Complex adaptive system

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A complex adaptive system is a system that is complex in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events. [1] [2] [3] It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure. [1] [2] [4] The Complex Adaptive Systems approach builds on replicator dynamics. [5]

Contents

The study of complex adaptive systems, a subset of nonlinear dynamical systems, [6] is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior. [7]

Overview

Complex Adaptive Systems Complex adaptive system.svg
Complex Adaptive Systems

The term complex adaptive systems, or complexity science , is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory—it encompasses more than one theoretical framework and is interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches. [8] Hard theories use formal language that is precise, tend to see agents as having tangible properties, and usually see objects in a behavioral system that can be manipulated in some way. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and a class of softer theory is Viable System Theory. Many of the propositional consideration made in hard theory are also of relevance to softer theory. From here on, interest will now center on CAS.


The study of CAS focuses on complex, emergent and macroscopic properties of the system. [4] [9] [10] John H. Holland said that CAS "are systems that have a large numbers of components, often called agents, that interact and adapt or learn." [11]

Typical examples of complex adaptive systems include: climate; cities; firms; markets; governments; industries; ecosystems; social networks; power grids; animal swarms; traffic flows; social insect (e.g. ant) colonies; [12] the brain and the immune system; and the cell and the developing embryo. Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS. [12] [13] [14] The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system. [15] [16] [17] CAS can be hierarchical, but more often exhibit aspects of "self-organization". [18]

The term complex adaptive system was coined in 1968 by sociologist Walter F. Buckley [19] [20] who proposed a model of cultural evolution which regards psychological and socio-cultural systems as analogous with biological species. [21] In the modern context, complex adaptive system is sometimes linked to memetics, [22] or proposed as a reformulation of memetics. [23] Michael D. Cohen and Robert Axelrod however argue the approach is not social Darwinism or sociobiology because, even though the concepts of variation, interaction and selection can be applied to modelling 'populations of business strategies', for example, the detailed evolutionary mechanisms are often distinctly unbiological. [24] As such, complex adaptive system is more similar to Richard Dawkins's idea of replicators. [24] [25] [26]

General properties

What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. A MAS is defined as a system composed of multiple interacting agents; whereas in CAS, the agents as well as the system are adaptive and the system is self-similar. A CAS is a complex, self-similar collectivity of interacting, adaptive agents. Complex Adaptive Systems are characterized by a high degree of adaptive capacity, giving them resilience in the face of perturbation.

Other important properties are adaptation (or homeostasis), communication, cooperation, specialization, spatial and temporal organization, and reproduction. They can be found on all levels: cells specialize, adapt and reproduce themselves just like larger organisms do. Communication and cooperation take place on all levels, from the agent to the system level. The forces driving co-operation between agents in such a system, in some cases, can be analyzed with game theory.

Characteristics

Some of the most important characteristics of complex adaptive systems are: [27]

Robert Axelrod & Michael D. Cohen identify a series of key terms from a modeling perspective: [28]

Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. [29] Each of these eight characteristics had been shown to be present in the creativity and innovative processes:

Modeling and simulation

CAS are occasionally modeled by means of agent-based models and complex network-based models. [34] Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model. [35] Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components. [36]

In 2013 SpringerOpen/BioMed Central launched an online open-access journal on the topic of complex adaptive systems modeling (CASM). Publication of the journal ceased in 2020. [37]

Evolution of complexity

Passive versus active trends in the evolution of complexity. CAS at the beginning of the processes are colored red. Changes in the number of systems are shown by the height of the bars, with each set of graphs moving up in a time series. Evolution of complexity.svg
Passive versus active trends in the evolution of complexity. CAS at the beginning of the processes are colored red. Changes in the number of systems are shown by the height of the bars, with each set of graphs moving up in a time series.

Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms. [38] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms". [39]

If this were generally true, evolution would possess an active trend towards complexity. As shown below, in this type of process the value of the most common amount of complexity would increase over time. [40] Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution. [41] [42]

However, the idea of a general trend towards complexity in evolution can also be explained through a passive process. [40] This involves an increase in variance but the most common value, the mode, does not change. Thus, the maximum level of complexity increases over time, but only as an indirect product of there being more organisms in total. This type of random process is also called a bounded random walk.

In this hypothesis, the apparent trend towards more complex organisms is an illusion resulting from concentrating on the small number of large, very complex organisms that inhabit the right-hand tail of the complexity distribution and ignoring simpler and much more common organisms. This passive model emphasizes that the overwhelming majority of species are microscopic prokaryotes, [43] which comprise about half the world's biomass [44] and constitute the vast majority of Earth's biodiversity. [45] Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias.

If there is a lack of an overall trend towards complexity in biology, this would not preclude the existence of forces driving systems towards complexity in a subset of cases. These minor trends would be balanced by other evolutionary pressures that drive systems towards less complex states.

See also

Related Research Articles

Complexity characterizes the behavior of a system or model whose components interact in multiple ways and follow local rules, leading to non-linearity, randomness, collective dynamics, hierarchy, and emergence.

