This article may be confusing or unclear to readers. Please help clarify the article; suggestions may be found on the talk page. (April 2011)

A visual, organizational map of complex systems science

Complex systems present problems in mathematical modelling. Complex systems is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment.

The equations from which complex system models are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of systems of nature that are considered fundamentally complex. The physical manifestations of such systems cannot be defined, so the usual choice is to refer to "the system" as the mathematical information model, without referring to the undefined physical subject the model represents. One of a variety of journals using this approach to complexity is Complex Systems.

Such systems are used to model processes in computer science, biology,^[1] economics, physics, chemistry,^[2] and many other fields. It is also called complex systems theory, complexity science, study of complex systems, sciences of complexity, non-equilibrium physics, and historical physics. A variety of abstract theoretical complex systems is studied as a field of mathematics.

The key problems of complex systems are difficulties with their formal modelling and simulation. From such a perspective, in different research contexts complex systems are defined on the basis of their different attributes. Since all complex systems have many interconnected components, the science of networks and network theory are important aspects of the study of complex systems. A consensus regarding a single universal definition of complex system does not yet exist.

For systems that are less usefully represented with equations various other kinds of narratives and methods for identifying, exploring, designing and interacting with complex systems are used. A broader view of disciplines and methodologies using the complex systems approach is found on the Encyclopedia of Earth.^[3]

Overview[link]

A Braitenberg simulation, programmed in breve, an artificial life simulator

The study of mathematical complex system models is used for many scientific questions poorly suited to the traditional mechanistic conception provided by science.^[4] Complex systems is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines including anthropology, artificial intelligence, artificial life, chemistry, computer science, economics, evolutionary computation, earthquake prediction, meteorology, molecular biology, neuroscience, physics, psychology and sociology.

In these endeavors, scientists often seek simple non-linear coupling rules which lead to complex phenomena (rather than describe; see above), but this need not be the case. Human societies (and probably human brains) are complex systems in which neither the components nor the couplings are simple. Nevertheless, they exhibit many of the hallmarks of complex systems. It is worth remarking that non-linearity is not a necessary feature of complex systems modeling: macro-analyses that concern unstable equilibrium and evolution processes of certain biological/social/economic systems can usefully be carried out also by sets of linear equations, which do nevertheless entail reciprocal dependence between variable parameters.

Traditionally, engineering has striven to solve the non-linear system problem while bearing in mind that for small perturbations, most non-linear systems can be approximated with linear systems significantly simplifying the analysis. Linear systems represent the main class of systems for which general techniques for stability control and analysis exist. However, many physical systems (for example lasers) are inherently "complex systems" in terms of the definition above, and engineering practice must now include elements of complex systems research.

Information theory applies well to the complex adaptive systems, CAS, through the concepts of object oriented design, as well as through formalized concepts of organization and disorder that can be associated with any systems evolution process.

History[link]

Complex systems is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment.

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 says that order in market systems is spontaneous (or emergent) in that it is the result of human action, but not the execution of any human design.^[5][6]

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 computational complexity.

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,^[7] not constraining his work to human economies but venturing into other fields such as psychology,^[8] 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.

Topics in the complex systems study[link]

Challenges of managing complexity[link]

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 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.^[9]

Complexity Economics[link]

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 Harvard economist Ricardo Hausmann and the MIT physicist Cesar Hidalgo. Based on the ECI, Hausmann, Hidalgo and their team of the The Observatory of Economic Complexity have produced GDP forecasts for the year 2020.

Complexity and modeling[link]

A way of modelling a Complex Adaptive System

One of 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 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.^[10] 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.^[11]

Complexity and chaos theory[link]

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.^[12] The point is that chaos remains deterministic. With perfect knowledge of the initial conditions and of the context of an action, the course of this action can be predicted in chaos theory. As argued by Ilya Prigogine,^[13] complexity is non-deterministic, and gives no way whatsoever to precisely predict the future (see also^[14]).

The emergence of complexity theory shows a domain between deterministic order and randomness which is complex.^[15] This is referred as the 'edge of chaos'.^[16]

A plot of the Lorenz attractor

When one analyzes complex systems, sensitivity to initial conditions, for example, is not an issue as important as within the chaos theory in which it prevails. As stated by Colander,^[17] 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.^[15]

Therefore, the main difference between Chaotic systems and complex systems is their history.^[18] 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'.^{[clarification needed]} 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. 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.

