Complex system

A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like cities), an ecosystem, a living cell, and ultimately the entire universe.

"Complex systems" redirects here. For the journal, see Complex Systems (journal).
Complex systems
Topics
Self-organization
Emergence
Collective behavior
Social dynamics

Collective intelligence
Collective action
Self-organized criticality
Herd mentality
Phase transition
Agent-based modelling
Synchronization
Ant colony optimization
Particle swarm optimization
Swarm behaviour

Collective consciousness
Networks
Scale-free networks

Social network analysis
Small-world networks
Centrality
Motifs
Graph theory
Scaling
Robustness
Systems biology
Dynamic networks

Adaptive networks
Evolution and adaptation
Artificial neural network

Evolutionary computation
Genetic algorithms
Genetic programming
Artificial life
Machine learning
Evolutionary developmental biology
Artificial intelligence
Evolutionary robotics

Evolvability
Pattern formation
Fractals

Reaction–diffusion systems
Partial differential equations
Dissipative structures
Percolation
Cellular automata
Spatial ecology
Self-replication

Geomorphology
Systems theory and cybernetics
Autopoiesis

Information theory
Entropy
Feedback
Goal-oriented
Homeostasis
Operationalization
Second-order cybernetics
Self-reference
System dynamics
Systems science
Systems thinking
Sensemaking
Variety

Theory of computation
Nonlinear dynamics
Time series analysis

Ordinary differential equations
Phase space
Attractors
Population dynamics
Chaos
Multistability
Bifurcation

Coupled map lattices
Game theory
Prisoner's dilemma

Rational choice theory
Bounded rationality

Evolutionary game theory

Complex systems are systems whose behavior is intrinsically difficult to model due to the dependencies, competitions, relationships, or other types of interactions between their parts or between a given system and its environment. Systems that are "complex" have distinct properties that arise from these relationships, such as nonlinearity, emergence, spontaneous order, adaptation, and feedback loops, among others. Because such systems appear in a wide variety of fields, the commonalities among them have become the topic of their independent area of research. In many cases, it is useful to represent such a system as a network where the nodes represent the components and links to their interactions.

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.[1] 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.

As an interdisciplinary domain, complex systems draws contributions from many different fields, such as the study of self-organization and critical phenomena from physics, that of spontaneous order from the social sciences, chaos from mathematics, 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 dynamics, anthropology, computer science, meteorology, sociology, economics, psychology, and biology.

Key concepts
Gosper's Glider Gun creating "gliders" in the cellular automaton Conway's Game of Life[2]

Adaptation

Complex adaptive systems 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 brain and the immune system, the cell and the developing embryo, the cities, manufacturing businesses and any human social group-based endeavor in a cultural and social system such as political parties or communities.[3]

Features

Complex systems may have the following features:[4]

Complex systems may be open
Complex systems are usually open systems — that is, they exist in a thermodynamic gradient and dissipate energy. In other words, complex systems are frequently far from energetic equilibrium: but despite this flux, there may be pattern stability,[5] see synergetics.
Complex systems may exhibit critical transitions
Graphical representation of alternative stable states and the direction of critical slowing down prior to a critical transition (taken from Lever et al. 2020).[6] Top panels (a) indicate stability landscapes at different conditions. Middle panels (b) indicate the rates of change akin to the slope of the stability landscapes, and bottom panels (c) indicate a recovery from a perturbation towards the system's future state (c.I) and in another direction (c.II).
Critical transitions are abrupt shifts in the state of ecosystems, the climate, financial systems or other complex systems that may occur when changing conditions pass a critical or bifurcation point.[7][8][9][10] The 'direction of critical slowing down' in a system's state space may be indicative of a system's future state after such transitions when delayed negative feedbacks leading to oscillatory or other complex dynamics are weak.[6]
Complex systems may be nested
The components of a complex system may themselves be complex systems. For example, an economy is made up of organisations, which are made up of people, which are made up of cells – all of which are complex systems. The arrangement of interactions within complex bipartite networks may be nested as well. More specifically, bipartite ecological and organisational networks of mutually beneficial interactions were found to have a nested structure.[11][12] This structure promotes indirect facilitation and a system's capacity to persist under increasingly harsh circumstances as well as the potential for large-scale systemic regime shifts.[13][14]
Dynamic network of multiplicity
As well as coupling rules, the dynamic network of a complex system is important. Small-world or scale-free networks[15][16] 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 cortex for example, we see dense local connectivity and a few very long axon projections between regions inside the cortex and to other brain regions.
May produce emergent phenomena
Complex systems may exhibit behaviors that are 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, empirical food webs display regular, scale-invariant features across aquatic and terrestrial ecosystems when studied at the level of clustered 'trophic' species.[17][18] Another example is offered by the termites in a mound which have physiology, biochemistry and biological development at one level of analysis, whereas their social behavior and mound building is a property that emerges from the collection of termites and needs to be analyzed at a different level.
Relationships are non-linear
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, the effect is always directly proportional to cause. See nonlinearity.
Relationships contain feedback loops
Both negative (damping) and positive (amplifying) feedback are always found in complex systems. The effects of an element's behavior are fed back in such a way that the element itself is altered.

