Erasmus Mundus Masters in

Complex Systems Science Fall 2013

Complex Systems Made Simple by Agent-Based Modeling and Simulation René Doursat http://iscpif.fr/~doursat

Course Contents  What this course is about (dense preview, will be repeated)  an exploration of various complex systems objects:  cellular automata, pattern formation, swarm intelligence, complex networks, spatial communities, structured morphogenesis

 and their common questions:  emergence, self-organization, positive feedback, decentralization, between simple and disordered, “more is different”, adaptation & evolution

 by interactive experimentation (using NetLogo),  introducing practical complex systems modeling and simulation  from a computational viewpoint, in contrast with a “mathematical” one (i.e., formal or numerical resolution of symbolic equations),  based on discrete agents moving in discrete or quasi-continuous space, and interacting with each other and their environment Fall 2013

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Research Project  What you will be doing  code two exercises  prepare an individual research project  topics must address complex systems and may be:

 expanding upon examples seen here  overlapping with your own interests / another project of yours  both or neither

 project deliverables:

 modeling & simulation program  journal-style report (but shorter)  conference-style presentation, with live demo

 project deadlines:

 day x: send proposal 1-2-page & few slides → presentations on x+1  day y: send final code, report & slides→ presentations on y+1

Fall 2013

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Complex Systems Made Simple 1.

Introduction

2.

A Complex Systems Sampler

3.

Commonalities

4.

NetLogo Tutorial

Fall 2013

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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 2013

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Complex Systems Made Simple 1.

Introduction a. b. c.

• Few agents What are complex systems? • Many agents • CS in this course

A vast archipelago

Computational modeling

2.

A Complex Systems Sampler

3.

Commonalities

4.

NetLogo Tutorial

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1. Introduction — a.

What is a system?

 System A group/configuration of elements/parts which are interacting/connected/joined together, and form a unified whole

 Types of systems – – – – –

Fall 2013

Physical systems: weather, planets (solar system), ... Biological systems: body (circulatory, respiratory, nervous), ... Engineering systems: BE, EE, ME, ... Information systems: CS, ICT, ... ...

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1. Introduction — a.

What are complex systems?

 Any ideas?

The School of Rock (2003) Jack Black, Paramount Pictures

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1. Introduction — a.

What are complex systems?

 Few agents, “simple” emergent behavior → ex: two-body problem  fully solvable and regular trajectories for inverse-square force laws (e.g., gravitational or electrostatic)

Two bodies with similar mass Wikimedia Commons

Fall 2013

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Two bodies with different mass Wikimedia Commons

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1. Introduction — a.

What are complex systems?

 Few agents, complex emergent behavior → ex: three-body problem  generally no exact mathematical solution (even in “restricted” case m1 〈〈 m2 ≈ m3): must be solved numerically → chaotic trajectories NetLogo model: /Chemistry & Physics/Mechanics/Unverified

Fall 2013

Transit orbit of the planar circular restricted problem Scholarpedia: Three Body Problem & Joachim Köppen Kiel’s applet

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1. Introduction — a.

What are complex systems?

 Few agents, complex emergent behavior → ex: more chaos (baker’s/horseshoe maps, logistic map, etc.)  chaos generally means a bounded, deterministic process that is aperiodic and sensitive on initial conditions → small fluctuations create large variations (“butterfly effect”)  even one-variable iterative functions: xn+1 = f(xn) can be “complex”

Baker’s transformation

Craig L. Zirbel, Bowling Green State University, OH

Fall 2013

René Doursat: "Complex Systems Made Simple"

Logistic map 11

1. Introduction — a.

What are complex systems?

 Many agents, simple rules, “simple” emergent behavior → ex: crystal and gas (covalent bonds or electrostatic forces)  either highly ordered, regular states (crystal)  or disordered, random, statistically homogeneous states (gas): a few global variables (P, V, T) suffice to describe the system NetLogo model: /Chemistry & Physics/GasLab Isothermal Piston

Diamond crystal structure

Tonci Balic-Zunic, University of Copenhagen

Fall 2013

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1. Introduction — a.

What are complex systems?

