Definitions

Definitions are key thinkers, attributes, and seminal texts that relate or contribute to Complex Adaptive Systems theory. This is an alphabetical listing of all definitions in the CAS Explorer.

'Fit' Unit Configurations

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'The Virtual'

This term is used by relational/assemblage philosophers to speak about the 'space of possibilities' only some of which is activated - see {{phase-space}} Learn More about 'The Virtual' →


A. Siksna

x Learn More about A. Siksna →


Affordances

The notion of 'Affordances' was developed by James Gibson. It has many strong similarities to the concept of 'phase space'. Learn More about Affordances →


Agents

The building blocks of a complex adaptive system. Learn More about Agents →


Albert Laszlo Barabasi

x Learn More about Albert Laszlo Barabasi →


Alejandro Aravena

x Learn More about Alejandro Aravena →


Arrow of Time

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Ash Amin & Thrift Nigel

x Learn More about Ash Amin & Thrift Nigel →


Attractor States

Complex Systems can unfold in multiple trajectories. However, there may be trajectories that become more stable of 'fit'. Such states are considered 'attractor states' to which a system tends to gravitate. Learn More about Attractor States →


Ben Hamouche

x Learn More about Ben Hamouche →


Benoit Mandelbrot

x Learn More about Benoit Mandelbrot →


Bernhard Riemann

x Learn More about Bernhard Riemann →


Besim Hakim

x Learn More about Besim Hakim →


Bifurcations

Complex systems do not follow linear, predictable chains of cause and effect. Instead, system trajectories can diverge wildly into entirely different regimes. The moments in time when a system splits into different, but equally probable trajectories is referred to as a system bifurcation. Learn More about Bifurcations →


Bottom-up

Complex Systems are generated from the local interactions of multiple AGENTS. These agents are not actively striving to achieve any form of 'global' structure or pattern, but simply behave in their own self interests. Hence, we describe CAS dynamics as being generated from 'the bottom up' as opposed to 'the top down' as would be the case in traditional hierarchical organizations. Learn More about Bottom-up →


Building Blocks

Complex systems are made up of simple 'building blocks'. A building block could take many forms: an ant, a cell, or a building within the urban fabric. Learn More about Building Blocks →


C. S. Holling

Holling relates concepts from CAS theory with notions of Resiliency. Learn More about C. S. Holling →


Capturing Flows

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Charles Darwin

Evolution Learn More about Charles Darwin →


Charles Waldheim

important LU guy Learn More about Charles Waldheim →


Charles Waldheim

x Learn More about Charles Waldheim →


Chris Langton

x Learn More about Chris Langton →


Claude Shannon

x Learn More about Claude Shannon →


Colin McFarlane & Ben Anderson

x Learn More about Colin McFarlane & Ben Anderson →


Contingency

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Cybernetics

Cybernetics,or 'Kybernetes', from the Greek meaning 'steersman' or 'oarsman', refers to the study of systems that have an internal regulator that in same way 'steers' the system so that it remains targeted to a pre-determined trajectory. Learn More about Cybernetics →


Degrees of Freedom

The notion of 'degrees of freedom' pertains to the potential range of behaviors available within a given system. See {{phase-space}} Learn More about Degrees of Freedom →


Denise Pumain

x Learn More about Denise Pumain →


Doreen Massey

x Learn More about Doreen Massey →


Dynamic Interactions

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Edward Norton Lorenz

x Learn More about Edward Norton Lorenz →


Emerg. of mathematical regularities

What one could describe as the 'fingerprint' of a complex system is the emergence of certain kinds of mathematical regularities that describe the system. Characteristic features of the system are organized according to POWER-LAW Distributions. Power-law distributions also appear in FRACTAL systems. Keep exploring in the topics below to learn more about mathematical regularities in CAS. Learn More about Emerg. of mathematical regularities →


Emerg. of Stabilized Features

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Emergence

Complex systems present unexpected novel properties that cannot be predicted based upon the inherent characteristics of the agents making up the system. These characteristics are referred to as 'Emergent'. Learn More about Emergence →


Emergent Schemata

CAS Systems develop order or pattern ‘for free’: this means that order arises as a result of independent agent behaviors, without need for other inputs. Learn More about Emergent Schemata →


Employing Time

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Enslaved State

'Enslavement' captures the idea that certain emergent states will constrain the agents that initially formed that state once they have emerged. Learn More about Enslaved State →


Eric Bonabeau

x Learn More about Eric Bonabeau →


Eric Sheppard

x Learn More about Eric Sheppard →


Evolving

CAS systems evolve over the course of time. That is to say that the agents that form the system will continuously adjust their independent behaviors in such a way that the overall behavior of the system becomes more fit. General Darwinian processes of Variation, Selection and Retention of desired characteristics help drive the system. Learn More about Evolving →


Feedback

A feedback loop is a system where energy or information that is an input to a system is transformed to become an output, which then acts in such a way that it is transformed into a new input in a continuous cycle. Learn More about Feedback →


