Complex Adaptive Systems
A complex adaptive system (CAS) is a system of interacting elements that can change and learn from its environment. Because adaptation is central to the idea of CASs, it makes sense to begin our discussion with some general considerations about adaptation.
Adaptation: Process or Result?
In order to discuss CASs further, we must first understand that the term adaptation can be used in more than one way. In its most general meaning, adaptation means adjustment or amendment to changing conditions. This general usage applies to CASs as well as other systems. CASs however also have a narrower definition of adaptation which refers to an intentional and active process rather than just some by-product of a system’s evolution.
In the case of human intelligence, adaptation can refer to a very specific set of ideas. For example- it was once thought that the brain had certain “centers” for speech and comprehension which had to be activated through some unknown process before an infant learned to speak or understand language. This is clearly not the case (see Chomsky), but the idea of a process of adaptation is still relevant. The ideas of neural plasticity and brain development illustrate that there is an active, ongoing process by which humans are adapting to their environment though brain development during childhood and adolescence.
The term adaptation usually implies that something within the system has changed either through mutation or as a response to its environment. In other words, adaptation is not just any change in a system but refers specifically to those changes which are beneficial within the context of an environment. This context dependency can be seen in many examples of evolutionary adaptation. For example- some species such as shellfish have evolved camouflage which helps them avoid predators while others like cacti have a variety of spines which serve as protection from herbivores.
The evolution of these traits is an example of adaptation in the specific sense that they represent beneficial changes within their respective environments. However, if we consider both shellfish and cacti together (as a system) then we see that this distinction blurs somewhat. In this context, both groups of organisms help to define one another. Cacti create a desert environment which is conducive to shellfish and in turn, the behavior of some shellfish creates an environment which helps cacti survive.
Complex Adaptive Systems: Boundaries
The idea that systems can be defined by their environments (and the interactions between these systems) is central to CAS theory. However, the concept of environment can be more specific in CASs than in some other contexts. For example- when biological organisms are considered as CASs, the boundaries are less clear than they would be for physical or chemical systems. A bacterial cell like E coli interacts with its environment through its metabolism and cell membrane. Its environment is defined by the chemical composition of its immediate surroundings as well as larger scale processes like photosynthesis which are integral to many other biological CASs.
The environment of a star however, would not be considered part of the CAS of E coli because these two systems have different boundaries. Once we consider E coli as part of a larger system then it becomes difficult to define the boundaries between systems. A group of developing E coli cells is clearly separate from its environment, but as they begin to function as a multi-cellular organism like Volvox, the line blurs.
Further examination reveals that Volvox is an important part of many other systems such as the CAS of algae and plants it interacts with, as well as the CAS of small fish which feed on certain parts of its cells. Each system can be considered both an environment and a part of Volvox’s (functional) body at the same time. This characteristic is common to many complex adaptive systems, including human society which has no clearly defined boundaries.
Biology and CASs: The Key to a Theory of Everything?
Complex adaptive systems may in fact be the key to understanding how biological organisms function on both individual and cellular levels. Perhaps, this is why many important theories like Darwin’s theory of evolution rely on principles which are present within CASs. For example- instead of looking at individual organisms which are more or less adapted to survive in one environment or another, Darwin’s theory revolves around the idea of competition within a larger population.
Some individuals and groups will die out while others will remain because they are better able to compete with other members of their species for resources. In modern biology, the study of CASs has come to be known as “complex systems biology” which is characterized by an interdisciplinary approach involving computer science and mathematics.
Complex Adaptive Systems: Common Characteristics
CAS theory seeks to explain phenomena like self-organization, emergence and hierarchy in terms of simpler building blocks which are common across biological systems. For example- in CASs, it has been found that a small number of simple rules can yield complex behavior (like the production of different kinds of cells and tissues). This is one reason why many researchers feel that understanding these systems could be key to finding a unified theory which explains how all biological organisms function.
The idea of “self-organization” refers to the spontaneous and often unpredictable formation of complex structures from a relatively simple set of rules. In CAS theory, this tends to emerge out of competition between entities or individuals (at least in biological systems). This is similar to how cells differentiate into different tissues even though all are ultimately derived from a single fertilized cell.
Another important principle in CAS theory is that of “emergence”. In complex systems, the properties which emerge from a system are often very different from those of individual units. For example- units may interact with each other to form large scale structures (which would be impossible at a smaller scale).
An interesting example of this behavior can be found in slime molds which generally act as individual amoeba searching for food. However, when starved they will link up with other slime molds to form a larger organism which can move much faster and seek out more sources of nourishment.
Self-organization, emergence and hierarchies are three concepts which find a common home in the study of complex adaptive systems. These ideas are also present in many other scientific fields such as physics which has seen the development of theories like “self-organized criticality”.
In biology, CAS theory has allowed researchers to develop new models which explain important biological phenomena like evolution and speciation. It is now commonly accepted that the principles of self-organization, emergence and hierarchies play a large part in the way that biological systems function at both the cellular level and across different levels of biological organization.
