ComplexityExplained_FRTranslation.txt

Complexity explained {#complexity-explained .unnumbered}
====================

La complexité Expliquée

"There's no love in a carbon atom, no hurricane in a water molecule, no
financial collapse in a dollar bill." (Peter Dodds) "Il n'y a aucun
amour dans un atome de carbone, aucun ouragan dans une molécule d'eau,
aucune crise financière dans un billet de banque." (Peter Dodds)

Complexity science, also called complex systems science, studies how a
large collection of components - locally interacting with each other at
the small scales - can spontaneously self-organize to exhibit
non-trivial global structures and behaviors at larger scales, often
without external intervention, central authorities or leaders. Les
sciences de la complexité, que l'on désigne aussi par sciences des
systèmes complexes, s'intéressent à la manière dont un large ensemble de
composants - qui interagissent localement entre eux aux échelles
microscopiques - peuvent spontanément s'auto-organiser pour induire des
structures globales et des comportements non triviaux aux échelles
macroscopiques, souvent sans intervention extérieure, autorités
centrales ou dirigeants.

The properties of the collection may not be understood or predicted from
the full knowledge of its constituents alone. Such a collection is
called a complex system and it requires new mathematical frameworks and
scientific methodologies for its investigation. Les propriétés de
l'ensemble peuvent ne pas être comprises ou prédites à partir de la
connaissance seule de ses constituents. Cet ensemble constitue alors un
système complexe, dont l'étude implique de nouvelles approches
mathématiques et de nouvelles méthodologies scientifiques.

Here are a few things you should know about complex systems. Voici un
certain nombre de notions essentielles autour des systèmes complexes.

Interactions
============

Interactions

COMPLEX SYSTEMS CONSIST OF MANY COMPONENTS INTERACTING WITH EACH OTHER
AND THEIR ENVIRONMENT IN MULTIPLE WAYS.

"Every object that biology studies is a system of systems." (François
Jacob)

Complex systems are often characterized by many components that interact
in multiple ways among each other and potentially with their environment
too. These components form networks of interactions, sometimes with just
a few components involved in many interactions. Interactions may
generate novel information that make it difficult to study components in
isolation or to completely predict their future. In addition, the
components of a system can also be whole new systems, leading to systems
of systems, being interdependent on one another.

The main challenge of complexity science is not only to see the parts
and their connections but also to understand how these connections give
rise to the whole.

Examples {#examples .unnumbered}
--------

Exemples

-   Billions of interacting neurons in the human brain

-   Computers communicating in the Internet

-   Humans in multifaceted relationships

Relevant Concepts {#relevant-concepts .unnumbered}
-----------------

Concepts

System, component, interactions, network, structure, heterogeneity,
inter-relatedness, inter-connectedness, interdependence, subsystems,
boundaries, environment, open/closed systems, systems of systems.

References {#references .unnumbered}
----------

Bibliographies

Mitchell, Melanie. Complexity: A Guided Tour. Oxford University Press,
2009. Capra, Fritjof and Luisi, Pier Luigi. The Systems View of Life: A
Unifying Vision. Cambridge University Press, 2016.

Emergence
=========

Emergence

Properties of complex systems as a whole are very different, and often
unexpected, from properties of their individual components. Les
propriétés des systèmes complexes pris dans leur ensemble sont très
différentes, et souvent inattendues, des propriétés de leur composants
individuels.

"You don't need something more to get something more. That's what
emergence means." (Murray Gell-Mann) "Il n'est pas nécessaire d'avoir
quelque chose en plus pour obtenir quelque chose en plus. C'est ce que
signifie le concept d'émergence." (Murray Gell-Mann)

In simple systems, the properties of the whole can be understood or
predicted from the addition or aggregation of its components. Dans des
systèmes simples, les propriétés de l'ensemble peuvent être comprises ou
prédites à partir de la superposition ou l'agrégation de ses composants.

In other words, macroscopic properties of a simple system can be deduced
from the microscopic properties of its parts. In complex systems,
however, the properties of the whole often cannot be understood or
predicted from the knowledge of its components because of a phenomenon
known as "emergence". This phenomenon involves diverse mechanisms
causing the interaction between components of a system to generate novel
information and exhibit non-trivial collective structures and behaviors
at larger scales.

En d'autres termes, les propriétés macroscopiques d'un système simple
peuvent être déduites des propriétés macroscopiques de ses composants.
Au contraire dans les systèmes complexes, les propriétés de l'ensemble
ne peuvent souvent pas être comprises ou prédites à partir de la
connaissance de ces composants, à cause d'un phénomène nommé
"émergence". Ce phénomène implique différents mécanismes induisant que
les interactions entre composants d'un système génèrent une information
nouvelle et présentent des structure collectives ou comportements
non-triviaux aux échelles supérieures.