Fitness

Complex Adaptive Systems become more 'fit' as they adapt. Fitness takes many forms, depending on the system, but can be thought of as entering into states that achieve more while expending less energy. Learn More about Fitness →


Fitness Landscape

A fitness landscape is a concept that employs the metaphor of a physical landscape to depict more or less 'fit' regions of phase space. see also {{fitness-peaks}} Learn More about Fitness Landscape →


Fitness Peaks

Fitness ‘peaks’ are regimes wherein a given agent behavior maximizes energetic returns while minimizing outputs. Peaks are thus optimum behaviors in phase space - though there may be numerous peaks, each employing different strategies. See also {{Fitness-Landscape}} Learn More about Fitness Peaks →


Flow of Energy

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Fluidity/Mobility

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Fractals

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Geoffrey West

Scale Free issues with Bettencourt Learn More about Geoffrey West →


George Kingsley Zipf

relates to {{power-laws}} Learn More about George Kingsley Zipf →


George Kingsley Zipf

x Learn More about George Kingsley Zipf →


Gregory Bateson

x Learn More about Gregory Bateson →


Heinz Von Foerster

x Learn More about Heinz Von Foerster →


Henri Poincare

x Learn More about Henri Poincare →


Henry Wai-chung Yeung

x Learn More about Henry Wai-chung Yeung →


Herbert Simon

hfksdflkajsdf Learn More about Herbert Simon →


Hermann Haken

Haken popularized the concepts of {{enslaved-states}} and 'synergetics'. The notion of 'enslavement' is similar to the idea of {{attractor-statesbasins}}, wherein a system will tend to gravitate towards a particular regime and then remain in that state unless there is a system {{perturbation}}. Learn More about Hermann Haken →


Hermann Haken

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History

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Humberto Maturana & Francisco Varela

x Learn More about Humberto Maturana & Francisco Varela →


Ignacio Farias

x Learn More about Ignacio Farias →


Ilya Prigogine & Isabelle Stengers

x Learn More about Ilya Prigogine & Isabelle Stengers →


Information Theory

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Itzhak Benenson

x Learn More about Itzhak Benenson →


Jack Ahern

x Learn More about Jack Ahern →


James Corner

x Learn More about James Corner →


Jason Dittmer

x Learn More about Jason Dittmer →


Jean Hillier

More here Learn More about Jean Hillier →


Jeremy Till & T. Schneider

x Learn More about Jeremy Till & T. Schneider →


John Conway

x Learn More about John Conway →


John Holland

John Holland is considered one of the seminal thinkers in Complex Adaptive Systems theory. Learn More about John Holland →


John Protevi

x Learn More about John Protevi →


John Urry

John Urry writes about flows and complexity Learn More about John Urry →


John Urry

x Learn More about John Urry →


John Von Neumann

x Learn More about John Von Neumann →


Josh Epstein and Rob Axtell

x Learn More about Josh Epstein and Rob Axtell →


Judith E. Innes & DE Booher

x Learn More about Judith E. Innes & DE Booher →


Juval Portugali

x Learn More about Juval Portugali →


Kim Dovey

more on Dovey Learn More about Kim Dovey →


Koen Franken

x Learn More about Koen Franken →


Local Interactions

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Loose Space

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Ludwig V. Bertalanffy

x Learn More about Ludwig V. Bertalanffy →


Luis Bettencourt

x Learn More about Luis Bettencourt →


Manifold

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Manuel de Landa

page about manuel Learn More about Manuel de Landa →


Mark Newman

x Learn More about Mark Newman →


Martin Prominski

x Learn More about Martin Prominski →


Michael Batty

Mike Batty is one of the key contributors to modeling cities as Complex Adaptive Systems Learn More about Michael Batty →


Mike Lydon and Anthony Garcia

have written about tactical urbanism. Learn More about Mike Lydon and Anthony Garcia →


Mitchell Feigenbaum

x Learn More about Mitchell Feigenbaum →


Modular

To speak of a CAS being modular is another way to refer to its {{building-blocks}} characteristic. Complex system are made up of multiple independent components that begin to form modules, nested hierarchies or patches. Learn More about Modular →


Multiple Equilibria

Early versions of systems theory assumed that systems could be 'optimized' to a single condition. CAS analysis assumes that more than one system state can satisfy optimizing criteria, and so the system is able to gravitate to multiple equilibria. Learn More about Multiple Equilibria →


Murray Gell-Mann

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N. John Hebraken

x Learn More about N. John Hebraken →


Negative Feedback

Negative Feedback is the tendency for systems to employ mechanisms whereby any fluctuations from a particular behavior or trajectory are 'dampened'; that is to say, divergence from a norm is hindered. Learn More about Negative Feedback →


Negentropy

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Network Topology

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Networks

Network theory allows us think about the dynamics of agent interactions in a complex system. CAS networks have a tendency to naturally evolve to have power law distributions between highly and weakly connected agents. This is due to the dynamics of {{preferential-attachment}} whereby 'the rich get richer'. Learn More about Networks →


