1. Introduction — a.
What are complex systems?
Many agents, complicated rules, complex emergent behavior natural ex: organisms (cells), societies (individuals + techniques) agent rules become more “complicated”, e.g., heterogeneous depending on the element’s type and/or position in the system behavior is also complex but, paradoxically, can become more controllable, e.g., reproducible and programmable biological development & evolution
termite mounds
Fall 2015
companies
techno-networks
René Doursat: "Complex Systems Made Simple"
cities
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1. Introduction — a.
What are complex systems?
From “statistical” to “morphological” social insects: complex collective constructions systems ant trail
cells: biological morphogenesis
inert matter: pattern formation
network of ant trails
termite mound
architectures cells without architects! termites
Fall 2015
ant nest
ants
René Doursat: "Complex Systems Made Simple"
grains of sand + warm air
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1. Introduction — a.
What are complex systems?
Many agents, complicated rules, complex emergent behavior ex: self-organized “artificial life”: swarm chemistry, morphogenesis in swarm chemistry (Sayama 2007), mixed self-propelled particles with different flocking parameters create nontrivial formations in embryomorphic engineering (Doursat 2006), cells contain the same genetic program, but differentiate and self-assemble into specific shapes
PF4
PF6
SA4
SA6
PF SA
Fall 2015
Swarm chemistry
Embryomorphic engineering
Hiroki Sayama, Binghamton University SUNY
René Doursat, Insitut des Systèmes Complexes, Paris
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1. Introduction — a.
What are complex systems?
Many agents, complicated rules, “deterministic” behavior classical engineering: electronics, machinery, aviation, civil construction
artifacts composed of a immense number of parts yet still designed globally to behave in a limited and predictable (reliable, controllable) number of ways "I don’t want my aircraft to be creatively emergent in mid-air"
not "complex" systems in the sense of: little decentralization no emergence no self-organization Fall 2015
Systems engineering Wikimedia Commons, http://en.wikipedia.org/wiki/Systems_engineering
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1. Introduction — a.
What are complex systems?
Many agents, complicated rules, “centralized” behavior spectators, orchestras, military, administrations people reacting similarly and/or simultaneously to cues/orders coming from a central cause: event, leader, plan hardly "complex" systems: little decentralization, little emergence, little self-organization
Fall 2015
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1. Introduction — a.
What are complex systems?
Recap: complex systems in this course
Fall 2015
Category
Agents / Parts
Local Rules
Emergent Behavior
A "Complex System"?
2-body problem
few
simple
“simple”
NO
3-body problem, few low-D chaos
simple
complex
NO – too small
crystal, gas
many
simple
“simple”
NO – few params
patterns, swarms, complex networks
many
simple
“complex”
YES – but mostly
structured morphogenesis
many
complicated
complex
YES – reproducible
machines, crowds with leaders
many
complicated
deterministic/ centralized
COMPLICATED
René Doursat: "Complex Systems Made Simple"
suffice to describe it
random and uniform
and heterogeneous
– not self-organized 20
1. Introduction — a.
What are complex systems?
Recap: complex systems in this course
Fall 2015
Category
Agents / Parts
Local Rules
Emergent Behavior
A "Complex System"?
2-body problem
few
simple
“simple”
NO
3-body problem, few low-D chaos
simple
complex
NO – too small
crystal, gas
many
simple
“simple”
NO – few params
patterns, swarms, complex networks
many
simple
“complex”
YES – but mostly
structured morphogenesis
many
complicated
complex
YES – reproducible
machines, crowds with leaders
many
complicated
deterministic/ centralized
COMPLICATED
René Doursat: "Complex Systems Made Simple"
suffice to describe it
random and uniform
and heterogeneous
– not self-organized 21
1. Introduction — a.
What are complex systems?