History

While the explicit study of complex systems dates at least to the 1970s,[19] the first research institute focused on complex systems, the Santa Fe Institute, was founded in 1984.[20][21] Early Santa Fe Institute participants included physics Nobel laureates Murray Gell-Mann and Philip Anderson, economics Nobel laureate Kenneth Arrow, and Manhattan Project scientists George Cowan and Herb Anderson.[22] Today, there are over 50 institutes and research centers focusing on complex systems.

Since the late 1990s, the interest of mathematical physicists in researching economic phenomena has been on the rise. The proliferation of cross-disciplinary research with the application of solutions originated from the physics epistemology has entailed a gradual paradigm shift in the theoretical articulations and methodological approaches in economics, primarily in financial economics. The development has resulted in the emergence of a new branch of discipline, namely "econophysics," which is broadly defined as a cross-discipline that applies statistical physics methodologies which are mostly based on the complex systems theory and the chaos theory for economics analysis.[23]

The 2021 Nobel Prize in Physics was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their work to understand complex systems. Their work was used to create more accurate computer models of the effect of global warming on the Earth's climate.[24]

Applications

Complexity in practice

The traditional approach to dealing with complexity is to reduce or constrain it. Typically, this involves compartmentalization: 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.

Complexity economics

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.

Recurrence quantification analysis has been employed to detect the characteristic of business cycles and economic development. To this end, Orlando et al.[25] developed the so-called recurrence quantification correlation index (RQCI) to test correlations of RQA on a sample signal and then investigated the application to business time series. The said index has been proven to detect hidden changes in time series. Further, Orlando et al.,[26] over an extensive dataset, shown that recurrence quantification analysis may help in anticipating transitions from laminar (i.e. regular) to turbulent (i.e. chaotic) phases such as USA GDP in 1949, 1953, etc. Last but not least, it has been demonstrated that recurrence quantification analysis can detect differences between macroeconomic variables and highlight hidden features of economic dynamics.

Complexity and education

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".[27]

Complexity and biology

Complexity science has been applied to living organisms, and in particular to biological systems. One of the areas of research is the emergence and evolution of intelligent systems. Analyses of the parameters of intellectual systems, patterns of their emergence and evolution, distinctive features, and the constants and limits of their structures and functions made it possible to measure and compare the capacity of communications (~100 to 300 million m/s), to quantify the number of components in intellectual systems (~1011 neurons), and to calculate the number of successful links responsible for cooperation (~1014 synapses)[28] Within the emerging field of fractal physiology, bodily signals, such as heart rate or brain activity, are characterized using entropy or fractal indices. The goal is often to assess the state and the health of the underlying system, and diagnose potential disorders and illnesses.