This fact is usually summarized with the popular phrase "the whole is
more than the sum of its parts." Ce fait est souvent synthétisé par la
phrase célèbre: "le tout est plus que la somme des parties".

Examples {#examples-1 .unnumbered}
--------

Exemples

-   A massive amount of air and vapor molecules forming a tornado

-   Multiple cells forming a living organism

-   Billions of neurons in a brain producing consciousness and
    intelligence

<!-- -->

-   Une grande quantité de molécules d'eau et de vapeur formant une
    tornade

-   De nombreuses cellules formant un organisme vivant

-   Des milliards de neurones dans un cerveau produisant la conscience
    et l'intelligence

Relevant concepts {#relevant-concepts-1 .unnumbered}
-----------------

Concepts

Emergence, scales, non-linearity, bottom-up, description, surprise,
indirect effects, non-intuitiveness, phase transition, non-reducibility,
breakdown of traditional linear/ statistical thinking, "the whole is
more than the sum of its parts."

Émergence, échelles, non-linéarité, *bottom-up*, description, surprise,
effets indirects, contre-intuitif, transition de phase,
non-réductibilité, limite de la pensée traditionnelle
linéaire/statistique, "le tout est plus que la somme des parties".

References {#references-1 .unnumbered}
----------

Bibliographie

Bar-Yam, Yaneer. Dynamics of Complex Systems. Addison-Wesley, 1997.

Ball, Philip. Critical Mass: How One Thing Leads to Another. Macmillan,
2004.

Dynamics
========

Dynamiques

COMPLEX SYSTEMS TEND TO CHANGE THEIR STATES DYNAMICALLY, OFTEN SHOWING
UNPREDICTABLE LONG-TERM BEHAVIOR.

"Chaos: When the present determines the future, but the approximate
present does not approximately determine the future." (Edward Lorenz)

Systems can be analyzed in terms of the changes of their states over
time. A state is described in sets of variables that best characterize
the system.

As the system changes its state from one to another, its variables also
change, often responding to its environment.

This change is called linear if it is directly proportional to time, the
system's current state, or changes in the environment, or non-linear if
it is not proportional to them.

Complex systems are typically non-linear, changing at different rates
depending on their states and their environment.

They also may have stable states at which they can stay the same even if
perturbed, or unstable states at which the systems can be disrupted by a
small perturbation.

In some cases, small environmental changes can completely change the
system behavior, known as bifurcations, phase transitions, or "tipping
points."

Some systems are "chaotic" - extremely sensitive to small perturbations
and unpredictable in the long run, showing the so- called "butterfly
effect."

A complex system can also be path-dependent, that is, its future state
depends not only on its present state, but also on its past history.

Examples {#examples-2 .unnumbered}
--------

Exemples

-   Weather constantly changing in unpredictable ways

-   Financial volatility in the stock market

Relevant concepts {#relevant-concepts-2 .unnumbered}
-----------------

Concepts

Dynamics, behavior, non-linearity, chaos, non-equilibrium, sensitivity,
butterfly effect, bifurcation, long-term non-predictability,
uncertainty, path/context dependence, non-ergodicity.

References {#references-2 .unnumbered}
----------

Bibliographie

Strogatz, Steven H. Nonlinear Dynamics and Chaos. CRC Press, 1994.

Gleick, James. Chaos: Making a New Science. Open Road Media, 2011.

Self-organization
=================

Auto-organisation

Complex systems may self-organize to produce non-trivial patterns
spontaneously without a blueprint. Les systèmes complexes peuvent
s'auto-organiser pour produire spontanément des motifs non-triviaux,
sans architecture globale.

"It is suggested that a system of chemical substances, called
morphogens, reacting together and diffusing through a tissue, is
adequate to account for the main phenomena of morphogenesis." (Alan
Turing) "On peut suggérer qu'un système de substances chimiques, nommées
morphogènes, réagissant entre elles et se diffusant dans un tissu, est
approprié pour rendre compte du phénomène principal de la morphogenèse."
(Alan Turing)

Interactions between components of a complex system may produce a global
pattern or behavior. This is often described as self-organization, as
there is no central or external controller. Les interactions entre les
composants d'un système complexe peuvent produire un motif ou un
comportement pour l'ensemble. Ce phénomène est souvent décrit comme une
auto-organisation, car il n'est pas induit par un contrôle central ou
extérieur.

Rather, the "control" of a self-organizing system is distributed across
components and integrated through their interactions. Self-organization
may produce physical/functional structures like crystalline patterns of
materials and morphologies of living organisms, or dynamic/informational
behaviors like shoaling behaviors of fish and electrical pulses
propagating in animal muscles.

