Emergence 2006
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Different people will look at different aspects of life with varying preconceptions. One person will find a certain beauty, a pattern in the genes which make up an organism, but find utter chaos in the workings of a city. Another person might see it the other way around, and yet another might see both as incomprehensible. John Holland attempts to simplify these systems into basic abstract attributes. In his book, Hidden Order: How Adaptation Builds Complexity, the father of genetic algorithms steps through both the properties which he believes are common to all complex systems and the development and principles of his modeling system, Echo.

The central phenomena which Holland studies in his book are what he terms complex adaptive systems, or cas (not to be confused with the plural of CAs). A cas is a type of complex system, which has many parts, yet retains coherence despite the introduction of new elements or challenges. In short, it is a dynamic network with the ability to change in response to stimuli. Many phenomena may be considered to be complex adaptive systems, including ant hills, cities, the immune system and the ecosystem. All of these examples have the ability to learn from past experiences. They work, without reason, on occasion. They are so complex that we cannot predict the exact outcome of any event acting on them.

Holland postulates that there exist seven basic elements that characterize a cas, four of which are properties and four of which are mechanisms. Separating the two types, I will attempt to simplify the explanations of these elements. The most basic of the properties, and in my opinion, one of the most essential, is the property of aggregation. Aggregation is as much a property of a cas as it is an ability in ourselves. We have the ability to look at a collection of objects (or elements, if you prefer) and see past the specifics and generalize. Instead of seeing 10 different types of cars and thinking that each one is truly unique and none of them could ever fit into a more general description, we look at that collection and think, yes, they are all cars. So it also is with complex adaptive systems; they can all be generalized into categories, and then all the categories are treated the same.

The next property is that of nonlinearity. Linear equations follow the form that the whole is equal to the sum if its parts. In contrast, nonlinear systems are equal to more than the sum of their parts. Instead of the summation, it takes the product of dissimilar variables, and this reveals far more about the cas than simply the sum. Inevitably, they are also more complicated to analyze than linear equations. Flows, in terms of complex adaptive systems, work much in the same way as they do in everyday life. Resources flow over the network of nodes (agents) and connectors. They are customarily denoted as such: {node, connector, resource}. A more specific example would be {cities, roads, produce} to represent the flow produce to cities. When we look at flows, an important concept to grasp is the one of the multiplier effect. The multiplier effect happens when an additional node is introduced into the system. The effect is how this affects the system and the flow. Another concept is that of the recycling effect.How does the reuse of resources affect the system as a whole? These are both things to keep in mind when looking at the flow of a cas.

Then Holland discusses the property of diversity, where each agent fulfills a function that is delineated by its interactions with the surrounding agents. With the spread of agents, it allows the modification and diversification of agents. New interactions develop, thereby creating a new kind of niche that is to be filled by a different type of agent. If an agent disappears, there is a hole in that system. While the agent that takes its place may not be the same agent, it tends to fulfill the same properties and provides equivalent tasks. Patterns in complex adaptive systems are likely to persevere despite disturbances. The example that Holland gives is water. Water has a fabric which is easily disturbed, but reverts back to its original fabric quickly. While agents might die (or go extinct) new agents come into the system to preserve the integrity of the pattern.

Tagging is what is termed the mechanism of identifying an element of an aggregate. To some extent, this could mean no more than calling the aggregate by a specified name. However, in terms of a cas, tagging often means putting some means of identification on an agent, such as actual tags on a wild animal. Tagging allows agents to "discriminate" between other agents, as well as allowing the observer to discriminate between all of the agents. Internal models are unique to each cas, and are a basic schema to the system. The internal model takes input and filters it into patterns which it is able to use to change itself. After one such occurrence, the agent should be able to anticipate the outcome of the same input if it occurs again. Tacit internal models only tell the system what to do at the current point, but overt internal models are used to explore alternatives, or to look ahead to the future. The mechanism of building blocks is the idea starting with the decomposition of a complex system into simple parts. These parts may then be reused and combined in any number of ways. This reusability leads to repetition which leads to patterns. An example with Holland gives is that of facial features. All types of facial features may be dissected into elements such as eyes, nose, ears, etc... They can then be combined, mixed up, and matched in a sort of building block-like fashion.

As is quite evident, these properties and mechanisms are essential to the idea of emergence, and how to model emergent phenomena, as complex adaptive systems came to be called. When looking at phenomena that have emergent properties, we do not ask what is unique about that phenomenon; rather, we compare that phenomenon to other emergent phenomena and ask what they have in common. In essence, we simplify it into a preexisting category; we use the property of aggregation. Different emergent phenomena display the property of diversity, but the most evident is that of the Game of Life. In a stable board, there would often be many types of agents, all working in conjunction with each other. They all fulfilled their own purpose, but their purpose depended upon the purpose of those around them. Once a board stabilized, it would continue to be stable until an external agent acted upon it. Afterwards, it would again stabilize. The mechanism of building blocks seems so instinctive that it is too obvious to even include, yet its exclusion would be disastrous. What is an emergent phenomenon without a pattern? These patterns arise out of the building blocks inherent in ecosystems, in economic systems, in the immune system and in social systems.

Holland does an excellent job of communicating both the seven basics and how they are applicable in different settings and situations. Understanding these properties and mechanisms is essential to understanding emergence because without them we would have no hope of modeling emergent phenomena. Holland found this when he was developing his program Echo to model genetic algorithms. By using them, he was able to simplify the process of creating Echo and at the same time demystify complex adaptive systems for everyone else. Not only do these properties help us model emergence, but they clarify what forms emergence might take in the world and help us recognize them.