Erasmus Mundus Masters in
Complex Systems Science Fall 2015
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 families of complex systems objects: cellular automata, pattern formation, swarm intelligence, complex networks, spatial communities, structured morphogenesis, etc.
and their common questions (concepts): emergence, self-organization, positive feedback, decentralization, between simple and disordered, “more is different”, adaptation & evolution, etc.
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 2015
René Doursat: "Complex Systems Made Simple"
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Research Projects What you will be doing warmup: code two exercises in NetLogo 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 conference-style paper conference-style presentation, with live demo
Important deadlines: (tentative)
Fall 2015
1. project proposals (2-page report & slides) → due & presented Oct 13 2. first submission of report and code (B, C) → due Dec 1 3. final revision of report and code (B, C) → due Dec 11 4. project slides (A) → due and presented Dec 15 René Doursat: "Complex Systems Made Simple"
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Complex Systems Made Simple 1.
Introduction
2.
A Complex Systems Sampler
3.
Commonalities
4.
NetLogo Tutorial
Fall 2015
René Doursat: "Complex Systems Made Simple"
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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
René Doursat: "Complex Systems Made Simple"
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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
• Few agents • Many agents • CS in this course
René Doursat: "Complex Systems Made Simple"
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1. Introduction — a.
What is a system?
System Sy(n)-stem: “standing together” A group/configuration of elements/parts which are interacting/connected/joined together, and form a unified whole
Types of systems – – – – – Fall 2015
Physical systems: weather, planets (solar system), ... Biological systems: body (circulatory, respiratory, nervous), ... Engineering systems: BE, EE, ME, CE ... Information systems: CS, ICT, ... ... René Doursat: "Complex Systems Made Simple"
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1. Introduction — a.
What are “complex” systems?
Any ideas?
The School of Rock (2003) Jack Black, Paramount Pictures
Fall 2015
René Doursat: "Complex Systems Made Simple"
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1. Introduction — a.
What are “complex” systems?
What makes a system “complex”?
Fall 2015
René Doursat: "Complex Systems Made Simple"
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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)
Fall 2015
Two bodies with similar mass
Two bodies with different mass
Wikimedia Commons
Wikimedia Commons
René Doursat: "Complex Systems Made Simple"
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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 2015
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
Logistic map
Craig L. Zirbel, Bowling Green State University, OH
Fall 2015
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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 2015
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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
Fall 2015
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