Au contraire, le "contrôle" d'un système auto-organisé est distribué
entre ses composants et intégré dans leurs interactions.
L'auto-organisation peut produire des structures
physiques/fonctionnelles comme les motifs cristallins des matériaux et
les morphologies des organismes vivants, ou bien des comportement
dynamiques/informationnels comme les comportement des bancs de poissons
et les impulsions électriques se propageant dans les muscles des
animaux.

As the system becomes more organized by this process, new interaction
patterns may emerge over time, potentially leading to the production of
greater complexity. Lorsque le système devient plus organisé par ce
processus, de nouvelles interactions peuvent émerger dans le temps, et
pouvant potentiellement conduire à la production d'une plus grande
complexité.

In some cases, complex systems may self-organize into a "critical" state
that could only exist in a subtle balance between randomness and
regularity. Dans certains cas, les systèmes complexes peuvent
s'auto-organiser en un état "critique" qui ne peut exister que dans un
équilibre subtil entre aléatoire et régularité.

Patterns that arise in such self-organized critical states often show
various peculiar properties, such as self-similarity and power-law
distributions of pattern properties.

Les motifs qui émergent dans de tels états critiques auto-organisés
présentent souvent des propriétés particulières, comme une
auto-similarité et des distributions en loi puissance des propriétés du
motif.

Examples {#examples-3 .unnumbered}
--------

Exemples

-   Single egg cell dividing and eventually self-organizing into complex
    shape of an organism

-   Cities growing as they attract more people and money

-   A large population of starlings showing complex flocking patterns

<!-- -->

-   Une unique cellule-oeuf de divisant et finalement s'auto-organisant
    en la forme complexe d'un organisme

-   Les villes qui croissent lorsqu'elles attirent plus d'individus et
    de flux économiques

-   Une grande population d'étourneaux décrivant des motifs complexes de
    mouvements collectifs

Relevant concepts {#relevant-concepts-3 .unnumbered}
-----------------

Concepts

Self-organization, collective behavior, swarms, patterns, space and
time, order from disorder, criticality, self-similarity, burst,
self-organized criticality, power laws, heavy-tailed distributions,
morphogenesis, decentralized/distributed control, guided
self-organization.

Auto-organisation, comportement collectif, essaims, motifs, espace et
temps, ordre émergeant du désordre, criticalité, auto-similarité,
explosion, criticalité auto-organisée, lois puissance, distributions à
grande queue, morphogenèse, contrôle décentralisé/distribué,
auto-organisation guidée.

References {#references-3 .unnumbered}
----------

Bibliographie

Ball, Philip. The Self-Made Tapestry: Pattern Formation in Nature.
Oxford University Press, 1999.

Camazine, Scott, et al. Self-Organization in Biological Systems.
Princeton University Press, 2003.

Adaptation
==========

Adaptation

COMPLEX SYSTEMS MAY ADAPT AND EVOLVE.

"Nothing in biology makes sense except in the light of evolution."
(Theodosius Dobzhansky)

Rather than just moving towards a steady state, complex systems are
often active and responding to the environment - the difference between
a ball that rolls to the bottom of a hill and stops and a bird that
adapts to wind currents while flying. This adaptation can happen at
multiple scales: cognitive, through learning and psychological
development; social, via sharing information through social ties; or
even evolutionary, through genetic variation and natural selection.

When the components are damaged or removed, these systems are often able
to adapt and recover their previous functionality, and sometimes they
become even better than before. This can be achieved by robustness, the
ability to withstand perturbations; resilience, the ability to go back
to the original state after a large perturbation; or adaptation, the
ability to change the system itself to remain functional and survive.
Complex systems with these properties are known as complex adaptive
systems.

Examples {#examples-4 .unnumbered}
--------

Exemples

-   An immune system continuously learning about pathogens

-   A colony of termites that repairs damages caused to its mound

-   Terrestrial life that has survived numerous crisis events in
    billions of years of its history

Relevant concepts {#relevant-concepts-4 .unnumbered}
-----------------

Concepts

Learning, adaptation, evolution, fitness landscapes, robustness,
resilience, diversity, complex adaptive systems, genetic algorithms,
artificial life, artificial intelligence, swarm intelligence,
creativity, open- endedness.

References {#references-4 .unnumbered}
----------

Bibliographie

Holland, John Henry. Adaptation in Natural and Artificial Systems. MIT
press, 1992.

Solé, Ricard, and Elena, Santiago F. Viruses as Complex Adaptive
Systems. Princeton University Press, 2018.

Interdisciplinarity
===================

Interdisciplinarité

Complexity science can be used to understand and manage a wide variety
of systems in many domains.

Les sciences de la complexité peuvent être appliquée pour comprendre et
superviser une grande variété de systèmes dans de nombreux domaines.