Complexity and chaos theory

Complex systems 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.[29] 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 the relevant equations describing the chaotic system's behavior, one can theoretically make perfectly accurate predictions of the system, though in practice this is impossible to do with arbitrary accuracy. Ilya Prigogine argued[30] that complexity is non-deterministic and gives no way whatsoever to precisely predict the future.[31]

The emergence of complex systems theory shows a domain between deterministic order and randomness which is complex.[32] This is referred to as the "edge of chaos".[33]

A plot of the Lorenz attractor.

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,[34] 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.[32] For recent examples in economics and business see Stoop et al.[35] who discussed Android's market position, Orlando [36] who explained the corporate dynamics in terms of mutual synchronization and chaos regularization of bursts in a group of chaotically bursting cells and Orlando et al.[37] who modelled financial data (Financial Stress Index, swap and equity, emerging and developed, corporate and government, short and long maturity) with a low-dimensional deterministic model.

Therefore, the main difference between chaotic systems and complex systems is their history.[38] Chaotic systems do not rely on their history as complex ones do. Chaotic behavior pushes a system in equilibrium into chaotic order, which means, in other words, out of what we traditionally define as 'order'. 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".[39] 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 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.

Complexity and network science

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.[40][41] For example, the Internet can be represented as a network composed of nodes (computers) and links (direct connections between computers). Other examples of complex networks include social networks, financial institution interdependencies,[42] airline networks,[43] and biological networks.