Research centers, conferences, and journals[link]

Institutes and research centers

• The Bristol Centre for Complexity Sciences (BCCS) at University of Bristol
• International Research Center for Mathematics & Mechanics of Complex Systems (M&MoCS) at University of L'Aquila, Italy
• Center for Nonlinear Phenomena and Complex Systems at Université libre de Bruxelles, Belgium
• New England Complex Systems Institute
• Santa Fe Institute
• Northwestern Institute on Complex Systems (NICO)
• Center for the Study of Complex Systems and Cognition at École Normale Supérieure, France
• Master's Programme in Complex Adaptive Systems at University of Gothenburg, Sweden
• Max Planck Institute for the Physics of Complex Systems, Germany
• Vermont Complex Systems Center
• Centro de Ciencias de la Complejidad, at UNAM, Mexico
• Complexity Complex at the University of Warwick, United Kingdom
• Center for Interdisciplinary Research on Complex Systems at Northeastern University
• Center for Complex Networks Research at Northeastern University
• Center for Social Dynamics & Complexity (CSDC) at Arizona State University
• Center for Complex Networks and Systems Research at Indiana University
• Southampton Institute for Complex Systems Simulation
• Center for the Study of Complex Systems at the University of Michigan
• Center for Complexity in Health at Kent State University
• Center for Complex Systems and Brain Sciences at Florida Atlantic University
• ARC Centre for Complex Systems, Australia
• Harvard-MIT Observatory of Economic Complexity
• Center for Social Complexity at George Mason University
• York Centre for Complex Systems Analysis at University of York
• The Complexity Science Group at University of Calgary
• Plexus Institute for the study of Complex Change and Innovation
• The Center for Complex Systems Research, Univ. of Illinois at Urbana-Champaign
• The Institute of Global Dynamic Systems, Canberra, Australia
• CEiBA Complex Systems Research Center, Bogotá, Colombia

Journals

• Advances in Complex Systems
• Complexity
• Complex Systems
• Interdisciplinary Description of Complex Systems

Other resources

• Complexity Digest

See also[link]

Cybernetics Cognitive Science Complex adaptive system Complexity Complexity economics Decision engineering Dynamical system Dynamical systems theory Emergence Enterprise systems engineering Generative sciences

Systems science portal Mixed reality Multi-agent system Nonlinearity Pattern oriented modeling Process architecture Systems theory Systems theory in anthropology Self organization The Invisible Hand Sociology and complexity science Volatility, uncertainty, complexity and ambiguity

References[link]

Further reading[link]

• Syed M. Mehmud (2011), A Healthcare Exchange Complexity Model
• D. Chu, R. Strand and R. Fjelland, "Theories of complexity", Complexity, 8:3, 2003
• L.A.N. Amaral and J.M. Ottino, Complex networks — augmenting the framework for the study of complex system, 2004.
• Murray Gell-Mann, Let's Call It Plectics, 1995/96.
• Nigel Goldenfeld and Leo P. Kadanoff, Simple Lessons from Complexity, 1999
• A. Gogolin, A. Nersesyan and A. Tsvelik, Theory of strongly correlated systems , Cambridge University Press, 1999.
• Kelly, K. (1995). Out of Control, Perseus Books Group.
• Graeme Donald Snooks, "A general theory of complex living systems: Exploring the demand side of dynamics", Complexity, vol. 13, no. 6, July/August 2008.
• Sorin Solomon and Eran Shir, Complexity; a science at 30, 2003.
• Preiser-Kapeller, Johannes, "Calculating Byzantium. Social Network Analysis and Complexity Sciences as tools for the exploration of medieval social dynamics". August 2010

External links[link]

• The Open Agent-Based Modeling Consortium
• Complexity Science Focus
• INDECS (Interdisciplinary Description of Complex Systems)
• Center for Complex Systems Research, Univ. of Illinois
• Introduction to complex systems - Short course by Shlomo Havlin

Look up complex systems in Wiktionary, the free dictionary.

Martin Schmidt (born 4 July 1969 in Berlin) is a German judoka.

Achievements[link]

References[link]

• Factfile on JudoInside.com

This biographical article related to German judo is a stub. You can help Wikipedia by expanding it. v t e