Complex systems in this course
large number of elementary agents interacting locally
(more or less) simple individual agent behaviors creating a complex emergent, self-organized behavior decentralized dynamics: no master blueprint or grand architect
physical, biological, technical, social systems (natural or artificial) pattern formation = matter
insect colonies = ant Fall 2015
the brain & cognition = neuron
biological development = cell
Internet & Web = host/page René Doursat: "Complex Systems Made Simple"
social networks = person 22
A Complex Systems Sampler Emergence on multiple levels of self-organization complex systems: a) a large number of elementary agents interacting locally b) simple individual behaviors creating a complex emergent collective behavior c) decentralized dynamics: no master blueprint or grand architect
Fall 2015
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A Complex Systems Sampler From genotype to phenotype, via development
Fall 2015
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A Complex Systems Sampler NetLogo “Fur”
From cells to pattern formation, via reaction-diffusion
ctivator nhibitor
Fall 2015
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A Complex Systems Sampler NetLogo “Ants”
From social insects to swarm intelligence, via stigmergy
Fall 2015
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A Complex Systems Sampler NetLogo “Flock”
From birds to collective motion, via flocking
separation
Fall 2015
René Doursat: "Complex Systems Made Simple"
alignment
cohesion
27
A Complex Systems Sampler From neurons to brain, via neural development . . .
Ramón y Cajal 1900
Fall 2015
. . .
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1. Introduction — a.
What are complex systems? Categories of complex systems by agents
the brain biological patterns
living cell
ant trails termite mounds
cells
molecules
physical patterns Internet, Web
Fall 2015
organisms
animal flocks
animals cities, populations
humans & tech markets, economy
social networks
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1. Introduction — a.
What are complex systems? Categories of complex systems by range of interactions
the brain
organisms
ant trails termite mounds
biological patterns
animal flocks
living cell
physical patterns
2D, 3D spatial range Internet, Web
Fall 2015
non-spatial, hybrid range
markets, economy
cities, populations
social networks
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1. Introduction — a.
What are complex systems? Natural and human-caused categories of complex systems
the brain
organisms
ant trails termite mounds
biological patterns
living cell
physical patterns
... yet, even human-caused systems are “natural” in the sense of their unplanned, spontaneous emergence Internet, Web
Fall 2015
markets, economy
animal flocks cities, populations
social networks
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1. Introduction — a.
What are complex systems? Architectured natural complex systems (without architects)
the brain
organisms
ant trails
biological patterns
living cell
physical patterns
biology strikingly demonstrates the possibility of combining pure self-organization and elaborate architecture Internet, Web
Fall 2015
markets, economy
termite mounds animal flocks cities, populations
social networks
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1. Introduction — a.
What are complex systems? Human superstructures are "natural" CS by their unplanned, spontaneous ... arising from a multitude of emergence and adaptivity... traditionally designed artifacts geography: cities, populations people: social networks wealth: markets, economy technology: Internet, Web small to midscale artifacts
large-scale emergence
Fall 2015
computers, routers
houses, buildings address books companies, institutions computers, routers
companies, institutions
address books
houses, buildings
Architects overtaken by their architecture cities, populations
Internet, Web
markets, economy
social networks
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Complex Systems Made Simple 1.
Introduction a.
What are complex systems?
b.
A vast archipelago
c.
Computational modeling
2.
A Complex Systems Sampler
3.
Commonalities
4.
NetLogo Tutorial
Fall 2015
• • • •
Common properties Related disciplines Big questions big objects Science engineering links
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Common Properties of Complex Systems Emergence the system has properties that the elements do not have these properties cannot be easily inferred or deduced different properties can emerge from the same elements
Self-organization “order” of the system increases without external intervention originates purely from interactions among the agents (possibly via cues in the environment)
Counter-examples of emergence without self-organization ex: well-informed leader (orchestra conductor, military officer) ex: global plan (construction area), full instructions (program) Fall 2015
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Common Properties of Complex Systems Positive feedback, circularity creation of structure by amplification of fluctuations (homogeneity is unstable) ex: termites bring pellets of soil where there is a heap of soil ex: cars speed up when there are fast cars in front of them ex: the media talk about what is currently talked about in the media
Decentralization the “invisible hand”: order without a leader ex: the queen ant is not a manager ex: the first bird in a V-shaped flock is not a leader
distribution: each agent carry a small piece of the global information ignorance: agents don’t have explicit group-level knowledge/goals parallelism: agents act simultaneously Fall 2015
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Common Properties of Complex Systems NOTE Decentralized processes are far more abundant than leader-guided processes, in nature and human societies ... and yet, the notion of decentralization is still counterintuitive many decentralized phenomena are still poorly understood a “leader-less” or “designer-less” explanation still meets with resistance this is due to a strong human perceptual bias toward an identifiable source or primary cause Fall 2015
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1. Introduction — b.
