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A '''complex system''' is a [[system]] composed of many components which may interact with each other. In many cases it is useful to represent such a system as a network where the nodes represent the components and the links their interactions. Examples of complex systems are Earth's global [[climate]], [[organisms]], the [[human brain]], social and economic organizations (like [[cities]]), an [[ecosystem]], a living [[Cell (biology)|cell]], and ultimately the entire [[universe]]. |
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Complex systems are [[system]]s whose behavior is intrinsically difficult to model due to the dependencies, relationships, or interactions between their parts or between a given system and its environment. Systems that are "[[Complexity|complex]]" have distinct properties that arise from these relationships, such as [[Nonlinear system|nonlinearity]], [[emergence]], [[spontaneous order]], [[Complex adaptive system|adaptation]], and [[Feedback|feedback loops]], among others. Because such systems appear in a wide variety of fields, the commonalities among them have become the topic of their own independent area of research. |
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== Overview == |
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The term ''complex systems'' often refers to the study of complex systems, which is an approach to science that investigates how relationships between a system's parts give rise to its collective behaviors and how the system interacts and forms relationships with its environment.<ref>{{cite journal|last=Bar-Yam|first=Yaneer|date=2002|title=General Features of Complex Systems|url=http://www.eolss.net/sample-chapters/c15/E1-29-01-00.pdf|journal=Encyclopedia of Life Support Systems|publisher=[[Encyclopedia of Life Support Systems|EOLSS]] [[UNESCO]] Publishers, Oxford, UK|doi=|pmid=|accessdate=16 September 2014}}</ref> The study of complex systems regards collective, or system-wide, behaviors as the fundamental object of study; for this reason, complex systems can be understood as an alternative paradigm to [[reductionism]], which attempts to explain systems in terms of their constituent parts and the individual interactions between them. |
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As an interdisciplinary domain, complex systems draws contributions from many different fields, such as the study of [[self-organization]] from physics, that of [[spontaneous order]] from the social sciences, [[Chaos theory|chaos]] from mathematics, [[Complex adaptive system|adaptation]] from biology, and many others. ''Complex systems'' is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines, including [[statistical physics]], [[information theory]], [[Nonlinear system|nonlinear dynamics]], [[anthropology]], [[computer science]], [[meteorology]], [[sociology]], [[economics]], [[psychology]], and [[biology]]. |
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== Key concepts == |
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=== Systems === |
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[[File:OpenSystemRepresentation.svg|thumb|252px|''Open systems'' have input and output flows, representing exchanges of matter, energy or information with their surroundings.]] |
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Complex systems is chiefly concerned with the behaviors and properties of ''[[system]]s''. A system, broadly defined, is a set of entities that, through their interactions, relationships, or dependencies, form a unified whole. It is always defined in terms of its ''boundary'', which determines the entities that are or are not part of the system. Entities lying outside the system then become part of the system's ''environment''. |
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A system can exhibit ''properties'' that produce ''behaviors'' which are distinct from the properties and behaviors of its parts; these system-wide or ''global'' properties and behaviors are characteristics of how the system interacts with or appears to its environment, or of how its parts behave (say, in response to external stimuli) by virtue of being within the system. The notion of ''behavior'' implies that the study of systems is also concerned with processes that take place over time (or, in [[mathematics]], some other [[phase space]] [[Parametrization|parameterization]]). Because of their broad, interdisciplinary applicability, systems concepts play a central role in complex systems. |
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As a field of study, complex systems is a subset of [[systems theory]]. General systems theory focuses similarly on the collective behaviors of interacting entities, but it studies a much broader class of systems, including non-complex systems where traditional reductionist approaches may remain viable. Indeed, systems theory seeks to explore and describe ''all'' classes of systems, and the invention of categories that are useful to researchers across widely varying fields is one of systems theory's main objectives. |
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As it relates to complex systems, systems theory contributes an emphasis on the way relationships and dependencies between a system's parts can determine system-wide properties. It also contributes the interdisciplinary perspective of the study of complex systems: the notion that shared properties link systems across disciplines, justifying the pursuit of modeling approaches applicable to complex systems wherever they appear. Specific concepts important to complex systems, such as emergence, feedback loops, and adaptation, also originate in systems theory. |
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=== Complexity === |
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Systems exhibit complexity when difficulties with modeling them are endemic. This means their behaviors cannot be understood apart from the very properties that make them difficult to model, and they are governed entirely, or almost entirely, by the behaviors those properties produce. Any modeling approach that ignores such difficulties or characterizes them as noise, then, will necessarily produce models that are neither accurate nor useful. As yet no fully general theory of complex systems has emerged for addressing these problems, so researchers must solve them in domain-specific contexts. Researchers in complex systems address these problems by viewing the chief task of modeling to be capturing, rather than reducing, the complexity of their respective systems of interest. |
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While no generally accepted exact definition of complexity exists yet, there are many archetypal examples of complexity. Systems can be complex if, for instance, they have [[Chaos theory|chaotic]] behavior (behavior that exhibits extreme sensitivity to initial conditions), or if they have [[Emergence|emergent]] properties (properties that are not apparent from their components in isolation but which result from the relationships and dependencies they form when placed together in a system), or if they are computationally intractable to model (if they depend on a number of parameters that grows too rapidly with respect to the size of the system). |