An important area of focus for CAS research has been to explain how these systems relate to other fields such as physics or economics. Probably one of the best examples is Sozou’s “Game of Life” which can be used to illustrate how simple rules can lead to complex behavior.
The Game of Life is a computer simulation game which has been popular among many scientists and mathematicians since its creation several decades ago. The game starts when an NxN grid undergoes the following transition:
Step 1: Cells with two or three live neighbors remain alive.
Step 2: Cells with four or more live neighbors die from overpopulation.
Step 3: Cells with one or no neighbors die from isolation.
A simple system like this can lead to a surprisingly large amount of complex behavior including the formation of “gliders”- structures which can be used to build more complex structures.
There is also a surprising amount of complexity which can emerge when the rules are changed slightly. For example, consider the following variation:
Step 1: Cells with three live neighbors remain alive.
Step 2: Cells with more than three live neighbors die from overpopulation.
In this version, the cells have evolved to adopt an entirely new behavior- instead of staying alive as they would in the original, cells with three live neighbors now die.
There are many other possible variations on the Game of Life which can be used to study different phenomena such as cellular automata. It is also important to note that this game has been studied extensively and some scientists have gone as far to say that the Game of Life is a good model for studying many other phenomena including evolution.
There has been some uncertainty about how biological systems can be described using models derived from physics. One of the most fundamental questions in evolutionary biology is whether or not there exist “laws” which govern evolution. It turns out that the distribution of fitness effects in biology is well described by a power-law distribution which also appears in many other natural phenomena.
One study suggested that this distribution which emerges from evolution can be explained using the statistical mechanics approach which has been developed for studying phase transitions and critical phenomena in physics such as avalanches. In this model, the fitness difference between individuals is replaced by a free-energy difference which reflects the stability of an evolutionary system. This approach has also been applied to study phase transitions in populations.
This idea that complex adaptive systems can be described using models derived for physics and other sciences can also be extended to more abstract concepts such as consciousness. The connection between CAS theory and different fields has led to the development of a new sub-field known as complexity science.
Complexity science can be seen as an approach which seeks to understand how these types of systems function by using analytical and simulation tools from mathematics, physics, computer science, economics and biology. In this sense it is very similar to multidisciplinary fields such as biophysics and computational neuroscience.
One of the main goals of complexity science is to relate different areas back to the fundamental principles which govern complex adaptive systems. There are many examples of this such as modeling neuronal networks using statistical mechanics, understanding how behavior can emerge from neural activity (i.e. neurodynamics), investigating cultural evolution using game theory, and developing tools to model epidemics using mathematical models.
One of the main goals in studying complex systems is to understand how they can be applied to solve real-world problems. One example of this question relates to economic activity. The economy has a number of properties which make it very similar to complex adaptive systems such as evolutionary processes, power-law distributions of events, and the emergence of structure. In fact, it has been suggested that economies can be described as CAS and recent research has shown that there are many similarities between mathematical models of evolutionary dynamics and economic activity.
It is important to note however that while this similarity exists, the economy is not governed by a system as deterministic as the rules of the game of life. So while complex adaptive systems can be used to study phenomena like economic activity, it seems unlikely that they can be used to predict future events with much more accuracy than traditional methods.
This is because while many principles in physics and other sciences are deterministic, complex adaptive systems tend to have much more probabilistic dynamics.
This is a reflection of the fact that these systems can be influenced by outside factors and are not completely governed by their own internal dynamics. This makes them inherently difficult to study and even harder to predict future changes.
It should also be noted that complex adaptive systems are usually very different from one another and so it is unlikely that one can be used to study all systems.
While the usefulness of complex adaptive system models are still disputed, it has been shown that some mathematical techniques from this field can help in understanding other fields which have not previously been linked back to CAS theory such as epigenetics and morphogenesis. This shows how complex systems concepts can lead to insights in other fields and provides motivation for further research.
One of the main criticisms of this field is that it has sometimes been seen as an example of reductionism which tries to explain all natural phenomena using basic principles from physics and other sciences. This criticism however, seems to be unfounded because complex systems are not reducible. This means that while they can be described using similar rules, they cannot be reduced to these rules. This is because a complex system is made up of different parts which interact in some way and the whole is greater than the sum of its parts. The emergent properties which arise as a result are not predicted by the principles that govern each individual component; there can even be opposing principles governing different parts of the system.
Another argument made against the usefulness of complex adaptive systems models is that they are too abstract to be applicable to real-world problems. In reality however, many of the applications of CAS theories come from describing real-world phenomena using these models and understanding how they can relate back to fundamental concepts in physics. For example, models based on neuronal dynamics have been used to understand how memory works and what the best way is for people to learn new information. Other applications include using evolutionary principles, a fundamental concept in physics, to develop better algorithms for information retrieval.
In summary, while complex adaptive systems do not seem to be an effective tool for prediction and are better at describing phenomena, they can be used to explain how different naturally occurring systems operate. This is because many of the properties of complex adaptive systems seem to arise as a result of basic principles found in physics. Furthermore, there seems to be no reason why these concepts cannot apply to all CASs which means that this research field has great potential.
