humanity's unsolved problems
result from such cascading failures.

This provides a new perspectiva on problems ranging
from traffic jams over crowd disasters to financial collapse
and the spread of crime, terrorism, diseases, conflict and war.

As a consequence, understanding the nature 
of these problems opens up opportunities for new cures.

Recently, many experts have started to hope that Big Data
would help us fix the above problems, among others.

The idea is that more data would provide more insights,
and that knowledge could be turned into power,
thereby allowing one to fix the world's problems, perhaps 
even using an artificial «superintelligence» based on deep learning.

So, if one would measure everything and had access 
to all the data produced on our globe and massive computer power too,
could one optimize the course of the world in real time?

Could one rule the world like a wise king?

This now sounds like a fascinating and plausible perspective [2]?

While decision-making was often lacking enough information in the past,
Big Data is now offering interesting new perspective to manage and improve systems.

However, there are undesired side effects such as potential discrimination [3]
as well as the violation of privacy and undermining of trust [4].

In addition, more data does not necessarily imply better decisions,
as demonstrated by the well-known problems of over-fitting
(fitting to irrelevant features) and of spurious correlations
(identification of patterns that are meaningless).

Furthermore, when trying 
to separate good from bad risks,
classification errors are frequent.

In other words, no matter how much data 
are available, mistakes will be made.

But if strongly used, a powerful tool
can be very destructive, particularly
if one takes large-scale 
rather than minimally invasive measures.

Some of the international wars in the past years,
which did not have the intended results,
may serve as examples.

Systemic complexity causes additional problems.

Complex dynamical systems may be so sensitive to details
that it may not be possible to predict their behavior well
or even just to calibrate their parameters,
which relates to phenomena such as «sensitivity» and «chaos».

Moreover, algorithmic complexity may prevent 
an optimization (or even proper system analysis) in real time, 
i.e. even the biggest supercomputers of the world
may be too slow (and will probably always be).

Finally, as we go on networking the world,
systemic complexity increases even faster than 
data volumes and much faster than processing power.

Consequently, the controllability 
with centralized control approaches 
will decrease over time! [5]

Therefor, the crucial question is,
how to respond to the complexity challenge?

How to build resilient systems that are 
not prone to undesired cascade effects, 
but recover quickly and well from disruptions?

This is primarily a matter of systems designs and management.

Modularization is a well-known principle
to make the complexity of a system manageable.

This basically means that the organization of a system
is broken down into substructures or «units»,
between which there is a lower level of connectivity
or interaction as compared to the connectivity
within substructures to a manageable level.

Furthermore, it decreases interaction effects
between units and, with this, undesirable cascade effects.

In principle, of course, 
the modular units of a system
can be organized in a hierarchical way.

This can be efficient, when the units
(and the interactions between them, including 
information flows and chains of command)
work reliably, with very few errors.

However, as much as hierarchical structures
help to define accountability and to generate power,
control might already be lost if a single node 
or link in the hierarchy is dysfunctional.

This problem can be mitigates
by redundancies and decentralization.

In particular, 
if the dynamics of the system
is hard to predict, local autonomy 
can improve power adaptation,
as it is needed to produce 
solutions that fit local needs well.

More autonomy, of course, requires
the decision-makers to take more responsibility,
which calls for higher-level education and suitable tools,
in particular good information systems.

A further important principle that can 
often support  resilience is diversity.

The benefits of diversity are multifold.

First of all, 
diversity makes it more likely
that some units stay functional 
when the system is disrupted,
and that solutions for  many kinds of problems
already exist somewhere in the system when needed.

Second, diversity supports collective intelligence.

Third, the innovation rate 
typically grows with diversity, too.

However, diversity also poses challenges,
as we know, for example, in intercultural interactions.

For this reason, interoperability is important.

I will come back to this issue below.

Finally, how can one control 
a complex dynamical system
in a distributed way?

This can be done using the principle 
of (guided) self-organization [6.7].

In complex systems, 
where many system components 
respond to each other in nonlinear ways,
the outcome is often the emergence
of macro-level structures, properties and functions.

The kind of outcome depends, of course,
on the details of these interactions.