Notable scholars

See also

References
  1. Bar-Yam, Yaneer (2002). "General Features of Complex Systems" (PDF). Encyclopedia of Life Support Systems. Archived (PDF) from the original on 2022-10-09. Retrieved 16 September 2014.
  2. Daniel Dennett (1995), Darwin's Dangerous Idea, Penguin Books, London, ISBN 978-0-14-016734-4, ISBN 0-14-016734-X
  3. Skrimizea, Eirini; Haniotou, Helene; Parra, Constanza (2019). "On the 'complexity turn' in planning: An adaptive rationale to navigate spaces and times of uncertainty". Planning Theory. 18: 122–142. doi:10.1177/1473095218780515. S2CID 149578797.
  4. Alan Randall (2011). Risk and Precaution. Cambridge University Press. ISBN 9781139494793.
  5. Pokrovskii, Vladimir (2021). Thermodynamics of Complex Systems: Principles and applications. IOP Publishing, Bristol, UK. Bibcode:2020tcsp.book.....P.
  6. Lever, J. Jelle; Leemput, Ingrid A.; Weinans, Els; Quax, Rick; Dakos, Vasilis; Nes, Egbert H.; Bascompte, Jordi; Scheffer, Marten (2020). "Foreseeing the future of mutualistic communities beyond collapse". Ecology Letters. 23 (1): 2–15. doi:10.1111/ele.13401. PMC 6916369. PMID 31707763.
  7. Scheffer, Marten; Carpenter, Steve; Foley, Jonathan A.; Folke, Carl; Walker, Brian (October 2001). "Catastrophic shifts in ecosystems". Nature. 413 (6856): 591–596. Bibcode:2001Natur.413..591S. doi:10.1038/35098000. ISSN 1476-4687. PMID 11595939. S2CID 8001853.
  8. Scheffer, Marten (26 July 2009). Critical transitions in nature and society. Princeton University Press. ISBN 978-0691122045.
  9. Scheffer, Marten; Bascompte, Jordi; Brock, William A.; Brovkin, Victor; Carpenter, Stephen R.; Dakos, Vasilis; Held, Hermann; van Nes, Egbert H.; Rietkerk, Max; Sugihara, George (September 2009). "Early-warning signals for critical transitions". Nature. 461 (7260): 53–59. Bibcode:2009Natur.461...53S. doi:10.1038/nature08227. ISSN 1476-4687. PMID 19727193. S2CID 4001553.
  10. Scheffer, Marten; Carpenter, Stephen R.; Lenton, Timothy M.; Bascompte, Jordi; Brock, William; Dakos, Vasilis; Koppel, Johan van de; Leemput, Ingrid A. van de; Levin, Simon A.; Nes, Egbert H. van; Pascual, Mercedes; Vandermeer, John (19 October 2012). "Anticipating Critical Transitions". Science. 338 (6105): 344–348. Bibcode:2012Sci...338..344S. doi:10.1126/science.1225244. hdl:11370/92048055-b183-4f26-9aea-e98caa7473ce. ISSN 0036-8075. PMID 23087241. S2CID 4005516. Archived from the original on 24 June 2020. Retrieved 10 June 2020.
  11. Bascompte, J.; Jordano, P.; Melian, C. J.; Olesen, J. M. (24 July 2003). "The nested assembly of plant-animal mutualistic networks". Proceedings of the National Academy of Sciences. 100 (16): 9383–9387. Bibcode:2003PNAS..100.9383B. doi:10.1073/pnas.1633576100. PMC 170927. PMID 12881488.
  12. Saavedra, Serguei; Reed-Tsochas, Felix; Uzzi, Brian (January 2009). "A simple model of bipartite cooperation for ecological and organizational networks". Nature. 457 (7228): 463–466. Bibcode:2009Natur.457..463S. doi:10.1038/nature07532. ISSN 1476-4687. PMID 19052545. S2CID 769167.
  13. Bastolla, Ugo; Fortuna, Miguel A.; Pascual-García, Alberto; Ferrera, Antonio; Luque, Bartolo; Bascompte, Jordi (April 2009). "The architecture of mutualistic networks minimizes competition and increases biodiversity". Nature. 458 (7241): 1018–1020. Bibcode:2009Natur.458.1018B. doi:10.1038/nature07950. ISSN 1476-4687. PMID 19396144. S2CID 4395634.
  14. Lever, J. Jelle; Nes, Egbert H. van; Scheffer, Marten; Bascompte, Jordi (2014). "The sudden collapse of pollinator communities". Ecology Letters. 17 (3): 350–359. doi:10.1111/ele.12236. hdl:10261/91808. ISSN 1461-0248. PMID 24386999.
  15. A. L. Barab´asi, R. Albert (2002). "Statistical mechanics of complex networks". Reviews of Modern Physics. 74 (1): 47–94. arXiv:cond-mat/0106096. Bibcode:2002RvMP...74...47A. CiteSeerX 10.1.1.242.4753. doi:10.1103/RevModPhys.74.47. S2CID 60545.
  16. M. Newman (2010). Networks: An Introduction. Oxford University Press. ISBN 978-0-19-920665-0.
  17. Cohen, J.E.; Briand, F.; Newman, C.M. (1990). Community Food Webs: Data and Theory. Berlin, Heidelberg, New York: Springer. p. 308. doi:10.1007/978-3-642-83784-5. ISBN 9783642837869.
  18. Briand, F.; Cohen, J.E. (1984). "Community food webs have scale-invariant structure". Nature. 307 (5948): 264–267. Bibcode:1984Natur.307..264B. doi:10.1038/307264a0. S2CID 4319708.