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=== Networks === |
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The interacting components of a complex system form a [[Network theory|network]], which is a collection of discrete objects and relationships between them, usually depicted as a [[Graph (discrete mathematics)|graph]] of vertices connected by edges. Networks can describe the relationships between individuals within an organization, between [[logic gate]]s in a [[Circuit (computer science)|circuit]], between [[gene]]s in [[gene regulatory network]]s, or between any other set of related entities. |
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Networks often describe the sources of complexity in complex systems. Studying complex systems as networks therefore enables many useful applications of [[graph theory]] and [[network science]]. Some complex systems, for example, are also [[complex network]]s, which have properties such as power-law degree distributions that readily lend themselves to emergent or chaotic behavior. The fact that the number of edges in a [[complete graph]] grows [[Quadratic growth|quadratically]] in the number of vertices sheds additional light on the source of complexity in large networks: as a network grows, the number of relationships between entities quickly dwarfs the number of entities in the network. |
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=== Nonlinearity === |
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[[File:A Trajectory Through Phase Space in a Lorenz Attractor.gif|frame|border|right|A sample solution in the Lorenz attractor when ρ = 28, σ = 10, and β = 8/3]] |
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Complex systems often have nonlinear behavior, meaning they may respond in different ways to the same input depending on their state or context. In [[mathematics]] and [[physics]], nonlinearity describes systems in which a change in the size of the input does not produce a proportional change in the size of the output. For a given change in input, such systems may yield significantly greater than or less than proportional changes in output, or even no output at all, depending on the current state of the system or its parameter values. |
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Of particular interest to complex systems are [[nonlinear dynamical systems]], which are systems of [[differential equation]]s that have one or more nonlinear terms. Some nonlinear dynamical systems, such as the [[Lorenz system]], can produce a mathematical phenomenon known as [[Chaos theory|chaos]]. Chaos as it applies to complex systems refers to the sensitive dependence on initial conditions, or "[[butterfly effect]]," that a complex system can exhibit. In such a system, small changes to initial conditions can lead to dramatically different outcomes. Chaotic behavior can therefore be extremely hard to model numerically, because small rounding errors at an intermediate stage of computation can cause the model to generate completely inaccurate output. Furthermore, if a complex system returns to a state similar to one it held previously, it may behave completely differently in response to exactly the same stimuli, so chaos also poses challenges for extrapolating from past experience. |
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=== Emergence === |
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[[File:Gospers glider gun.gif|frame|right|[[Bill Gosper|Gosper's]] [[Gun (cellular automaton)|Glider Gun]] creating "[[Glider (Conway's Life)|gliders]]" in the cellular automaton [[Conway's Game of Life]]<ref>[[Daniel Dennett]] (1995), ''[[Darwin's Dangerous Idea]]'', Penguin Books, London, {{ISBN|978-0-14-016734-4}}, {{ISBN|0-14-016734-X}}</ref>]] |
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Another common feature of complex systems is the presence of emergent behaviors and properties: these are traits of a system which are not apparent from its components in isolation but which result from the interactions, dependencies, or relationships they form when placed together in a system. [[Emergence]] broadly describes the appearance of such behaviors and properties, and has applications to systems studied in both the social and physical sciences. While emergence is often used to refer only to the appearance of unplanned organized behavior in a complex system, emergence can also refer to the breakdown of organization; it describes any phenomena which are difficult or even impossible to predict from the smaller entities that make up the system. |
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One example of complex system whose emergent properties have been studied extensively is [[Cellular automaton|cellular automata]]. In a cellular automaton, a grid of cells, each having one of finitely many states, evolves over time according to a simple set of rules. These rules guide the "interactions" of each cell with its neighbors. Although the rules are only defined locally, they have been shown capable of producing globally interesting behavior, for example in [[Conway's Game of Life]]. |
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==== Spontaneous order and self-organization ==== |
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When emergence describes the appearance of unplanned order, it is [[spontaneous order]] (in the social sciences) or [[self-organization]] (in physical sciences). Spontaneous order can be seen in [[herd behavior]], whereby a group of individuals coordinates their actions without centralized planning. Self-organization can be seen in the global symmetry of certain [[crystal]]s, for instance the apparent radial [[symmetry]] of [[snowflake]]s, which arises from purely local [[Intermolecular force|attractive and repulsive forces]] both between water molecules and between water molecules and their surrounding environment. |
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=== Adaptation === |
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[[Complex adaptive system]]s are special cases of complex systems that are [[adaptive]] in that they have the capacity to change and learn from experience. Examples of complex adaptive systems include the [[stock market]], social insect and [[ant]] colonies, the [[biosphere]] and the [[ecosystem]], the [[Human brain|brain]] and the [[immune system]], the [[Cell (biology)|cell]] and the developing [[embryo]], [[Manufacturing|manufacturing businesses]] and any human social group-based endeavor in a cultural and [[social system]] such as [[Political party|political parties]] or [[Community|communities]]. |
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==Features== |