 Many agents, simple rules, complex emergent behavior → ex: cellular automata, pattern formation, swarm intelligence (insect colonies, neural networks), complex networks, spatial communities  the “clichés” of complex systems: a major part of this course and NetLogo models

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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 2013

companies

techno-networks

René Doursat: "Complex Systems Made Simple"

cities 14

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 2013

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 Swarm chemistry

Hiroki Sayama, Binghamton University SUNY

Fall 2013

Embryomorphic engineering

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 2013

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

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1. Introduction — a.

What are complex systems?

 Recap: complex systems in this course

Fall 2013

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

machines, crowds with leaders

many

complicated

deterministic/ centralized

René Doursat: "Complex Systems Made Simple"

suffice to describe it

random and uniform

YES – reproducible

and heterogeneous

COMPLICATED

– not self-organized 19

1. Introduction — a.

What are complex systems?

 Recap: complex systems in this course

Fall 2013

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

machines, crowds with leaders

many

complicated

deterministic/ centralized

René Doursat: "Complex Systems Made Simple"

suffice to describe it

random and uniform

YES – reproducible

and heterogeneous

COMPLICATED

– not self-organized 20

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 2013

the brain & cognition = neuron

biological development = cell

Internet & Web = host/page René Doursat: "Complex Systems Made Simple"

social networks = person 21

1. Introduction — a.

What are complex systems? Physical pattern formation: Convection cells

WHAT? Rayleigh-Bénard convection cells in liquid heated uniformly from below

∆T

Convection cells in liquid (detail)

HOW? Schematic convection dynamics

(Manuel Velarde, Universidad Complutense, Madrid)

(Arunn Narasimhan, Southern Methodist University, TX)

Solar magnetoconvection

Hexagonal arrangement of sand dunes

(Scott Camazine, http://www.scottcamazine.com)

Sand dunes

(Scott Camazine, http://www.scottcamazine.com)

(Steven R. Lantz, Cornell Theory Center, NY)

(Solé and Goodwin, “Signs of Life”, Perseus Books)

 thermal convection, due to temperature gradients, creates stripes and tilings at multiple scales, from tea cups to geo- and astrophysics Fall 2013

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1. Introduction — a.

What are complex systems? Biological pattern formation: Animal colors

WHAT?

ctivator

HOW? nhibitor

Mammal fur, seashells, and insect wings (Scott Camazine, http://www.scottcamazine.com)

NetLogo fur coat simulation, after David Young’s model of fur spots and stripes (Michael Frame & Benoit Mandelbrot, Yale University)

 animal patterns (for warning, mimicry, attraction) can be caused by pigment cells trying to copy their nearest neighbors but differentiating from farther cells Fall 2013

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1. Introduction — a.

What are complex systems? Spatiotemporal synchronization: Neural networks

HOW?

Cortical layers

WHAT? Animation of a functional MRI study

Pyramidal neurons & interneurons

(J. Ellermann, J. Strupp, K. Ugurbil, U Minnesota)

 the brain constantly generates patterns of activity (“the mind”)

(Ramón y Cajal 1900)

Schematic neural network

 they emerge from 100 billion neurons that exchange electrical signals via a dense network of contacts Fall 2013

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1. Introduction — a.

What are complex systems? Swarm intelligence: Insect colonies (ant trails, termite mounds)

WHAT? Harvester ant

(Deborah Gordon, Stanford University) http://taos-telecommunity.org/epow/epow-archive/ archive_2003/EPOW-030811_files/matabele_ants.jpg

http://picasaweb.google.com/ tridentoriginal/Ghana

 ants form trails by following and reinforcing each other’s pheromone path

Termite stigmergy

Termite mound

(J. McLaughlin, Penn State University)

Fall 2013

http://cas.bellarmine.edu/tietjen/ TermiteMound%20CS.gif

HOW?

 termite colonies build complex mounds by “stigmergy”

(after Paul Grassé; from Solé and Goodwin, “Signs of Life”, Perseus Books)

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1. Introduction — a.

