Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data


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A complex system is a system composed of many components which may interact with each other.

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Examples of complex systems are Earth's global climate , organisms , the human brain , infrastructure such as power grid, transportation or communication systems, social and economic organizations like cities , an ecosystem , a living cell , and ultimately the entire universe.

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 own 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 the links 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.

Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data - CRC Press Book

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 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. Complex systems is chiefly concerned with the behaviors and properties of systems.

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. 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 parameterization.

Because of their broad, interdisciplinary applicability, systems concepts play a central role in complex systems. 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.

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.

Systems exhibit complexity means that their behaviors cannot be easily implied from the very properties that make them difficult to model, and the complex behaviors 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.

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 chaotic behavior behavior that exhibits extreme sensitivity to initial conditions , or if they have 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.

The interacting components of a complex system form a network , which is a collection of discrete objects and relationships between them, usually depicted as a graph of vertices connected by edges. Networks can describe the relationships between individuals within an organization, between logic gates in a circuit , between genes in gene regulatory networks , or between any other set of related entities. 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 networks , which have properties such as phase transitions and 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 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.

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.

Of particular interest to complex systems are nonlinear dynamical systems , which are systems of differential equations that have one or more nonlinear terms. Some nonlinear dynamical systems, such as the Lorenz system , can produce a mathematical phenomenon known as 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.


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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. 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.

One example of complex system whose emergent properties have been studied extensively is 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.

When emergence describes the appearance of unplanned order, it is spontaneous order in the social sciences or self-organization in physical sciences.

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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 crystals , for instance the apparent radial symmetry of snowflakes , which arises from purely local attractive and repulsive forces both between water molecules and between water molecules and their surrounding environment.

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.

Complex systems may have the following features: [4]. 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. In the area of mathematics , arguably the largest contribution to the study of complex systems was the discovery of chaos in deterministic systems, a feature of certain dynamical systems that is strongly related to nonlinearity.

The notion of self-organizing systems is tied with 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 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.

One complex system containing humans is 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 emergent in that it is the result of human action, but not the execution of any human design. 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.

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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, [15] not constraining his work to human economies but venturing into other fields such as psychology , [16] 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. While the explicit study of complex systems dates at least to the s, [17] the first research institute focused on complex systems, the Santa Fe Institute , was founded in 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. 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.

Over the last decades, within the emerging field of complexity economics new predictive tools have been developed to explain economic growth. Hidalgo and the Harvard economist Ricardo Hausmann. 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".

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 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.

Chaos is sometimes viewed as extremely complicated information, rather than as an absence of order. 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 [26] that complexity is non-deterministic, and gives no way whatsoever to precisely predict the future. The emergence of complexity theory shows a domain between deterministic order and randomness which is complex. 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, [30] 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. Therefore, the main difference between chaotic systems and complex systems is their history. Chaotic behaviour pushes a system in equilibrium into chaotic order, which means, in other words, out of what we traditionally define as 'order'. 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".

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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.

Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data

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