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Complex systems may have the following features:<ref>{{cite book|title=Risk and Precaution |
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|author=Alan Randall |
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|author-link=Alan Randall |
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|isbn=9781139494793 |
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|url=https://books.google.de/books?id=IlHj3fvJzMsC&printsec=frontcover&dq=inauthor:%22Alan+Randall%22&hl=de&sa=X&ved=0ahUKEwjA_u_a2pXLAhVIOpoKHZRLAacQ6AEIODAE#v=onepage&q&f=false |
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|publisher=Cambridge University Press|year=2011}}</ref> |
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;[[Cascading failure]]s |
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:Due to the strong coupling between components in complex systems, a failure in one or more components can lead to cascading failures which may have catastrophic consequences on the functioning of the system.<ref>{{cite journal|author=S. V. Buldyrev|author2=R. Parshani|author3=G. Paul|author4=H. E. Stanley|author5=S. Havlin|author5-link=Shlomo Havlin|title=Catastrophic cascade of failures in interdependent networks|journal=Nature|year=2010|volume=464|pages=08932|url=http://havlin.biu.ac.il/Publications.php?keyword=Catastrophic+cascade+of+failures+in+interdependent+networks&year=*&match=all | doi = 10.1038/nature08932|pmid=20393559|issue=7291|arxiv = 0907.1182 |bibcode = 2010Natur.464.1025B }}</ref> |
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Localized attack may lead to cascading failures in spatial networks.<ref name="BerezinBashan2015">{{cite journal|last1=Berezin|first1=Yehiel|last2=Bashan|first2=Amir|last3=Danziger|first3=Michael M.|last4=Li|first4=Daqing|last5=Havlin|first5=Shlomo|title=Localized attacks on spatially embedded networks with dependencies|journal=Scientific Reports|volume=5|issue=1|year=2015|issn=2045-2322|doi=10.1038/srep08934}}</ref> |
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;Complex systems may be open |
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:Complex systems are usually [[Open system (systems theory)|open systems]] — that is, they exist in a [[thermodynamic]] gradient and dissipate energy. In other words, complex systems are frequently far from energetic [[thermodynamic equilibrium|equilibrium]]: but despite this flux, there may be [[pattern stability]], see [[synergetics (Haken)|synergetics]]. |
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;Complex systems may have a memory |
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:The history of a complex system may be important. Because complex systems are [[dynamical systems]] they change over time, and prior states may have an influence on present states. More formally, complex systems often exhibit spontaneous failures and recovery as well as [[hysteresis]].<ref name="MajdandzicPodobnik2013">{{cite journal|last1=Majdandzic|first1=Antonio|last2=Podobnik|first2=Boris|last3=Buldyrev|first3=Sergey V.|last4=Kenett|first4=Dror Y.|last5=Havlin|first5=Shlomo|last6=Eugene Stanley|first6=H.|title=Spontaneous recovery in dynamical networks|journal=Nature Physics|volume=10|issue=1|year=2013|pages=34–38|issn=1745-2473|doi=10.1038/nphys2819}}</ref> |
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Interacting systems may have complex hysteresis of many transitions.<ref name="MajdandzicBraunstein2016">{{cite journal|last1=Majdandzic|first1=Antonio|last2=Braunstein|first2=Lidia A.|last3=Curme|first3=Chester|last4=Vodenska|first4=Irena|last5=Levy-Carciente|first5=Sary|last6=Eugene Stanley|first6=H.|last7=Havlin|first7=Shlomo|title=Multiple tipping points and optimal repairing in interacting networks|journal=Nature Communications|volume=7|year=2016|pages=10850|issn=2041-1723|doi=10.1038/ncomms10850}}</ref> |
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;Complex systems may be [[Hierarchy#Nested hierarchy|nested]] |
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:The components of a complex system may themselves be complex systems. For example, an [[Economics|economy]] is made up of [[organisation]]s, which are made up of [[person|people]], which are made up of [[cell (biology)|cells]] - all of which are complex systems. |
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;Dynamic network of multiplicity |
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:As well as [[coupling]] rules, the dynamic [[Biological network|network]] of a complex system is important. [[Small-world network|Small-world]] or [[Scale-free network|scale-free]] networks<ref>{{cite journal|last=A. L. Barab´asi|first=R. Albert|title=Statistical mechanics of complex networks|journal=Reviews of Modern Physics |year=2002|volume=74|pages=47–94|url=http://rmp.aps.org/abstract/RMP/v74/i1/p47_1 | doi = 10.1103/RevModPhys.74.47|bibcode=2002RvMP...74...47A|arxiv = cond-mat/0106096 }}</ref><ref>{{cite book|title= Networks: An Introduction|author= M. Newman|year=2010|publisher=Oxford University Press|isbn=978-0-19-920665-0}}</ref><ref>{{cite book|title=[[Complex Networks]]: Structure, Robustness and Function|last=Reuven Cohen|first=Shlomo Havlin|author-link=Shlomo Havlin|year=2010|publisher=Cambridge University Press|isbn=978-0-521-84156-6}}</ref> which have many local interactions and a smaller number of inter-area connections are often employed. Natural complex systems often exhibit such topologies. In the human [[Cerebral cortex|cortex]] for example, we see dense local connectivity and a few very long [[axonal|axon]] projections between regions inside the cortex and to other brain regions. |
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;May produce emergent phenomena |
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:Complex systems may exhibit behaviors that are [[emergence|emergent]], which is to say that while the results may be sufficiently determined by the activity of the systems' basic constituents, they may have properties that can only be studied at a higher level. For example, the [[termites]] in a mound have physiology, biochemistry and biological development that are at one level of analysis, but their [[social behavior]] and mound building is a property that emerges from the collection of termites and needs to be analysed at a different level. |
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;Relationships are non-linear |
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:In practical terms, this means a small perturbation may cause a large effect (see [[butterfly effect]]), a proportional effect, or even no effect at all. In linear systems, effect is ''always'' directly proportional to cause. See [[nonlinearity]]. |
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;Relationships contain feedback loops |
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:Both negative ([[damping]]) and positive (amplifying) [[feedback]] are always found in complex systems. The effects of an element's behaviour are fed back to in such a way that the element itself is altered. |