Every organism is a complex adaptive system. For example, cells are made up of many different specialized subcellular organelles which each play an important role in the cell’s behavior. Therefore, even though cells are built on a similar set of principles they can be seen as complex adaptive systems because their internal organization and function varies from one cell type to another. This is also true when looking at multi-cellular organisms because the behavior of each cell is influenced by its environment and all cells must cooperate in order for the organism to function as a whole.
It has been suggested that even human civilizations can be described as complex adaptive systems and it has been hypothesized that this is why they follow power laws. This means that the frequency and intensity of social activities vary in a similar way to other natural phenomena such as earthquakes, forest fires, epidemics, etc. This is because human behavior is strongly influenced by external factors which are not easy to predict but nevertheless have dramatic effects on the system as a whole. An example of this can be seen in the study of history which shows that some events have a larger impact on the future than others. For example, large empires such as the Roman Empire and its influence throughout Europe, or isolated societies such as Easter Island & Greenland, tend to have more influence on today’s world than small groups which were nevertheless very important in their day like the Sumerians or the Harappa civilization. This is because even though they have a small impact when they are flourishing, once they disappear their influence on modern systems diminishes since their information is not passed on to future societies.
Complex adaptive systems can also be seen in certain economic models, such as speculative markets and some financial crises which are better described by agent based models. These complex systems are characterized not only by their high level of interconnectedness between the various components, but also by the relative ease with which new structures can emerge and existing ones disappear due to external changes in the environment. For example, it has been suggested that the world economy is a complex adaptive system which responds to crises by creating new markets or institutions which then support the economy in its recovery.
However, it is important to note that CASs are not always entirely unpredictable and models such as cellular automata have been very successful at making long-term predictions about phenomena like global warming, sea level rise, continental drift, etc. This has some interesting implications because it means that certain complex adaptive systems operate in a predictable manner and can be modeled using similar principles.
Finally, it is important to stress that CASs theory has not been widely accepted by biologists and there are those who think that this whole approach is overly complicated and does not lead to better understanding of how organisms behave. For example, biologist George C. Williams, proposed the notion of “adaptation”, a concept that he saw as fundamental in biology and was only explained by genetic changes without reference to external factors. However, this view has been challenged as it does not consider how organisms are adapted to their environment and is therefore incomplete.
However, there have also been some attempts at applying CAS to other disciplines such as computer science and economics. For example, games like chess were seen as a way to test the limits of computerized technology since they require extensive analysis and the ability to search through a very large number of possibilities. This is also true in human societies where many activities are repetitive or follow rules, for examples sports or traditional dances, which allow people to play or perform them automatically after some practice.
This section describes several well-known complex adaptive systems as examples of CASs:
Cellular Automata are also considered by many scientists to be a type of CAS because the components are simple and strongly connected, but it has been harder to make generalizations about their behavior as a whole. For example, it has been mathematically proven that for 2 dimensional cellular automata there is an infinite number of stable states but only a finite number of patterns which can reach these states. This might then be considered a law about this type of system and future research could focus on studying the conditions under which such laws appear in alternative models.
A network of interconnected neurons is a simple example of CAS since the behavior of each neuron depends on its immediate neighbors, but also has an effect on distant neurons through its electric signals. Even though the individual components are very simple and their function is well understood, it can be difficult to predict how they will behave when connected together in different configurations. Finally, it has also been shown that such networks can learn and adapt to their environment using the same principles as artificial neural networks which are often used in computational intelligence for pattern recognition.
In financial markets, investors interact with each other in order to share information about prices or make transactions and this gives rise to very complex behavior that in many cases cannot be predicted with models based on classical physics such as the Black-Scholes model. This means that it is hard to study market dynamics using causal reasoning and therefore most of the research in this area has focused on computational representations instead, either by constructing simplified financial networks or by studying phase transitions which are events where a CAS undergoes a sudden change in its behavior due to small changes in the way it is defined or initial conditions.
Complex adaptive systems have been recognized as a new facet of the semantic web and have started to be implemented as part of this technology such as in the Linked Data project by David Wood from Digital Enterprise Research Institute (DERI). This new approach is based on the idea that a CAS is an evolving system where all its components interact with each other and therefore it can be considered as a “living” system. This would make the web itself become alive and allow it to evolve in unpredictable ways, another application of the principles of self-organization.
There are several ongoing research programs aimed at studying and implementing complex adaptive systems. Below are some examples:
One of the main challenges in studying CAS is creating simplified mathematical models of them which can then be analyzed mathematically and whose behavior can be extrapolated without considering specific properties. This is often done by representing each agent as a node in a graph with weighted edges between nodes, where each edge represents a connection between two agents. Agents can then be described by specifying the properties of these connections, which can then be used to predict how they behave as a whole.
Although this kind of approach is often useful for studying CASs and has led to several models being proposed over the past decades, it is still not clear when such simplified representations can be validly used to describe real systems. This is especially true for the study of phase transitions, where it is hard to find a common ground between the results obtained using simplified models and those observed in nature.