  19. Vemuri, V. (1978). Modeling of Complex Systems: An Introduction. New York: Academic Press. ISBN 978-0127165509.
  20. Ledford, H (2015). "How to solve the world's biggest problems". Nature. 525 (7569): 308–311. Bibcode:2015Natur.525..308L. doi:10.1038/525308a. PMID 26381968.
  21. "History | Santa Fe Institute". www.santafe.edu. Archived from the original on 2019-04-03. Retrieved 2018-05-17.
  22. Waldrop, M. M. (1993). Complexity: The emerging science at the edge of order and chaos. Simon and Schuster.
  23. Ho, Y. J.; Ruiz Estrada, M. A; Yap, S. F. (2016). "The evolution of complex systems theory and the advancement of econophysics methods in the study of stock market crashes". Labuan Bulletin of International Business & Finance. 14: 68–83.
  24. "Nobel in physics: Climate science breakthroughs earn prize". BBC News. 5 October 2021.
  25. Orlando, Giuseppe; Zimatore, Giovanna (18 December 2017). "RQA correlations on real business cycles time series". Indian Academy of Sciences – Conference Series. 1 (1): 35–41. doi:10.29195/iascs.01.01.0009.
  26. Orlando, Giuseppe; Zimatore, Giovanna (1 May 2018). "Recurrence quantification analysis of business cycles". Chaos, Solitons & Fractals. 110: 82–94. Bibcode:2018CSF...110...82O. doi:10.1016/j.chaos.2018.02.032. ISSN 0960-0779. S2CID 85526993.
  27. Forsman, Jonas; Moll, Rachel; Linder, Cedric (2014). "Extending the theoretical framing for physics education research: An illustrative application of complexity science". Physical Review Special Topics - Physics Education Research. 10 (2): 020122. Bibcode:2014PRPER..10b0122F. doi:10.1103/PhysRevSTPER.10.020122. hdl:10613/2583.
  28. Eryomin, A. L. (2022). Biophysics of Evolution of Intellectual Systems. Biophysics, 67(2), 320-326.
  29. Hayles, N. K. (1991). Chaos Bound: Orderly Disorder in Contemporary Literature and Science. Cornell University Press, Ithaca, NY.
  30. Prigogine, I. (1997). The End of Certainty, The Free Press, New York.
  31. See also D. Carfì (2008). "Superpositions in Prigogine approach to irreversibility". AAPP: Physical, Mathematical, and Natural Sciences. 86 (1): 1–13..
  32. Cilliers, P. (1998). Complexity and Postmodernism: Understanding Complex Systems, Routledge, London.
  33. Per Bak (1996). How Nature Works: The Science of Self-Organized Criticality, Copernicus, New York, U.S.
  34. Colander, D. (2000). The Complexity Vision and the Teaching of Economics, E. Elgar, Northampton, Massachusetts.
  35. Stoop, Ruedi; Orlando, Giuseppe; Bufalo, Michele; Della Rossa, Fabio (2022-11-18). "Exploiting deterministic features in apparently stochastic data". Scientific Reports. 12 (1): 19843. Bibcode:2022NatSR..1219843S. doi:10.1038/s41598-022-23212-x. ISSN 2045-2322. PMC 9674651. PMID 36400910.
  36. Orlando, Giuseppe (2022-06-01). "Simulating heterogeneous corporate dynamics via the Rulkov map". Structural Change and Economic Dynamics. 61: 32–42. doi:10.1016/j.strueco.2022.02.003. ISSN 0954-349X.
  37. Orlando, Giuseppe; Bufalo, Michele; Stoop, Ruedi (2022-02-01). "Financial markets' deterministic aspects modeled by a low-dimensional equation". Scientific Reports. 12 (1): 1693. Bibcode:2022NatSR..12.1693O. doi:10.1038/s41598-022-05765-z. ISSN 2045-2322. PMC 8807815. PMID 35105929.
  38. Buchanan, M. (2000). Ubiquity : Why catastrophes happen, three river press, New-York.
  39. Gell-Mann, M. (1995). What is Complexity? Complexity 1/1, 16-19
  40. Dorogovtsev, S.N.; Mendes, J.F.F. (2003). Evolution of Networks. p. 1079. arXiv:cond-mat/0106144. doi:10.1093/acprof:oso/9780198515906.001.0001. ISBN 9780198515906. {{cite book}}: |work= ignored (help)
  41. Newman, Mark (2010). Networks. doi:10.1093/acprof:oso/9780199206650.001.0001. ISBN 9780199206650.
  42. Battiston, Stefano; Caldarelli, Guido; May, Robert M.; Roukny, tarik; Stiglitz, Joseph E. (2016-09-06). "The price of complexity in financial networks". Proceedings of the National Academy of Sciences. 113 (36): 10031–10036. Bibcode:2016PNAS..11310031B. doi:10.1073/pnas.1521573113. PMC 5018742. PMID 27555583.
  43. Barrat, A.; Barthelemy, M.; Pastor-Satorras, R.; Vespignani, A. (2004). "The architecture of complex weighted networks". Proceedings of the National Academy of Sciences. 101 (11): 3747–3752. arXiv:cond-mat/0311416. Bibcode:2004PNAS..101.3747B. doi:10.1073/pnas.0400087101. ISSN 0027-8424. PMC 374315. PMID 15007165.

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