Nikos Salingaros

resilience Learn More about Nikos Salingaros →


No Central Control

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Norbert Weiner

x Learn More about Norbert Weiner →


Ole Jensen

x Learn More about Ole Jensen →


Ole Jensen

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Open / Dissipative

A system is considered to be open and dissipative when energy or inputs can be absorbed into the system, and 'waste' discharged. While the system still is considered 'bounded' (interactions are primarily internal), the system nonetheless has a relationship with its external environment. Learn More about Open / Dissipative →


Open Scaffolds

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Open System

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Pareto

power law distributions Learn More about Pareto →


Path Dependency

Non-Linear systems are subject to {{positive-feedback}}, which tends to amplify changes. Accordingly, they are 'non-linear' in that a small change in input can lead to a large change in output. Learn More about Path Dependency →


Patsy Healey

Patsey Healey has provided a significant contribution to discussions regarding participatory planning and complex systems. Learn More about Patsy Healey →


Patterns of Interactions

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Paul Torrens

x Learn More about Paul Torrens →


Per Bak

Self organized criticality Learn More about Per Bak →


Perturbation

Once a Complex System has 'settled in' to a fit regime (or basin of attraction), it is very difficult for it to be 'shaken' from this state. A system perturbation acts as a kind of 'shock' that, if large enough, is able to move a system out of its attractor state and potentially into a new regime. Learn More about Perturbation →


Peter Allen

x Learn More about Peter Allen →


Peter Sunley

x Learn More about Peter Sunley →


Phase Space

Phase space is an abstract concept that refers to all possible behaviors available to an agent within a complex system. Learn More about Phase Space →


Pierre Belanger

x Learn More about Pierre Belanger →


Pierre Paul Grasse

x Learn More about Pierre Paul Grasse →


Positive Feedback

Positive Feedback serves to amplify particular behaviors, such that a small change in initial conditions can engender a large change in overall system behavior over the course of time. Learn More about Positive Feedback →


Power Laws

CAS tend to self-organize to a critical state, where the distributions of agents relative to their impact on the system follows a power-law distribution. Learn More about Power Laws →


Preferential Attachment

Think of preferential attachment as an attribute of when 'the rich get richer' within a networked system. This occurs when nodes that have a lot of links tend to attract more links as other nodes enter the system resulting in super-nodes. {{network-topology}} Learn More about Preferential Attachment →


Redundancy

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Rem Koolhaas

x Learn More about Rem Koolhaas →


René Thom

x Learn More about René Thom →


Requisite Variety

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Rhizomes

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Robert Axelrod

x Learn More about Robert Axelrod →


Ron Boschma

x Learn More about Ron Boschma →


Ron Martin

x Learn More about Ron Martin →


Ronald Rietveld

x Learn More about Ronald Rietveld →


Ross Ashby

x Learn More about Ross Ashby →


Rule Based

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Safe to Fail

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Seewall Wright

x Learn More about Seewall Wright →


Self Similarity

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Self-Organized Criticality

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Sensitive to Initial Conditions

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Sergio Porta

x Learn More about Sergio Porta →


Signals / Stigmergy

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Stabilized Assemblages

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Stan Allen

x Learn More about Stan Allen →


STEALTH.unlimited

x Learn More about STEALTH.unlimited →


Stephen Marshall

x Learn More about Stephen Marshall →


Stephen Wolfram

x Learn More about Stephen Wolfram →


Steven Strogatz

Relates to understanding of networks Learn More about Steven Strogatz →


Steward Pickett

x Learn More about Steward Pickett →


Stewart Brand

x Learn More about Stewart Brand →


Stuart Kauffman

jkdlsj Learn More about Stuart Kauffman →


Swarm Behavior

Swarm behavior - examples of which happen in the natural world - is an evocative example of complex system wherein simple agents (birds, ants, fish), can collectively display emergent forms. Learn More about Swarm Behavior →


The Architecture of Complexity

This 1962 article by Herbert Simon, provides a key source for defining CAS Learn More about The Architecture of Complexity →


The Virtual

'The Virtual' is a term used by Deleuze and Guattari that parallels the idea of {{phase-space}}. The Virtual are aspects of an entity that can manifest, but may not yet have manifested. Learn More about The Virtual →


Time/Iterations

CAS systems unfold over the course of time, as agents continuously adjust their behaviors in response to feedback. Each iteration of agent behavior can be considered as a 'test' - with productive tests being reinforced and unproductive tests being abandoned. The more time to produce tests, and the more iterations of tests, the more likely a system is to gravitate towards fit configurations. Learn More about Time/Iterations →


Tom Evans

x Learn More about Tom Evans →


Unfolds via Interactions

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Unit Based

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Variables

Another way to speak about {{large-number-elements}} Caramelization half and half robust kopi-luwak, brewed, foam affogato grounds extraction plunger pot, bar single shot froth eu shop latte et, chicory, steamed seasonal grounds dark organic. Learn More about Variables →


Vilfredo Pareto

x Learn More about Vilfredo Pareto →


Von Forster

important guy Learn More about Von Forster →


Warren Weaver

x Learn More about Warren Weaver →