Overview
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]
- The number of elements is sufficiently large that conventional descriptions (e.g. a system of differential equations) are not only impractical, but cease to assist in understanding the system. Moreover, the elements interact dynamically, and the interactions can be physical or involve the exchange of information.
- Such interactions are rich, i.e. any element or sub-system in the system is affected by and affects several other elements or sub-systems.
- The interactions are non-linear: small changes in inputs, physical interactions or stimuli can cause large effects or very significant changes in outputs.
- Interactions are primarily but not exclusively with immediate neighbours and the nature of the influence is modulated.
- Any interaction can feed back onto itself directly or after a number of intervening stages. Such feedback can vary in quality. This is known as recurrency.
- The overall behavior of the system of elements is not predicted by the behavior of the individual elements
- Such systems may be open and it may be difficult or impossible to define system boundaries
- Complex systems operate under far from equilibrium conditions. There has to be a constant flow of energy to maintain the organization of the system
- Agents in the system are adaptive. They update their strategies in response to input from other agents, and the system itself.[3]
- Elements in the system may be ignorant of the behaviour of the system as a whole, responding only to the information or physical stimuli available to them locally
Robert Axelrod & Michael D. Cohen identify a series of key terms from a modeling perspective:[28]
- Strategy, a conditional action pattern that indicates what to do in which circumstances
- Artifact, a material resource that has definite location and can respond to the action of agents
- Agent, a collection of properties, strategies & capabilities for interacting with artifacts & other agents
- Population, a collection of agents, or, in some situations, collections of strategies
- System, a larger collection, including one or more populations of agents and possibly also artifacts
- Type, all the agents (or strategies) in a population that have some characteristic in common
- Variety, the diversity of types within a population or system
- Interaction pattern, the recurring regularities of contact among types within a system
- Space (physical), location in geographical space & time of agents and artifacts
- Space (conceptual), "location" in a set of categories structured so that "nearby" agents will tend to interact
- Selection, processes that lead to an increase or decrease in the frequency of various types of agent or strategies
- Success criteria or performance measures, a "score" used by an agent or designer in attributing credit in the selection of relatively successful (or unsuccessful) strategies or agents
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:
- Path dependent: Systems tend to be sensitive to their initial conditions. The same force might affect systems differently.[30]
- Systems have a history: The future behavior of a system depends on its initial starting point and subsequent history.[31]
- Non-linearity: React disproportionately to environmental perturbations. Outcomes differ from those of simple systems.[30][32]
- Emergence: Each system's internal dynamics affect its ability to change in a manner that might be quite different from other systems.[30]
- Irreducible: Irreversible process transformations cannot be reduced back to its original state.[33]
- Adaptive/Adaptability: Systems that are simultaneously ordered and disordered are more adaptable and resilient.[30]
- Operates between order and chaos: Adaptive tension emerges from the energy differential between the system and its environment.[33]
- Self-organizing: Systems are composed of interdependency, interactions of its parts, and diversity in the system.[30]
