Complexity Theory External Resources Complexity Labs
Complexity Theory External Resources
Power Laws and Rich-Get-Richer Phenomena
We have been studying situations in which a personâ€™s behavior or decisions depend on the choices made by other people â€” either because the personâ€™s rewards are dependent on what other people do, or because the choices of other people convey information that is useful in the decision-making process
In real life situations, open systems in non-equilibrium are sometimes in steady state and sometimes in non-steady states. Steady state is obtained when forces and counter forces interact. However, when multiple forces are operative involving autocatalysis (positive feedback) and inhibitory step (negative feedback), exotic non-equilibrium phenomena are observed.
To enable a better understanding of the underlying logic of path dependence, we set forth a theoretical framework explaining how organizations become path dependent. At its core are the dynamics of self-reinforcing mechanisms, which are likely to lead an organization into a lock-in. By drawing on studies of technological paths, we conceptualize the emergent process of path dependence along three distinct stages.
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distributionâ€”the part of the distribution representing large but rare eventsâ€” and by the difficulty of identifying the range over which power-law behavior holds.
The last decade and a half has seen an ardent development of self-organised criticality (SOC), a new approach to complex systems, which has become important in many domains of natural as well as social science, such as geology, biology, astronomy, and economics, to mention just a few. This has led many to adopt a generalist stance towards SOC, which is now repeatedly claimed to be a universal theory of complex behaviour.
The concept of self-organized criticality was introduced to explain the behaviour of the sandpile model. In this model, particles are randomly dropped onto a square grid of boxes. When a box accumulates four particles they are redistributed to the four adjacent boxes or lost off the edge of the grid. Redistributions can lead to further instabilities with the possibility of more particles being lost from the grid, contributing to the size of each â€?avalancheâ€™.
Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in the literature and by those in the field.
We live in a world characterized by evolutionâ€”that is, by ongoing processes of development, formation, and growth in both natural and human-created systems. Biology tells us that complex, natural systems are not created all at once but must instead evolve over time. We are becoming increasingly aware that evolutionary processes are ubiquitous and critical for social, educational, and technological innovations as well.
The various at the Santa Fe Institute studying complex adaptive systems(CAS) have somewhat different points of view and have adopted different vocabularies. Some of us speak of â€śartificial lifeâ€ť or â€śartificial social lifeâ€ť or â€śartificial worlds,â€ť while others, of whom I am one, prefer to consider natural CAS and computer-based systems together.
A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society.
We are surrounded by complex systems, from cells made of thousands of molecules to society, a collection of billions of interacting individuals. These systems display signatures of order and self-organization. Understanding and quantifying this complexity is a grand challenge for science.
Primer on Complexity, Self-organization & Emergence
Complex Systems Science aims to understand concepts like complexity, self-organization, emergence and adaptation, among others. The inherent fuzziness in complex systems definitions is complicated by the unclear relation among these central processes: does self-organisation emerge or does it set the preconditions for emergence? Does complexity arise by adaptation or is complexity necessary for adaptation to arise?
The concepts of complexity and chaos are being invoked with increasing frequency in the health sciences literature. However, the concepts underpinning these concepts are foreign to many health scientists and there is some looseness in how they have been translated from their origins in mathematics and physics, which is leading to confusion and error in their application.
The study of complex adaptive systems, from cells to societies, is a study of the interplay among processes operating at diverse scales of space, time and organizational complexity. The key to such a study is an understanding of the interrelationships between microscopic processes and macroscopic patterns, and the evolutionary forces that shape systems.
The study of complex systems in a unified framework has become recognized in recent years as a new scientific discipline, the ultimate of interdisciplinary fields. It is strongly rooted in the advances that have been made in diverse fields ranging from physics to anthropology, from which it draws inspiration and to which it is relevant.
Systems are entities composed of wellâ€?defined components. When integrated the components act together as to form a functioning whole with dynamical behaviors and responses to the environment. Systems can be embedded into other functional entities as components. Identifying a system or a hierarchy of systems requires a certain level of abstraction and simplification.
Complexity theory is a relatively new field that began in the mid-1980s at the Santa Fe Institute in New Mexico. Work at the Santa Fe Institute is usually presented as the study of Complex Adaptive Systems (CAS). The CAS movement is predominantly American, as opposed to the European â€śnatural scienceâ€ť tradition in the area of cybernetics and systems.
This paper offers a brief description and summary of the characteristics of complex adaptive systems. The use of computer software such as StarLogo and NetLogo is presented as a powerful way to explore the dynamics of such systems.