Introduction to Complexity
URL : https://www.complexityexplorer.org/courses/119introductiontocomplexity
I enrolled on this in August 2021. I see it as a bit of a refresher on what I did in [[Evolutionary and adaptive systems]]. And to give me a fun intro to [[agentbased modelling]] with [[NetLogo]].
And then, the aim would be, to be followed up by some study of [[Systems thinking]]. They're related, but slightly different. See [[Complex Adaptive Systems, Systems Thinking, and AgentBased Modeling]].
That I can then apply to questions around [[political organisation]], [[climate change]], and [[social network]]s.
In this course you'll learn about the tools used by scientists to understand [[complex systems]]. The topics you'll learn about include dynamics, chaos, fractals, information theory, selforganization, agentbased modeling, and networks. Youβll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology.
The Course
Unit 1: What is Complexity?
Some examples of complex systems given are [[ant colonies]], [[the brain]], [[social network]]s, the web, the human genome, the economy, [[food webs]], the [[immune system]], cities.
I'm probably most interested in the [[networks]] complex systems. But they're all interesting.
Biological, social, technological.
Properties common to complex systems
 [[agents]]
 [[nonlinear]] interactions
 no central control ([[decentralisation]])
 [[selforganisation]]

[[emergence]] (emergent behaviours)
 hierarchical organisation
 information processing

complex dynamics
 e.g. foraging trails in ants
 e.g. stock prices
 evolution and learning
Core Disciplines, Goals, and Methodologies of the Sciences of Complexity
disicplines
dynamics : the study of continually changing structure and behaviour of systems
information : the study of representation, symbols, and communication
computation : the study of how systems process information and act on the results
evolution : the study of how systems adapt to constantly changing environments
goals

crossdisciplinary insights into complex systems
 e.g how does information processing in ant colonies relate to information processing in cities
 e.g. how is information flow in the brain simalar to information flow in an economic network

general theory
 is it possible?
methodologies

experimental work

theoretical work

computer simulation
This course has a focus on computer simulation of complex systems.
Definitions of complexity
Hard to defineβ¦ lots of definitions. We'll look at [[Shannon information]] and [[Fractal dimension]].
[[Warren Weaver]]

problems of simplicity
 a few variables, e.g. pressure and temperature; current, resistance, voltage; population vs time

problems of disorganized complexity
 billions or trillions of variables
 e.g. laws of temperature and pressure
 averages, statistical mechanics
 we assume little interaction between variables

problems of organized complexity
 moderate to large number of variables

strong non linear interactions
 can't be averaged meaningfully
Problems of organized complexity
 what makes an evening primrose open when it does?
 what is aging?
 what is a gene?
 on what does the price of wheat depend?
 how can you explain the behaviour of e.g. a labour union?
What are Complex Systems? The Experts Weigh In

something where there's no simple compact way of describing the system
 systems that encode long histories
 sophisticated internal architecture of how it stores information
 interacting things with emergent behaviour
 evolution and adaptation is a key part of complex systems?
 and feedback?
Introduction to NetLogo
NetLogo is super simple to set up and get running the demos. The Ants model is very cool  foraging for food sources and finding the closest thanks to pheromone trails. This is the kind of thing I faffed around with graphics programming on in my Masters, surely would have been easier to use a prebuilt system for it. I wonder why we didn'tβ¦
I really like the way it's presented, in that it gets you thinking about how the agentbased models might run and their dynamics. And it also makes you make predictions as to how changes in parameters and behaviours might change the dynamics. Thinking a bit more scientifically about it. Making a prediction and testing it with an experiment.
I also love the NetLogo agentbased modelling stuff because it is very much thinking visually. When some result isn't what you expected, you actually view the behaviour on screen.
Unit 2: Dynamics and chaos
Introduction to dynamics
[[Dynamics]] is the science of how systems change over time. How does behaviour unfold and how does it change over time.
e.g. planetary dynamics; fluid dynamics; electrical dynamics; climate dynamics; crowd dynamics; population dynamics; financial dynamics; group dynamics; dynamics of conflicts and dynamics of cooperation.
Dynamical systems theory

branch of maths of how systems change over time
 calculus
 diff eqs
 iterated maps
 etc

dynamics of a system
 manner in which the system changes
 gives us vocabularly and set of tools for describing dynamics
Brief history

(in the west) Aristotle
 one set of laws for the earth, one for the heavens

copernicus
 sun is stationary

galileo
 experimental method
 proved aristotle laws were false

newton
 founder of modern science of dynamics
 laws of motion same on earth and in heavens

laplace
 proponent of newtonian reductionism
 thought you could have complete prediction of the future

poincare
 small differences in intial conditions produce very great ones in the final phenonema

"sensitive dependence on initial conditions"
 socalled butterfly effect
Chaos
 one particular type of dynamics of a system
 defined as "sensitive dependence on initial conditions"

chaos is present in lots of places in nature
 solar system orbits, weather and climate, computer networks, population growth and dynamics, and more
 we'll look at population growth
 what is the difference between chaos and randomness?
Iteration
 Doing something again and again.

population growth is iterative
 iterative part is we take last years pop to calculate this years pop
 we have a linear equation because we have a linear system
 linear equation because no interaction between bunnies
 independence yields linearity
Linear vs nonlinear systems
 what happens when the parts interact in a nonlinear way?
 linearity: "the whole is the sum of the parts"

for nonlinear  we add in death through overcrowding
 plus a death rate
 this gives us the "[[logistic model]]"
 with nonlinear systems, the whole is not the sum of the parts
 public document at doc.anagora.org/introductiontocomplexity
 video call at meet.jit.si/introductiontocomplexity
(none)
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agent based modelling
agents
ant colonies
climate change
complex adaptive systems systems thinking and agent based modeling
complex systems
decentralisation
dynamics
emergence
evolutionary and adaptive systems
food webs
fractal dimension
immune system
logistic model
netlogo
networks
nonlinear
political organisation
self organisation
shannon information
social network
systems thinking
the brain
warren weaver