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Complexity-Resources

From Basic to Advanced. Complex does not equal complicated, a computer for example is complicated, whereas the brain is complex. Complexity is a property of the systems as a whole. The science of complexity takes a global view of the world, it regards the interrelations between systems and the elements that they are made up of. This hollistic apporach is deterministic but nonreductionistic.

Basics in Complexity:

Butterfly effect: even initial situations that are very close to one another may present a difference that becomes sizeable over time, this property is called sensitivity to initial conditions. Improving the precision of measurement can to some extend increase the so-called predictability horizon, but errors introduced by using approximations increasingly amplify themself. Unstable dynamical systems are characterized by the unpredictability this effect causes. This is called deterministic chaos, it is the substantial and intrinsic unpredictability of the future evolution of both a real system and an abstract model.

https://www.reddit.com/r/compsci/comments/91ocq7/recommendation_for_complexity_theory_courseware/

  • add things from man and machine to complexity

'It's better to have questions you can‘t answer than having answers you can‘t question.'

Advanced Complex Systems

  • Merge advanced / research; different sections and subsections
  • add self-organization and emergence
  • add scaling (especially in biological systems)
  • Network science (science of networks applied to real-world networks) / examples brains etc.
  • Evolution of complex (adaptive) systems

Fractals/ Scale

  • Definition: fractal video (Definition dimension)
  • Elliot waves

Complexity Research

Related research fields/buzzwords: modeling artificial life, choas theory, genetic algorithms, complexity economics, econophysics, complex networks, systems biology, self-organization, emergence, complex adaptive systems, cybernetics, cognitive modeling, fractal geometry, multi-agent systems, nonlinearity

  • MIT books

https://www.quantamagazine.org/the-beautiful-intelligence-of-bacteria-and-other-microbes-20171113/

Information theory:

  • randomness
  • Information density
  • dimensionality reduction
  • laws of theormodynamics

Information as form of energy? (eg speech as pressure wave is reducable to energy states)

https://www.reddit.com/r/compsci/comments/1prnxi/complexity_theoryinformation_theory_reading/

Computation

What does computing mean?

  • biological and evolutionary computation
  • Neural computation
  • collective computation (swarm intelligence)
  • nature-inspired computation (e.g. immune system)
  • evolutionary computing (https://www.mitpressjournals.org/loi/evco)

Math:

Phase space, orbit, basin of attraction and attractor are all mathematical concepts and as such abstract models constructed from observed processes.

  • graph theory (math)
  • network theory

The Lyapunov exponents provides a measurment of the stability of a dynamical system, they quantify the level of chaos by indicating the speed of growth (instability) or reduction (stability). The sum of these exponents known as the Kolmogorov–Sinai entropy it is an indicator of the extent to which the system is unpredictable (the higher, the less predictable).

Complexity Paper

History (add more detailed history):

  • Warren Weaver (1948) - Science & Complexity

  • Science oldest cliche, the idea of hidden unity and common underlying form in nature had an intrinsic appeal.

  • Chaos started with weather in 1960s

  • Lorenz Paper of deterministic nonperiodic flow (For a long time people who like mathematics tend toward physics, maths or economics than the other life sciences)

  • Phase Space

  • Feigenbaum sequence of bifurcations

  • vernon mountcastle​, similar to darwins idea of a simple algorithm - mountcastle says the same about the brain

  • Poincare

  • Mandelbrot -Chao Tang and Kurt Wiesenfeld; sandpile model

  • Stuart Kauff man’s ‘button’ model

  • Historic paper/ review

-global pattern donation and ethic/cultural violence (New England complex systems institute) -morphogenesis”—the capacity of all life-forms to develop ever more baroque bodies out of impossibly simple beginnings - Turing

Complexity in neuroscience: https://www.nature.com/collections/ycjylwzvmz/neuroscience

Applied complexity research

Economies are self-organizing systems they are dealing with dynamic, changing systems, in which positive feedbacks are involved (stock market). Social systems in general are instable and very often 'situations' are different from each other, which makes the determination of regularities difficult.

  • Thermodynamics -Neural networks

  • Biological poulations (movment of human populations/ Bak-Sneppen model): story of life in the Universe is an example of surface complexity built upon foundations of simple rules.

  • ants eusocial insects (aided by group selection - single super organism)

  • Evolution (individual selection, kin selection, reciprocal altruism)

  • Genetic analysis (interactions during evolution/ evolution as complexification of organisms/ tree of life) -pubMed for complex gene analysis

  • Geoscinece/ Meterology (Earthquakes / Idea of Gaia, the Earth as a self-regulating system / Daisyworld model) (https://serc.carleton.edu/NAGTWorkshops/complexsystems/activities/daisyworld.html)

  • add pictures into text / include pictures on fitting on points (+ sources of pictures)

  • change picture to betwork theory and add background about this theory

Software

Cellular automata:

Modeling:

Artificial life:

Computational modeling

'What I cannot create, I do not understand.' - Richard Feynman

Mathematical modelling has become an increasingly relevant instrument, reality often turns out too complicated to be summarized within a limited set of rules. Especially the assumptions underlying linearization are unacceptable, the further a phenomen is from a condition of stable equilibrium. Nonlinear models are often better suited, but they are limited due to incomplete knowledge of the initial state leading to approximations that worsen over time.

A model, intrinsically, is a partial representation of several aspects of reality, it remains only a tool of analysis, even though a fundamental one. To build such models it is important to identify the number of state variable need to constitute the phase space to contain the possible attractor (an the dimension of such). The number of state variables is often numerous and usually even impossible to identify.

All sciences have the identification and prediction of the evolution of systems over time in common, for this purpose modeling is well suited.

  • neuroscience
  • econometrics/ econophysics
  • population modeling/ ecosystems
  • weather forecast
  • protein folding

Other Resources

Persons to follow:

Institutes to follow:

https://www.santafe.edu

https://www.mis.mpg.de/ay/

Monash University, Victoria

New England Complex System Institute (NECSI)

Blogs and Websites to follow:

-opendatascience

http://understandingthecomplex.blogspot.com http://www.if.pw.edu.pl/~agatka/catalogue/soc.html

http://chaosbook.org