"It may not be entirely vain, however, to search for common properties
among diverse kinds of complex systems...The ideas of feedback and
information provide a frame of reference for viewing a wide range of
situations." (Herbert Simon)

"Il n'est peut-être pas totalement vain de cependant chercher des
propriétés communes au sein de divers types de systèmes complexes...Les
idées de retroaction et d'information fournissent un cadre de référence
pour lire une grande variété de situations." (Herbert Simon)

Complex systems appear in all scientific and professional domains,
including physics, biology, ecology, social sciences, finance, business,
management, politics, psychology, anthropology, medicine, engineering,
information technology, and more. Many of the latest technologies, from
social media and mobile technologies to autonomous vehicles and
blockchain, produce complex systems with emergent properties that are
crucial to understand and predict for societal well-being.

Les systèmes complexes se rencontrent dans tous les domaines
scientifiques et professionnels, incluant la physique, la biologie,
l'écologie, les sciences sociales, la finance, les affaires, la gestion,
la politique, la psychologie, l'anthropologie, la médecine,
l'ingénierie, les technologies de l'information, et d'autres. De
nombreuses technologies parmi les plus récentes, des réseaux sociaux et
technologies mobiles aux véhicules autonomes et à la blockchain,
produisent des systèmes complexes avec des propriétés émergentes qu'il
est crucial de comprendre et prédire pour le bien-être sociétal.

A key concept of complexity science is universality, which is the idea
that many systems in different domains display phenomena with common
underlying features that can be described using the same scientific
models. These concepts warrant a new multidisciplinary
mathematical/computational framework. Un concept clé des sciences de la
complexité est l'universalité, qui est l'idée que de nombreux systèmes
dans différents domaines présentent des phénomènes avec des
caractéristiques sous-jacentes communes qui peuvent être décrites en
utilisant les mêmes modèles scientifiques. Ces concepts justifient un
nouveau cadre mathématique/computationnel multidisciplinaire.

Complexity science can provide a comprehensive, cross-disciplinary
analytical approach that complements traditional scientific approaches
that focus on specific subject matter in each domain. Les sciences de la
complexité peuvent fournir une approche analytique complète et à cheval
entre les disciplines, qui complémente les approches scientifiques
traditionnelles qui se concentrent sur des objets d'étude spécifiques
dans chaque domaine.

Examples {#examples-5 .unnumbered}
--------

Exemples

-   Common properties of various information- processing systems
    (nervous systems, the Internet, communication infrastructure)

-   Universal patterns found in various spreading processes (epidemics,
    fads, forest fires)

Relevant concepts {#relevant-concepts-5 .unnumbered}
-----------------

Concepts

Universality, various applications, multi-/
inter-/cross-/trans-disciplinarity, economy, social systems, ecosystems,
sustainability, real-world problem solving, cultural systems, relevance
to everyday life decision making.

References {#references-5 .unnumbered}
----------

Bibliographie

Thurner, Stefan, Hanel, Rudolf and Klimek, Peter. Introduction to the
Theory of Complex Systems. Oxford University Press, 2018

Page, Scott E. The Model Thinker. Hachette UK, 2018.

Methods
=======

Méthodes

MATHEMATICAL AND COMPUTATIONAL METHODS ARE POWERFUL TOOLS TO STUDY
COMPLEX SYSTEMS.

"All models are wrong, but some are useful." (George Box)

Complex systems involve many variables and configurations that cannot be
explored simply with intuition or paper-and-pencil calculation. Instead,
advanced mathematical and computational modeling, analysis and
simulations are almost always required to see how these systems are
structured and change with time.

With the help of computers, we can check if a set of hypothetical rules
could lead to a behavior observed in nature, and then use our knowledge
of those rules to generate predictions of different "what-if" scenarios.
Computers are also used to analyze massive data coming from complex
systems to reveal and visualize hidden patterns that are not visible to
human eyes.

These computational methods can lead to discoveries that then deepen our
understanding and appreciation of nature.

Examples {#examples-6 .unnumbered}
--------

Exemples

-   Agent-based modeling for the flocking of birds

-   Mathematical and computer models of the brain

-   Climate forecasting computer models

-   Computer models of pedestrian dynamics

Relevant concepts {#relevant-concepts-6 .unnumbered}
-----------------

Concepts

Modeling, simulation, data analysis, methodology, agent-based modeling,
network analysis, game theory, visualization, rules, understanding.

References
----------

Bibliographie

Pagels, Heinz R. The Dreams of Reason: The Computer and the Rise of the
Sciences of Complexity. Bantam Books, 1989.

Sayama, Hiroki. Introduction to the Modeling and Analysis of Complex
Systems. Open SUNY Textbooks, 2015.

"I think the next \[21st\] century will be the century of complexity."
(Stephen Hawking)