A vast archipelago
Precursor and neighboring disciplines complexity: measuring the length to describe, time to build, or resources to run, a system
adaptation: change in typical functional regime of a system
systems sciences: holistic (nonreductionist) view on interacting parts
dynamics: behavior and activity of a system over time
multitude, statistics: large-scale properties of systems
different families of disciplines focus on different aspects Fall 2015
(naturally, they intersect a lot: don’t take this taxonomy too seriously) René Doursat: "Complex Systems Made Simple"
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1. Introduction — b.
A vast archipelago
Precursor and neighboring disciplines complexity: measuring the length to describe, time to build, or resources to run, a system information theory (Shannon; entropy) computational complexity (P, NP) Turing machines & cellular automata
Toward a unified “complex systems” science and engineering?
systems sciences: holistic (nonreductionist) view on interacting parts systems theory (von Bertalanffy) systems engineering (design) cybernetics (Wiener; goals & feedback) control theory (negative feedback)
dynamics: dynamics:behavior behaviorand andactivity activityof ofaa system systemover overtime time nonlinear dynamics & chaos stochastic processes systems dynamics (macro variables)
Fall 2015
adaptation: change in typical functional regime of a system evolutionary methods genetic algorithms machine learning
René Doursat: "Complex Systems Made Simple"
multitude, statistics: large-scale properties of systems graph theory & networks statistical physics agent-based modeling distributed AI systems 39
1. Introduction — b.
A vast archipelago
Sorry, there is no general “complex systems science” or “complexity theory”... there are a lot of theories and results in related disciplines (“systems theory”, “computational complexity”, etc.), yet such generic names often come from one researcher with one particular view there is no unified viewpoint on complex systems, especially autonomous in fact, there is not even any agreement on their definition
we are currently dealing with an intuitive set of criteria, more or less shared by researchers, but still hard to formalize and quantify: Fall 2015
complexity emergence self-organization multitude / decentralization adaptation, etc.
... but don’t go packing yet!
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1. Introduction — b.
A vast archipelago
The French “roadmap” toward complex systems science another way to circumscribe complex systems is to list “big (horizontal) questions” and “big (vertical) objects”, and cross them
Big questions reconstruct multiscale dynam. emergence & immergence spatiotemp. morphodynamics optimal control & steering artificial design fluctuations out-of-equilib. adaptation, learning, evolution Fall 2015
Toward a complex systems science CARGESE MEETINGS 2006, 2008, 2010 ~40 researchers from French institutions
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1. Introduction — b.
A vast archipelago
The French “roadmap” toward complex systems science another way to circumscribe complex systems is to list “big (horizontal) questions” and “big (vertical) objects”, and cross them
Big questions reconstruct multiscale dynam.
multiscale
emergence & immergence spatiotemp. morphodynamics optimal control & steering
...
artificial design fluctuations out-of-equilib. adaptation, learning, evolution Fall 2015
Triller & Dahan
Blue Brain
René Doursat: "Complex Systems Made Simple"
Laufs et al.
42
Paris Ile-de-France 4th French Complex Systems Summer School, 2010
National
Fall 2015
Lyon Rhône-Alpes
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Resident Researchers (2010)
mathematical neuroscience
artificial life / neural computing
high performance computing
complex networks / cellular automata
urban systems / innovation networks embryogenesis
statistical mechanics / collective motion
web mining / social intelligence
structural genomics
spiking neural dynamics
computational evolution / development
social networks
peer-to-peer networks
spatial networks / swarm intelligence
active matter / complex networks
44 nonlinear dynamics / oceanography
Visualization of Research Networks (from D. Chavalarias)
45