Strategy is a general plan to achieve one or more long-term or overall goals under conditions of uncertainty. In the sense of the "art of the general", which included several subsets of skills including military tactics, siegecraft, logistics etc., the term came into use in the 6th century C.E. in Eastern Roman terminology, and was translated into Western vernacular languages only in the 18th century. From then until the 20th century, the word "strategy" came to denote "a comprehensive way to try to pursue political ends, including the threat or actual use of force, in a dialectic of wills" in a military conflict, in which both adversaries interact.

<span class="mw-page-title-main">Emergence</span> Unpredictable phenomenon in complex systems

In philosophy, systems theory, science, and art, emergence occurs when a complex entity has properties or behaviors that its parts do not have on their own, and emerge only when they interact in a wider whole.

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, an ecosystem, a living cell, and, ultimately, for some authors, the entire universe.

<span class="mw-page-title-main">Autopoiesis</span> Systems concept which entails automatic reproduction and maintenance

The term autopoiesis refers to a system capable of producing and maintaining itself by creating its own parts. The term was introduced in the 1972 publication Autopoiesis and Cognition: The Realization of the Living by Chilean biologists Humberto Maturana and Francisco Varela to define the self-maintaining chemistry of living cells.

<span class="mw-page-title-main">Self-organization</span> Process of creating order by local interactions

Self-organization, also called spontaneous order in the social sciences, is a process where some form of overall order arises from local interactions between parts of an initially disordered system. The process can be spontaneous when sufficient energy is available, not needing control by any external agent. It is often triggered by seemingly random fluctuations, amplified by positive feedback. The resulting organization is wholly decentralized, distributed over all the components of the system. As such, the organization is typically robust and able to survive or self-repair substantial perturbation. Chaos theory discusses self-organization in terms of islands of predictability in a sea of chaotic unpredictability.

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.

<span class="mw-page-title-main">Edge of chaos</span> Transition space between order and disorder

The edge of chaos is a transition space between order and disorder that is hypothesized to exist within a wide variety of systems. This transition zone is a region of bounded instability that engenders a constant dynamic interplay between order and disorder.

<span class="mw-page-title-main">Cooperation</span> Groups working or acting together

Cooperation takes place when a group of organisms works or acts together for a collective benefit to the group as opposed to working in competition for selfish individual benefit. In biology, many animal and plant species cooperate both with other members of their own species and with members of other species with whom they have relationships.

<span class="mw-page-title-main">Emergentism</span> Philosophical belief in emergence

Emergentism is the belief in emergence, particularly as it involves consciousness and the philosophy of mind. A property of a system is said to be emergent if it is a new outcome of some other properties of the system and their interaction, while it is itself different from them. Within the philosophy of science, emergentism is analyzed both as it contrasts with and parallels reductionism. This philosophical theory suggests that higher-level properties and phenomena arise from the interactions and organization of lower-level entities yet are not reducible to these simpler components. It emphasizes the idea that the whole is more than the sum of its parts. Historically, emergentism has significantly influenced various scientific and philosophical ideas, highlighting the complexity and interconnectedness of natural systems.

<span class="mw-page-title-main">Computational sociology</span> Branch of the discipline of sociology

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Complexity theory and organizations, also called complexity strategy or complex adaptive organizations, is the use of the study of complexity systems in the field of strategic management and organizational studies. It draws from research in the natural sciences that examines uncertainty and non-linearity. Complexity theory emphasizes interactions and the accompanying feedback loops that constantly change systems. While it proposes that systems are unpredictable, they are also constrained by order-generating rules.

Computer simulation is a prominent method in organizational studies and strategic management. While there are many uses for computer simulation, most academics in the fields of strategic management and organizational studies have used computer simulation to understand how organizations or firms operate. More recently, however, researchers have also started to apply computer simulation to understand organizational behaviour at a more micro-level, focusing on individual and interpersonal cognition and behavior such as team working.

Complexity economics is the application of complexity science to the problems of economics. It relaxes several common assumptions in economics, including general equilibrium theory. While it does not reject the existence of an equilibrium, it sees such equilibria as "a special case of nonequilibrium", and as an emergent property resulting from complex interactions between economic agents. The complexity science approach has also been applied to computational economics.

<span class="mw-page-title-main">Biological organisation</span> Hierarchy of complex structures and systems within biological sciences

Biological organisation is the organisation of complex biological structures and systems that define life using a reductionistic approach. The traditional hierarchy, as detailed below, extends from atoms to biospheres. The higher levels of this scheme are often referred to as an ecological organisation concept, or as the field, hierarchical ecology.

The evolution of biological complexity is one important outcome of the process of evolution. Evolution has produced some remarkably complex organisms – although the actual level of complexity is very hard to define or measure accurately in biology, with properties such as gene content, the number of cell types or morphology all proposed as possible metrics.

Business agility refers to rapid, continuous, and systematic evolutionary adaptation and entrepreneurial innovation directed at gaining and maintaining competitive advantage. Business agility can be sustained by maintaining and adapting the goods and services offered to meet with customer demands, adjusting to the marketplace changes in a business environment, and taking advantage of available human resources.

Living systems are life forms treated as a system. They are said to be open self-organizing and said to interact with their environment. These systems are maintained by flows of information, energy and matter. Multiple theories of living systems have been proposed. Such theories attempt to map general principles for how all living systems work.

<span class="mw-page-title-main">Artificial life</span> Field of study

Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987, Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.

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Literature

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