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== History == |
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[[File:Complexity Map.svg|thumb|360px|A perspective on the development of complexity science: http://www.art-sciencefactory.com/complexity-map_feb09.html|alt= http://www.art-sciencefactory.com/complexity-map_feb09.html]] |
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Although it is arguable that humans have been studying complex systems for thousands of years, the modern scientific study of complex systems is relatively young in comparison to established fields of science such as [[physics]] and [[chemistry]]. The history of the scientific study of these systems follows several different research trends. |
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In the area of [[mathematics]], arguably the largest contribution to the study of complex systems was the discovery of [[chaos theory|chaos]] in [[deterministic]] systems, a feature of certain [[dynamical systems]] that is strongly related to [[nonlinearity]].<ref>[http://www.irit.fr/COSI/training/complexity-tutorial/history-of-complex-systems.htm History of Complex Systems<!-- Bot generated title -->]</ref> The study of [[neural networks]] was also integral in advancing the mathematics needed to study complex systems. |
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The notion of [[self-organizing]] systems is tied up to work in [[nonequilibrium thermodynamics]], including that pioneered by [[chemist]] and [[Nobel laureate]] [[Ilya Prigogine]] in his study of [[dissipative structures]]. Even older is the work by [[Hartree-Fock]] c.s. on the [[quantum-chemistry]] equations and later calculations of the structure of molecules which can be regarded as one of the earliest examples of emergence and emergent wholes in science. |
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The first research institute focused on complex systems, the [[Santa Fe Institute]], was founded in 1984.<ref>Ledford, H. (2015). [http://www.nature.com/news/how-to-solve-the-world-s-biggest-problems-1.18367 How to solve the world's biggest problems]. Nature, 525(7569), 308-311.</ref> Early Santa Fe Institute participants included physics Nobel laureates [[Murray Gell-Mann]] and [[Philip Warren Anderson|Philip Anderson]], economics Nobel laureate [[Kenneth Arrow]], and Manhattan Project scientists [[George Cowan]] and [[Herbert L. Anderson|Herb Anderson]].<ref>Waldrop, M. M. (1993). [https://books.google.com/books/about/Complexity.html?id=JTRJxYK_tZsC Complexity: The emerging science at the edge of order and chaos.] Simon and Schuster.</ref> Today, there are over 50 [[Complex systems#Institutes and research centers|institutes and research centers]] focusing on complex systems. |
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The earliest precursor to modern complex systems theory can be found in the classical political economy of the [[Scottish Enlightenment]], later developed by the [[Austrian school of economics]], which argues that order in market systems is spontaneous (or [[Emergence|emergent]]) in that it is the result of human action, but not the execution of any human design.<ref>{{cite book |last=Ferguson |first=Adam |authorlink=Adam Ferguson |coauthors= |title=An Essay on the History of Civil Society |publisher=T. Cadell |year=1767 |location=London |pages=Part the Third, Section II, p. 205 |url=http://oll.libertyfund.org/index.php?option=com_staticxt&staticfile=show.php%3Ftitle=1428&Itemid=28 |doi= |id= |isbn= |nopp=true}}</ref><ref>Friedrich Hayek, "The Results of Human Action but Not of Human Design" in ''New Studies in Philosophy, Politics, Economics'', Chicago: University of Chicago Press, 1978, pp. 96–105.</ref> |
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Upon this the Austrian school developed from the 19th to the early 20th century the [[economic calculation problem]], along with the concept of [[dispersed knowledge]], which were to fuel debates against the then-dominant [[Keynesian economics]]. This debate would notably lead economists, politicians and other parties to explore the question of [[Economic calculation problem#Computational complexity|computational complexity]].{{Citation needed|date=November 2016}} |
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A pioneer in the field, and inspired by [[Karl Popper]]'s and [[Warren Weaver]]'s works, Nobel prize economist and philosopher [[Friedrich Hayek]] dedicated much of his work, from early to the late 20th century, to the study of complex phenomena,<ref>Bruce J. Caldwell, Popper and Hayek: [http://www.unites.uqam.ca/philo/pdf/Caldwell_2003-01.pdf Who influenced whom?], Karl Popper 2002 Centenary Congress, 2002.</ref> not constraining his work to human economies but venturing into other fields such as [[psychology]],<ref>Friedrich von Hayek, ''The Sensory Order: An Inquiry into the Foundations of Theoretical Psychology'', The University of Chicago Press, 1952.</ref> [[biology]] and [[cybernetics]]. [[Gregory Bateson]] played a key role in establishing the connection between anthropology and systems theory; he recognized that the interactive parts of cultures function much like ecosystems. |
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In [[mathematics]], arguably the largest contribution to the study of complex systems was the discovery of [[Chaos theory|chaos]] in [[deterministic]] systems, a feature of certain [[dynamical systems]] that is strongly related to [[nonlinearity]].<ref>[http://www.irit.fr/COSI/training/complexity-tutorial/history-of-complex-systems.htm History of Complex Systems<!-- Bot generated title -->]</ref> |
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The notion of [[self-organizing]] systems is tied to work in [[nonequilibrium thermodynamics]], including that pioneered by [[chemist]] and [[Nobel laureate]] [[Ilya Prigogine]] in his study of [[dissipative structures]]. Even older is the work by [[Hartree-Fock]] c.s. on the [[quantum-chemistry]] equations and later calculations of the structure of molecules which can be regarded as one of the earliest examples of emergence and emergent wholes in science. |
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The first research institute focused on complex systems, the [[Santa Fe Institute]], was founded in 1984.<ref>{{cite journal | last1 = Ledford | first1 = H | year = 2015 | title = How to solve the world's biggest problems | url = http://www.nature.com/news/how-to-solve-the-world-s-biggest-problems-1.18367 | journal = Nature | volume = 525 | issue = 7569| pages = 308–311 | doi=10.1038/525308a}}</ref> Early Santa Fe Institute participants included physics Nobel laureates [[Murray Gell-Mann]] and [[Philip Warren Anderson|Philip Anderson]], economics Nobel laureate [[Kenneth Arrow]], and Manhattan Project scientists [[George Cowan]] and [[Herbert L. Anderson|Herb Anderson]].<ref>Waldrop, M. M. (1993). [https://books.google.com/books/about/Complexity.html?id=JTRJxYK_tZsC Complexity: The emerging science at the edge of order and chaos.] Simon and Schuster.</ref> Today, there are over 50 [[#Institutes_and_research_centers|institutes and research centers]] focusing on complex systems. |