But modifying the interactions allows one to let 
other structures, properties, and functions emerge.

The disciplines needed 
to find the right kinds of interactions 
to obtain a desirable outcome are called 
«complexity science» and «mechanism design».

Even with simple local interactions,
it is possible to generate a surprisingly rich spectrum
of often complex structures, properties and functions.

One particularly favorable feature of self-organization
is that the resulting structures, properties and functions
occur by themselves and very efficiently,
by using the forces within the system
rather than forcing the system to behave
in a way that is against «its nature».

But how to determine suitable interaction rules
to let a system produce a certain desired outcome?

There are different possibilities.

Computer simulations allow one 
to study the self-organization 
of complex dynamical systems
in a computer, if the interactions are 
simple enough and well enough defined.

Otherwise, to get an idea what outcomes
the interactions of real human beings might produce,
one can perform lab experiment or web experiments
using Amazon Mechanical Turk.

Furthermore, interactive online games 
have become a tool for the exploration
of socio-economic interactions.

it will worth identifying societies
and their resilience to disruptions.

most of these mechanisms
are not explicitly known,
but are "internalized" subconsciously.

If they were known, 
however, we could combine 
the many success mechanism
of the world's cultures in new ways.

Interactions produce «externalities»,
i.e. external effects, but these can
usually be changed by introducing
or modifying feedback loops in the system.

Such feedbacks allow the system components
to adapt to the local conditions in ways
that restore the normal functionality.

In economic systems,  feedback mechanisms 
are often produced by financial costs or rewards,
while in social systems it is common 
to use incentives and sanctions [8].

However, certain kinds of information exchange
and coordination mechanisms are even more efficient
(«altruistic signaling», for instance) [7].

It is also important to consider
that one kind of feedback mechanism
(such as money) is usually too restricted
to let a complex socio-economic system
self-organize, and therefore
a multi-dimensional value exchange system
is needed, as I have recently proposed it [9].

In fact, many chemicals or pharmaceutical drugs
cannot be produced by controlling a single variable
such as the concentration of a particular ingredient.

Instead, one needs to control the temperature,
pressure, and concentrations of many ingredients.

In a similar way, our body would not do well,
if we increase the quantity of just one substance,
e.g. the amount of water we drink.

We need to have enough carbon hydrates, 
proteins, vitamins and minerals as well.

Therefor, to create a better working economy,
the establishment of a multidimensional 
value exchange system is inevitable.

The multi-dimensional value exchange system
would be best built on the externalities 
that matter, i.e. all the in- and outputs.

Desirable outputs would be represented
by positive numbers («gains») 
in a specific dimension related
to that particular kind of output,
and undesirable ones
by negative numbers («losses»).

Desirable inputs would be represented
by negative numbers («costs»),
and undesirable inputs should be avoided.

In other words, to enable a self-organizing economy,
externalities must be measured in real-time
to allow for real-time feedbacks,
and those feedbacks would be created
by the multi-dimensional value exchange system I propose.

Interestingly, the real-time measurement of externalities
becomes increasingly possible now, thanks to
the spread of the «Internet of Things», 
i.e. of networks of sensors that can 
communicate with each other in a wireless way.

For this purpose, my collaborators and I
have recently proposed to build
a participatory information platform
as a Citizen Web, which we call
the «Planetary Nervous System» [10].

With this enabling technology
we can finally make the «invisible hand» work.

That is, 300 years after its invention,
we can perform the measurement of externalities
and feed them back on the decision-making entities
(people, institutions, companies, or even algorithms) 
in such a way that efficient AND desirable outcomes are produced.

For example, one can build assistant systems
to dissolve traffic jams or produce fluent traffic flows in cities.

One could also build an assistant system 
to stabilize global supply chains 
and thereby reduce the bullwhip effects 
that would otherwise 
produce booms and recessions.

Furthermore, one could build digital assistant systems
to support cooperation and avoid conflict [6].

These «Social Technologies» would help one
to ensure favorable outcomes of interactions for all sides.

In fact, interactions between two entities
(be it people, companies, or institutions)
can basically have four possible outcomes:

(1) If an interaction would be lossful for both entities,
as it is often the case in conflicts and wars,
the interaction should be avoided.

