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Evolution

Evolution is a gradual process that takes place over the course of several life-cycles to a population of agents

Evolution is a gradual process that takes place over the course of several life-cycles of a population of agents

Evolution is a process of adaptation that operates on the macro-level of a system. Adaptation is the capacity to generate a response to some change within the environment. Evolution is this same process but operating on the macro scale, i.e. on the level of a population of agents.1 Here again, it is the capacity for the system to respond to changes within its environment. Evolution is the adaptive response of a group of entities that occurs over a period of many life cycles. Evolutionary changes reflect the response of the collection of agents to their environment.2 The concept of evolution has a strong association with its application in biology. Complexity theory, though, deals with the concept on a slightly more abstract level as it applies to all complex adaptive systems from the development of civilizations to financial markets, cultures, and technologies. As such, we are trying to understand evolution as a continuous and pervasive phenomenon that occurs in all types of natural, social and engineered systems.

Basic Framework

The basic framework of the theory of evolution, as posited by systems theory and complexity theory, consists of just a few key elements. Firstly, a system exists within an environment and that environment changes periodically. For the system to endure, it must be able to generate the appropriate response to these environmental perturbations. Secondly, generating the appropriate response means selecting from a variety of different internal states or strategies, and thus the system needs to maintain and be able to generate a certain degree of variety. Thirdly, that variety is not for free.3 It costs the system something to maintain, and thus it must select from these variants the most appropriate responses for that environment in order to minimize this cost while maximizing the payoff to the system as a whole. Lastly, these variants that have proven most appropriate for responding to changes within that particular environment will then be selected for replication in order to be more prevalent within the system during its next life cycle, thus achieving the ultimate goal of altering the entire system to make it better suited to that environment.

Response To Change

Our first statement that a system needs to be able to generate an appropriate response to any change from its environment in order for it to endure can be described by the theory of homeostasis, that all systems require a certain state to their environment in order to operate, and they need to somehow maintain that set of input values from their environment or else they will cease to properly function when it changes. If I am driving my car down the road and someone pulls out in front of me, I need to be able to identify this and steer around them. The environment has changed, and if I cannot generate the appropriate response then I am in trouble. So this is not so much a statement of how evolution works, but more a statement of why we need evolution. Put very simply, it is because environments change. If the system cannot change with it, then it will eventually cease to function within that environment; without a centralized control system, evolution is the only way to prevent this.

Requisite Variety

The random deformation and cross mixing of DNA is one way the biological systems ensure sufficient variety amongst a population

The random deformation and cross mixing of DNA is one way that biological systems ensure sufficient variety amongst a population

Secondly, that the system needs to maintain a certain level of variety from which it can select the appropriate response given any environmental change. This is the so-called law of requisite variety4 that a system needs sufficient variety of states to respond to the variety within its environment. For example, the human immune system needs to have the right type of antibodies to neutralize an invader.
The immune system does this by simply producing an astronomical variety of different antibody shapes so that it will have the appropriate response when needed.5 In general, this requisite variety may be created by randomness – as in the random deformation of DNA – or cross-mixing, such as sexual reproduction, but also may be purposefully generated, as would be the case for an R&D lab.

Selection

Thirdly, selection. The most appropriate responses to the given state of the environment are selected, because diversity typically has some cost associated with it, and thus we can only maintain a limited amount of it. I may want to be appropriately dressed for any given weather condition, but I cannot bring my whole wardrobe with me. Each item I take will have a carrying cost associated with it. Thus, I must perform selection upon the variety of clothing I have based upon an assessment of the environment’s state. Biological evolution by means of natural selection is another example. There is a limited amount of resources within any ecosystem. Selection takes place as a result of the competition among the members of a population for resources, and this helps by working to ensure that only those that are so-called “fit” for that environment will endure.6 In this way, the system can adjust its internal configuration to external perturbations, while minimizing the cost of diversity and changes to its overall organization.

Replication

Selection is performed on a population of agents resulting in those that are most "fit" for that environment becoming more prevalent within the future population

Image of a woman choosing between different products to purchase in a supermarket. Selection is performed on a population of agents resulting in those that are most relevant for that context becoming more prevalent within the future population

Lastly, replication. Unlike adaptation which is an immediate process operating on the level of an individual, evolution works instead on the level of a population of agents. It does not operate immediately but plays out over the course of several life cycles to the population. Elements that have proven to be functional within that environment during their life cycle are selected for replication, thus increasing the percentage of their representation within the future population in order for the overall system to exhibit more of their desired characteristics.

Environmental Complexity

With this process of evolution, a system can, through its iteration over a prolonged period of time, go from starting simple to becoming more complex through the retention of functional variants. In so doing, expand to become capable of operating within broader more complex environments. Continuing on with the example of the immune system, the immature immune system of a newborn child is dependent upon its mother to produce and provide it with antibodies in order to fend off invaders. As it grows and comes in contact with new antigens, naturally or through vaccines, it develops its own antibodies and retains copies that have proven successful for future application. Thus, building up a catalog of successful antibodies that can provide it with the requisite variety to maintain its physiological homeostasis within more threatening environments.
When a system has requisite variety, then it can be said to have control over itself within that particular environment.7 However, there is always a broader environment that will present the system with a wider more complex set of eventualities for it to deal with. As the system evolves, it retains the appropriate responses to a given perturbation until it has accumulated the requisite variety for a given environment, and then can expand into a broader one where again it will have to generate more variety in order to deal with a new set of perturbations.

Genetic Algorithms

Now that researchers have come to understand the dynamics of evolution, it is increasingly being used as an optimization algorithm in many areas.8 For example, computer scientists create programs or formulas that compete against one another to solve a problem, the winners being rewarded with “offspring” in the next generation that then compete again. Over a series of generations, one can use this process to evolve optimal solutions to difficult problems. The resulting method, under the name genetic algorithms, has become a widely used optimization method and a tool for complex systems researchers. Genetic algorithms are good at taking a very large search space and looking for optimal solutions through iteration.

Cite this article as: Joss Colchester, "Evolution," in Complexity Labs, July 15, 2014, http://complexitylabs.io/evolution/.
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2017-05-24T12:26:23+00:00