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== Applications of complex systems == |
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===Complexity in practice=== |
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The traditional approach to dealing with complexity is to reduce or constrain it. Typically, this involves compartmentalisation: dividing a large system into separate parts. Organizations, for instance, divide their work into departments that each deal with separate issues. Engineering systems are often designed using modular components. However, modular designs become susceptible to failure when issues arise that bridge the divisions. |
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===Complexity management=== |
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As projects and [[acquisitions]] become increasingly complex, companies and governments are challenged to find effective ways to manage mega-acquisitions such as the Army [[Future Combat Systems]]. Acquisitions such as the [[Future Combat Systems|FCS]] rely on a web of interrelated parts which interact unpredictably. As acquisitions become more network-centric and complex, businesses will be forced to find ways to manage complexity while governments will be challenged to provide effective governance to ensure flexibility and resiliency.<ref>[http://csis.org/files/publication/090410_Organizing_for_a_Complex_World_The_Way_Ahead_0.pdf CSIS paper: "Organizing for a Complex World: The Way Ahead]</ref> |
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===Complexity economics=== |
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Over the last decades, within the emerging field of [[complexity economics]] new predictive tools have been developed to explain economic growth. Such is the case with the models built by the [[Santa Fe Institute]] in 1989 and the more recent [[economic complexity index]] (ECI), introduced by the [[MIT]] physicist [[Cesar A. Hidalgo]] and the [[Harvard]] economist [[Ricardo Hausmann]]. Based on the ECI, Hausmann, Hidalgo and their team of [[The Observatory of Economic Complexity]] have [[List of countries by future GDP (based on ECI) estimates|produced GDP forecasts for the year 2020]].{{Citation needed|date=February 2016}} |
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=== Complexity and education === |
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Focusing on issues of student persistence with their studies, Forsman, Moll and Linder explore the "viability of using complexity science as a frame to extend methodological applications for physics education research," finding that "framing a social network analysis within a complexity science perspective offers a new and powerful applicability across a broad range of PER topics."<ref>{{Cite journal|last=Forsman|first=Jonas|last2=Moll|first2=Rachel|last3=Linder|first3=Cedric|date=2014|title=Extending the theoretical framing for physics education research: An illustrative application of complexity science|url=http://link.aps.org/doi/10.1103/PhysRevSTPER.10.020122|journal=Physical Review Special Topics - Physics Education Research|volume=10|issue=2|doi=10.1103/PhysRevSTPER.10.020122|id=http://hdl.handle.net/10613/2583}}</ref> |
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===Complexity and modeling=== |
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One of Friedrich Hayek's main contributions to early complexity theory is his distinction between the human capacity to predict the behaviour of simple systems and its capacity to predict the behaviour of complex systems through [[Scientific modelling|modeling]]. He believed that economics and the sciences of complex phenomena in general, which in his view included biology, psychology, and so on, could not be modeled after the sciences that deal with essentially simple phenomena like physics.<ref>[http://www.reason.com/news/show/33304.html Reason Magazine - The Road from Serfdom<!-- Bot generated title -->]</ref> Hayek would notably explain that complex phenomena, through modeling, can only allow pattern predictions, compared with the precise predictions that can be made out of non-complex phenomena.<ref>[http://nobelprize.org/nobel_prizes/economics/laureates/1974/hayek-lecture.html Friedrich August von Hayek - Prize Lecture<!-- Bot generated title -->]</ref> |
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===Complexity and chaos theory=== |
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Complexity theory is rooted in [[chaos theory]], which in turn has its origins more than a century ago in the work of the French mathematician [[Henri Poincaré]]. Chaos is sometimes viewed as extremely complicated information, rather than as an absence of order.<ref>Hayles, N. K. (1991). ''Chaos Bound: Orderly Disorder in Contemporary Literature and Science''. Cornell University Press, Ithaca, NY.</ref> Chaotic systems remain deterministic, though their long-term behavior can be difficult to predict with any accuracy. With perfect knowledge of the initial conditions and of the relevant equations describing the chaotic system's behavior, one can theoretically make perfectly accurate predictions about the future of the system, though in practice this is impossible to do with arbitrary accuracy. [[Ilya Prigogine]] argued<ref>Prigogine, I. (1997). ''The End of Certainty'', The Free Press, New York.</ref> that complexity is non-deterministic, and gives no way whatsoever to precisely predict the future.<ref>See also {{cite journal |author=D. Carfì |year=2008 |title=Superpositions in Prigogine approach to irreversibility |journal=AAPP: Physical, Mathematical, and Natural Sciences |volume=86 |issue=1 |pages=1–13 |url=http://cab.unime.it/journals/index.php/AAPP/article/view/384/0 |format= |accessdate=}}.</ref> |
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The emergence of complexity theory shows a domain between deterministic order and randomness which is complex.<ref name="PC98">[[Paul Cilliers|Cilliers, P.]] (1998). ''Complexity and Postmodernism: Understanding Complex Systems'', Routledge, London.</ref> This is referred as the "[[edge of chaos]]".<ref>[[Per Bak]] (1996). ''How Nature Works: The Science of Self-Organized Criticality'', Copernicus, New York, U.S.</ref> |
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[[File:Lorenz attractor yb.svg|thumb|left|200px|A plot of the [[Lorenz attractor]].]] |