(2) If the interacton would be favorable for one side,
but bad for the other and lossful overall,
the interaction should also be avoided,
and to ensure this, the second entity must be 
protected from exploitation by the first one.

(3) If the interaction would again be favorable
for one side and bad for the other, but positive overall,
it can be turned into a win-win situation
by means of a value transfer.

(4) Finally, if the interaction would be beneficial
for both sides, one should engage in it,
but one might decide to share the overall benefits
in a fairer way by means of a value exchange.

Digital assistants could support us in all these situations.

They could help to create situational awareness,
including the potential side effects and risks 
implied by certain decisions and (inter)actions.

Without such assistants, we would 
certainly overlook many opportunities
for beneficial interactions
we could actually engage in.

Digital assistants could also help us
to organize protection against exploitation,
which would otherwise deteriorate
the overall state of the system.

And finally, 
Social Technologies could support us 
with multidimensional value exchange,
as I discussed it before.

Social Technologies can assist us particularly
in avoiding the systemic instabilities
that I discussed in the beginning of this chapter
as the main source of our unsolved problems.

This might also include digital assistance
to «tragedies of the commons» such as
environmental exploitation, overfishing,
or global climate change.

In summary, instabilities in complex systems
and the often resulting large-scale cascading failures
are the underlying reasons for some of
the greatest unsolved problems in the world.

They result from wrong system designs
and management approaches,
which lead to uncontrollable outcomes,
despite massive accounts of data,
modern technology, and best intentions.

However, a paradigm shift
in the way we are creating 
and managing these systems
could solve our problems.

One would mainly have to engage
in a distributed systems approach,
characterized by modular designs,
distributed control, and self-organization.

This also applies to our entire economy [11].

Diversity is another relevant ingredient,
which is important for resilience, 
innovation, and collective intelligence.

However, in the past we have often 
had difficulties to handle diversity.

Digital assistants can support us in this, 
such that we will become increasingly able
to reap the benefits of diversity,
which is also a key factor
of economic success and social well-being.

Finally, we have not made 
sufficient use of the success principles 
underlying the diverse cultures in the world.

This can now be changed.

But to make various systems interoperable
and to produce favorable outcomes of interactions,
one needs to measure the diverse externalities,
and feed them back by means of a 
multi-dimensional value exchange system.

The «Planetary Nervous System»
is the enabling technology for this.

Combined with the insights of complexity science,
this will finally allow us to let the «invisible hand»
work for us, creating a self-organization 
of complex dynamical systems that produces
the systemic structures, properties and functions we want [12].

Further Reading

 [1] D. Helbing, Globally networked risks and how to respond.
      Nature 497, 51-59 (2013).

 [2] D. Helbing,  Crystal ball and magic wand - The dangerous promise of Big Data,

 [3] D. Helbing, Big Data Society: Age of reputation or age of discrimination?,

 [4] D. Helbing, Thinking Ahead: Essays on Big Data, Digital Revolution,
      and Participatory Market Society (Springer, 2015).

 [5] D. Helbing, The World after Big Data: What the digital revolution means for us,

 [6] D. Helbing, Guided self-organization - Making the invisible hand work,

 [7] D. Helbing, How Society Works - Social order by self-organization,

 [8] D. Helbing, Networked Minds - Where human evolution is heading?

 [9] D. Helbing, Qualified Money - A better financial system for the future,

[10] D. Helbing, Creating («making») a Planetary Nervous System as a Citizen Web,

[11] D. Helbing, Economy 4.0 and Digital Society - The Participatory Market Society is born,

[12] D. Helbing, The Planetary Nervous System - A CERN Society,

[Siempre está la posibilidad que países, instituciones o individuos, sean capaces
de manejar grandes volúmenes de datos, y puedan aprovechar las estrategias
que se proponen en la serie de trabajos que respaldan este breve ensayo
(+ otras iniciativas complementarias desde una variedad de autores)
para operar no desde el círculo virtuoso que aquí se propone,
sino para beneficio propio solamente, perjuicio ajeno, o para socavar
estos cambios de paradigma, utilizando las mismas u otras herramientas
que vayan surgiendo).

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