What are complex systems? Collective motion: flocking, schooling, herding

HOW? S

A

C

Separation, alignment and cohesion

Fish school

(“Boids” model, Craig Reynolds, http://www.red3d.com/cwr/boids)

(Eric T. Schultz, University of Connecticut)

WHAT?

 coordinated collective movement of dozens or 1000s of individuals

(confuse predators, close in on prey, improve motion efficiency, etc.)

Bison herd

(Center for Bison Studies, Montana State University, Bozeman)

Fall 2013

 each individual adjusts its position, orientation and speed according to its nearest neighbors

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1. Introduction — a.

What are complex systems? Complex networks and morphodynamics: human organizations organizations

urban dynamics

cellular automata model

HOW?

WHAT? SimCity (http://simcitysocieties.ea.com) (Thomas Thü Hürlimann, http://ecliptic.ch)

global connectivity

techno-social networks

NSFNet Internet (w2.eff.org) Fall 2013

René Doursat: "Complex Systems Made Simple"

NetLogo urban sprawl simulation

“scale-free” network model

NetLogo preferential attachment simulation 27

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 2013

organisms

animal flocks

animals humans & tech markets, economy

cities, populations 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 2013

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 biological patterns

living cell

physical patterns

ant trails

 ... yet, even human-caused systems are “natural” in the sense of their unplanned, spontaneous emergence Internet, Web

Fall 2013

organisms

markets, economy

termite mounds 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 biological patterns

living cell physical patterns

ant trails

 biology strikingly demonstrates the possibility of combining pure self-organization and elaborate architecture Internet, Web

Fall 2013

organisms

markets, economy

termite mounds animal flocks cities, populations

social networks

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1. Introduction — a.

What are complex systems? “Simple/random” vs. architectured natural complex systems

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1. Introduction — a.

What are complex systems?

 Complex systems can possess a strong architecture, too 

"complex" doesn’t imply "homogeneous"...



"complex" doesn’t imply "flat"...



"complex" doesn’t imply "random"...

→ heterogeneous agents and diverse patterns, via positions → modular, hierarchical, detailed architecture → reproducible patterns relying on programmable agents architecture

soldier

queen

worker defend

transport

reproduce

royal chamber

nursery galleries ventilation shaft

but then what does it mean for a module to be an "emergence" of many fine-grain agents?

build

fungus gardens

(mockup) EA-style diagram of a termite mound

→ cells and social insects have successfully "aligned business and infrastructure" for millions of years without any architect telling them how to

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Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012) Fall 2013

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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 2013

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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1. Introduction — a.

What are complex systems?

 Many self-organized systems exhibit random patterns... more architecture

(a) "simple"/random self-organization

... while "complicated" architecture is designed by humans (d) direct design (top-down) Fall 2013

René Doursat: "Complex Systems Made Simple"

more self-organization

gap to fill

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1. Introduction — a.

What are complex systems?

 Many self-organized systems exhibit random patterns...

....

....

 self-forming robot swarm  self-programming software  self-connecting micro-components Fall 2013

   

artificial

(c) engineered self-organization (bottom-up)

natural

(b) natural self-organized architecture

self-reconfiguring manufacturing plant self-stabilizing energy grid self-deploying emergency taskforce self-architecting enterprise

René Doursat: "Complex Systems Made Simple"

more self-organization

 Can we transfer some of their principles to human-made systems and organizations?

more architecture

 The only natural emergent and structured CS are biological

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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

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A Complex Systems Sampler

 From genotype to phenotype, via development

×

Fall 2013

→

René Doursat: "Complex Systems Made Simple"

×

→

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A Complex Systems Sampler

NetLogo “Fur”

 From cells to pattern formation, via reaction-diffusion

ctivator nhibitor

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A Complex Systems Sampler

NetLogo “Ants”

 From social insects to swarm intelligence, via stigmergy

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A Complex Systems Sampler NetLogo “Flock”

 From birds to collective motion, via flocking

separation

Fall 2013

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alignment

cohesion

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A Complex Systems Sampler

 From neurons to brain, via neural development . . .

Ramón y Cajal 1900

Fall 2013

. . .

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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 2013

• 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)

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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 2013

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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 2013

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