The Looking Glass of General System Theory

Swarm Intelligence In the 20th century, there was a need to build more complex machinery which required components from heterogeneous technologies such as from mechanical, electronic or chemical sources. Such machinery might be an aircraft, which would also involve the interaction of man and machine, as well as a host of economics and social issues. The number of issues to be considered can often be innumerable, and as such another approach was needed in order to solve such problems.

Enter the Systems Approach, which essentially focuses on the ???big picture??? of an issue at hand. Given a specific objective, the systems approach allows us to look at a complex network of interactions then consider the most optimized solution at maximum efficiency and minimal cost. This approach appeared very general and it appealed across various disciplines which is rightfully so because of how the approach required the re-orientation in scientific thinking to deal with complexities with wholes or systems in all fields of knowledge (Bertalanffy, 1976).

Before the development of the General Systems Theory, the ways in which we explained certain phenomena in our world were somewhat limited. For example, in Second World War, we simple blamed Hitler as the cause of people???s suffering. Applying the Systems Approach, we can see that Hitler was no super human as the World War was not started by one man alone, but by a combination of forces (prejudices, ideologies, social trends, etc) within several related systems. From this example, we can see how the systems approach is innovative in that it studies systems as an entity with open interactions to other entities and by doing so, we are taking a phenomena and slicing it into manageable pieces for examination rather than to deal with it too narrowly or to broadly.

As the General Systems Theory is isomorphic, some might argue that it discovers nothing new and worse still, it cannot be reduced to lower level science for analysis. It is true that this danger of meaningless analogies exists, but one must recall that the theory is actually for scientific interpretation and theory where none existed. The systems approach should be seen as work in progress and in constant need of ideas and exploration. To date, three popular contributions to it have emerged, namely Wiener???s Cybernetics (1948), Shannon and Weaver???s Information Theory (1949) and Morgenstern???s Game Theory (1947). These theories have been well used in various disciplines and are testaments to the utility of the Systems Approach.

We can find real-world applications of general system theory in the book ???On our Nonexistence as Entities: The Social Organism??? (2001). Author Kennedy talked about how systems can be seen a multiple ???zoom angles??? (frames of references) and at different levels (microscopic to cosmic). The main idea behind this chapter seems to be about adaptability or survival, as seen from the microscopic germ, to the rats & roaches that survive natural disasters, to human being who are said to be the most adaptable in any environment and even to Gaia (Mother Earth). In studying social behavior as optimization as seen in flock, herds and swarms, I enjoyed how Kennedy applied what was seen in nature to how we could do the same for robots. He suggested how cheaper mini robots a singular robot (i.e. Dante II) should be made for harsh terrain exploration as by process of trial and error, it would be quicker for the multiple mini robots to communicate and adapt to the environment as compared to one robot making mistakes.

Just this weekend, I was driving along the highway around campus when I saw a flock of swallows fly together in a haphazard yet beautiful pattern, as if it were a coordinated dance. Indeed when I went home to work on my computer, there on my screensaver was the exactly same pattern of streaking colors. While it is possible to simulate the behavior of nature with a few simple rules (pg 112, fig 3.6), given a decent feedback mechanism, this apparently disordered behavior might be what holds the key to good optimization processes. Like how a rabbit runs away from its predator in quick random scurries, an example of how this behavior could be employed is in computer gaming. The essence of playing a game is to encounter situations in which we had to find creative ways in which an objective could be met. If the situation were the same, there would be little time before the best way is discovered and the player would find little reward in playing that game. This is why multiplayer games have such staying power. Good multiplayer games allow for players to challenge each other???s strategies in which they can be ever changing and unpredictable. With each round (iteration) of a multiplayer game, players who are more willing to try out different strategies should fair better each time because by process or trial and error, they would start to discover their opponents??? strengths and weaknesses. In order to survive, trial and error tactics works great as a learning ability for both predator seeking its prey and prey escaping its predator. By understanding this overarching pattern of disorder, we have in a sense created order in chaos which benefits us, without the need for us to even be conscious of it.

References
Kennedy, J., Eberhart, C., & Shi, Y. (2001). On our nonexistence as entities: The social organization. Swarm intelligence. San Francisco: Morgan Kaufmann.

Von, Bertalanffy, L. (1976). General system theory: Foundations, development, applications.New York: Braziller. Selection.