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When one analyzes complex systems, sensitivity to initial conditions, for example, is not an issue as important as it is within chaos theory, in which it prevails. As stated by Colander,<ref>Colander, D. (2000). ''The Complexity Vision and the Teaching of Economics'', E. Elgar, Northampton, Massachusetts.</ref> the study of complexity is the opposite of the study of chaos. Complexity is about how a huge number of extremely complicated and dynamic sets of relationships can generate some simple behavioral patterns, whereas chaotic behavior, in the sense of deterministic chaos, is the result of a relatively small number of non-linear interactions.<ref name="PC98"/> |
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Therefore, the main difference between chaotic systems and complex systems is their history.<ref>Buchanan, M. (2000). ''Ubiquity : Why catastrophes happen'', three river press, New-York.</ref> Chaotic systems do not rely on their history as complex ones do. Chaotic behaviour pushes a system in equilibrium into chaotic order, which means, in other words, out of what we traditionally define as 'order'.{{clarify|date=September 2011}} On the other hand, complex systems evolve far from equilibrium at the [[edge of chaos]]. They evolve at a critical state built up by a history of irreversible and unexpected events, which physicist [[Murray Gell-Mann]] called "an accumulation of frozen accidents."<ref>Gell-Mann, M. (1995). What is Complexity? Complexity 1/1, 16-19</ref> In a sense chaotic systems can be regarded as a subset of complex systems distinguished precisely by this absence of historical dependence. Many real complex systems are, in practice and over long but finite time periods, robust. However, they do possess the potential for radical qualitative change of kind whilst retaining systemic integrity. Metamorphosis serves as perhaps more than a metaphor for such transformations. |
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{{clear left}} |
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===Complexity and network science=== |
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A complex system is usually composed of many components and their interactions. Such a system can be represented by a network where nodes represent the components and links represent their interactions.<ref name="DorogovtsevMendes2003">{{cite journal|last1=Dorogovtsev|first1=S.N.|last2=Mendes|first2=J.F.F.|year=2003|doi=10.1093/acprof:oso/9780198515906.001.0001|title=Evolution of Networks}}</ref> |
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<ref name="Fortunato2011">{{cite journal|last1=Fortunato|first1=Santo|title=Reuven Cohen and Shlomo Havlin: Complex Networks|journal=Journal of Statistical Physics|volume=142|issue=3|year=2011|pages=640–641|issn=0022-4715|doi=10.1007/s10955-011-0129-7}}</ref><ref name="Newman2010">{{cite journal|last1=Newman|first1=Mark|year=2010|doi=10.1093/acprof:oso/9780199206650.001.0001|title=Networks}}</ref> for example, the INTERNET can be represented as a network composed of nodes (computers) and links (direct connections between computers). Its resilience to failures was studied using percolation theory in.<ref name="CohenErez2001">{{cite journal|last1=Cohen|first1=Reuven|last2=Erez|first2=Keren|last3=ben-Avraham|first3=Daniel|last4=Havlin|first4=Shlomo|title=Cohen, Erez, ben-Avraham, and Havlin Reply:|journal=Physical Review Letters|volume=87|issue=21|year=2001|issn=0031-9007|doi=10.1103/PhysRevLett.87.219802|bibcode=2001PhRvL..87u9802C}}</ref> |
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Other examples are social networks, airline networks,<ref name="BarratBarthelemy2004">{{cite journal|last1=Barrat|first1=A.|last2=Barthelemy|first2=M.|last3=Pastor-Satorras|first3=R.|last4=Vespignani|first4=A.|title=The architecture of complex weighted networks|journal=Proceedings of the National Academy of Sciences|volume=101|issue=11|year=2004|pages=3747–3752|issn=0027-8424|doi=10.1073/pnas.0400087101|pmid=15007165|pmc=374315}}</ref> biological networks and climate networks.<ref name="YamasakiGozolchiani2008">{{cite journal|last1=Yamasaki|first1=K.|last2=Gozolchiani|first2=A.|last3=Havlin|first3=S.|title=Climate Networks around the Globe are Significantly Affected by El Niño|journal=Physical Review Letters|volume=100|issue=22|year=2008|issn=0031-9007|doi=10.1103/PhysRevLett.100.228501|pmid=18643467|page=228501}}</ref> |
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Networks can also fail and recover spontaneously. For modeling this phenomenon see ref.<ref name="MajdandzicPodobnik2013">{{cite journal|last1=Majdandzic|first1=Antonio|last2=Podobnik|first2=Boris|last3=Buldyrev|first3=Sergey V.|last4=Kenett|first4=Dror Y.|last5=Havlin|first5=Shlomo|last6=Eugene Stanley|first6=H.|title=Spontaneous recovery in dynamical networks|journal=Nature Physics|volume=10|issue=1|year=2013|pages=34–38|issn=1745-2473|doi=10.1038/nphys2819}}</ref> |
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Interacting complex systems can be modeled as networks of networks. For their breakdown and recovery properties see <ref name="GaoBuldyrev2011">{{cite journal|last1=Gao|first1=Jianxi|last2=Buldyrev|first2=Sergey V.|last3=Stanley|first3=H. Eugene|last4=Havlin|first4=Shlomo|title=Networks formed from interdependent networks|journal=Nature Physics|volume=8|issue=1|year=2011|pages=40–48|issn=1745-2473|doi=10.1038/nphys2180|bibcode=2012NatPh...8...40G}}</ref> |
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<ref name="MajdandzicBraunstein2016">{{cite journal|last1=Majdandzic|first1=Antonio|last2=Braunstein|first2=Lidia A.|last3=Curme|first3=Chester|last4=Vodenska|first4=Irena|last5=Levy-Carciente|first5=Sary|last6=Eugene Stanley|first6=H.|last7=Havlin|first7=Shlomo|title=Multiple tipping points and optimal repairing in interacting networks|journal=Nature Communications|volume=7|year=2016|pages=10850|issn=2041-1723|doi=10.1038/ncomms10850}}</ref> |
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===General form of complexity computation=== |
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The computational law of reachable optimality<ref>Wenliang Wang (2015). Pooling Game Theory and Public Pension Plan. {{ISBN|978-1507658246}}. Chapter 4.</ref> is established as a general form of computation for ordered system and it reveals complexity computation is a compound computation of optimal choice and optimality driven reaching pattern overtime underlying a specific and any experience path of ordered system within the general limitation of system integrity. |
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The computational law of reachable optimality has four key components as described below. |
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1. '''Reachability of Optimality''': Any intended optimality shall be reachable. Unreachable optimality has no meaning for a member in the ordered system and even for the ordered system itself. |
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2. '''Prevailing and Consistency''': Maximizing reachability to explore best available optimality is the prevailing computation logic for all members in the ordered system and is accommodated by the ordered system. |
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3. '''Conditionality''': Realizable tradeoff between reachability and optimality depends primarily upon the initial bet capacity and how the bet capacity evolves along with the payoff table update path triggered by bet behavior and empowered by the underlying law of reward and punishment. Precisely, it is a sequence of conditional events where the next event happens upon reached status quo from experience path. |
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4. '''Robustness''': The more challenge a reachable optimality can accommodate, the more robust it is in term of path integrity. |
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There are also four computation features in the law of reachable optimality. |
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1. '''Optimal Choice''': Computation in realizing Optimal Choice can be very simple or very complex. A simple rule in Optimal Choice is to accept whatever is reached, Reward As You Go (RAYG). A Reachable Optimality computation reduces into optimizing reachability when RAYG is adopted. The Optimal Choice computation can be more complex when multiple NE strategies present in a reached game. |
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2. '''Initial Status''': Computation is assumed to start at an interested beginning even the absolute beginning of an ordered system in nature may not and need not present. An assumed neutral Initial Status facilitates an artificial or a simulating computation and is not expected to change the prevalence of any findings. |
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3. '''Territory''': An ordered system shall have a territory where the universal computation sponsored by the system will produce an optimal solution still within the territory. |
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4. '''Reaching Pattern''': The forms of Reaching Pattern in the computation space, or the Optimality Driven Reaching Pattern in the computation space, primarily depend upon the nature and dimensions of measure space underlying a computation space and the law of punishment and reward underlying the realized experience path of reaching. There are five basic forms of experience path we are interested in, persistently positive reinforcement experience path, persistently [[negative reinforcement]] experience path, mixed persistent pattern experience path, decaying scale experience path and selection experience path. |
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The compound computation in selection experience path includes current and lagging interaction, dynamic topological transformation and implies both invariance and variance characteristics in an ordered system's experience path. |
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In addition, the computation law of reachable optimality gives out the boundary between complexity model, chaotic model and determination model. When RAYG is the Optimal Choice computation, and the reaching pattern is a persistently positive experience path, persistently negative experience path, or mixed persistent pattern experience path, the underlying computation shall be a simple system computation adopting determination rules. If the reaching pattern has no persistent pattern experienced in RAYG regime, the underlying computation hints there is a chaotic system. When the optimal choice computation involves non-RAYG computation, it's a complexity computation driving the compound effect. |
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== Notable figures == |
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* [[Christopher Alexander]] |
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* [[Gregory Bateson]] |
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* [[Ludwig von Bertalanffy]] |
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* [[Samuel Bowles (economist)|Samuel Bowles]] |
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* [[Paul Cilliers]] |
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* [[Murray Gell-Mann]] |
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* [[Arthur Iberall]] |
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* [[Stuart Kauffman]] |
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* [[Cris Moore]] |
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* [http://www.billmckelvey.org/ Bill McKelvey] |
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* [[Jerry Sabloff]] |
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* [[Geoffrey West]] |
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* [[Yaneer Bar-Yam]] |
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* [[Walter Clemens, Jr.]] |
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== See also == |
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{{Portal|Systems science}} |
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{| |
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|- style="vertical-align:top" |
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|style="padding-right:2em"| |
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* [[Chaos theory]] |
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* [[Cybernetics]] |
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* [[Cognitive model#Dynamical systems|Cognitive modeling]] |
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* [[Cognitive Science]] |
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* [[Complex adaptive system]] |
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* [[Complex networks]] |
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* [[Complexity]] |
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* [[Complexity economics]] |
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* [[Decision engineering]] |
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* [[Dual-phase evolution]] |
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* [[Dynamical system]] |
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* [[Dynamical systems theory]] |
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|style="padding-right:2em"| |
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* [[Emergence]] |
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* [[Enterprise systems engineering]] |
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* [[Generative sciences]] |
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* [[Homeokinetics]] |
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* [[Interdependent networks]] |
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* [[Invisible hand]] |
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* [[Mixed reality]] |
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* [[Multi-agent system]] |
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* [[Network Science]] |
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* [[Nonlinearity]] |
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* [[Pattern-oriented modeling]] |
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* [[Percolation Theory]] |
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* [[Process architecture]] |
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| |
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* [[Systems theory]] |
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** [[Systems theory in anthropology|in anthropology]] |
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* [[Self-organization]] |
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* [[Sociology and complexity science]] |
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* {{longitem|style=line-height:1.35em|[[Volatility, uncertainty, complexity and ambiguity|Volatility, uncertainty, complexity<br/>and ambiguity]]}} |
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|} |
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== References == |
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{{Reflist}} |
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== Further reading == |
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* Bazin, A. (2014). [https://www.academia.edu/attachments/34737324/download_file?st=MTQxNzA5MzgyNywxMDguMjYuMTIzLjE2MSwxMzMzMjk5MA%3D%3D&s=swp-toolbar&ct=MTQxNzA5MzgyNyw2OTU5MCwxMzMzMjk5MA== [[Defeating ISIS]] and Their Complex Way of War] Small Wars Journal. |
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* Syed M. Mehmud (2011), [http://predictivemodeler.com/sitecontent/book/Ch06_Applications/Actuarial/HEC_Model/Healthcare%20Exchange%20Complexity%20Model%20-%20Report%20-%20Aug2011.pdf ''A Healthcare Exchange Complexity Model''] |
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* {{cite journal | last1 = Chu | first1 = D. | last2 = Strand | first2 = R. | last3 = Fjelland | first3 = R. | year = 2003 | title = Theories of complexity | url = | journal = Complexity | volume = 8 | issue = 3 | pages = 19–30 | doi = 10.1002/cplx.10059 }} |
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* [[Luis Amaral|L.A.N. Amaral]] and J.M. Ottino, [http://amaral-lab.org/media/publication_pdfs/Amaral-2004-Eur.Phys.J.B-38-147.pdf ''Complex networks — augmenting the framework for the study of complex system''], 2004. |
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* {{cite journal | last1 = Gell-Mann | first1 = Murray | year = 1995 | title = Let's Call It Plectics | url = http://www.santafe.edu/~mgm/Site/Publications_files/MGM%20118.pdf | format = PDF | journal = Complexity | volume = 1 | issue = 5 }} |
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* [[Nigel Goldenfeld]] and Leo P. Kadanoff, [http://guava.physics.uiuc.edu/~nigel/articles/complexity.html ''Simple Lessons from Complexity''], 1999 |
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* A. Gogolin, A. Nersesyan and A. Tsvelik, [http://www.cmth.bnl.gov/~tsvelik/theory.html ''Theory of strongly correlated systems ''], Cambridge University Press, 1999. |
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* Kelly, K. (1995). [http://www.kk.org/outofcontrol/contents.php ''Out of Control''], Perseus Books Group. |
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* {{cite journal | last1 = Donald Snooks | first1 = Graeme | year = 2008 | title = A general theory of complex living systems: Exploring the demand side of dynamics | url = | journal = Complexity | volume = 13 | issue = 6 | doi=10.1002/cplx.20225 | pages=12–20}} |
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* Sorin Solomon and Eran Shir, [http://www.europhysicsnews.org/index.php?option=article&access=standard&Itemid=129&url=/articles/epn/abs/2003/02/epn03204/epn03204.html ''Complexity; a science at 30''], 2003. |
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* [http://www.oeaw.ac.at/byzanz/repository/Preiser_WorkingPapers_Calculating_I.pdf Preiser-Kapeller, Johannes, "Calculating Byzantium. Social Network Analysis and Complexity Sciences as tools for the exploration of medieval social dynamics". August 2010] |
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* [[Walter Clemens, Jr.]], [https://web.archive.org/web/20150219221633/http://www.sunypress.edu/p-5782-complexity-science-and-world-af.aspx ''Complexity Science and World Affairs''], SUNY Press, 2013. |
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== External links == |
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{{Commons category|Complex systems}} |
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{{Wiktionary|complex systems}} |
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* {{cite web|url=http://www.openabm.org |title=The Open Agent-Based Modeling Consortium}} |
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* {{cite web|url=http://www.complexity.ecs.soton.ac.uk/ |title=Complexity Science Focus}} |
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* {{cite web|url=http://www.santafe.edu/ |title=Santa Fe Institute}} |
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* {{cite web|url=http://www.lsa.umich.edu/cscs/ |title=The Center for the Study of Complex Systems, Univ. of Michigan Ann Arbor}} |
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* {{cite web|url=http://indecs.eu/ |title=INDECS}} (Interdisciplinary Description of Complex Systems) |
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* {{cite web|url=http://www.ccsr.uiuc.edu/ |title=Center for Complex Systems Research, Univ. of Illinois}} |
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* {{cite web|url=http://havlin.biu.ac.il/course3.php |title=Introduction to complex systems - Short course by Shlomo Havlin}} |
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* {{cite web|url=http://www.eoearth.org/view/article/51cbed507896bb431f69154d/?topic=51cbfc79f702fc2ba8129ed7 |title=Complex Systems|date=October 24, 2013|author=Jessie Henshaw|publisher=[[Encyclopedia of Earth]]}} |
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{{Complex systems topics}} |
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{{Systems science}} |
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[[Category:Complex systems theory| ]] |
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[[Category:Cybernetics]] |
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[[Category:Systems]] |
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[[Category:Systems science]] |
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[[Category:Mathematical modeling]] |
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[[bg:Комплексни системи]] |
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[[ca:Sistemes complexos]] |
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[[de:Komplexes System]] |
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[[es:Sistema complejo]] |
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[[fr:Système complexe]] |
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[[it:Sistema complesso]] |
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[[pt:Sistemas Complexos]] |
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[[ru:Сложная система]] |
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