One of the breakthroughs of the last couple of decades was the ability to replicate the complex dynamics of flocks and swarms through individual-based models. In short, one could replicate the dynamics of bird flocks or ant swarms by creating a large number of autonomous agents (individual birds or ants), giving each one a few simple instructions, and turning the group loose in a simulation. A quick internet search on the keyword ``boids'' will give many links to examples of one of the first examples of this sort of simulation described in popular literature.
There are many things in our world that seem complex, which can in fact be conceived as a collection of many interacting individuals with simple rules. An ecosystem, an economy, a culture, or a mob is complex when viewed synoptically, but each can be reduced to a large number of individuals acting by simple, largely self-interested rules. One of the most familiar examples that we have is automobile traffic patterns. Traffic seems complex or even chaotic (especially in Utah!), and there is an extensive body of literature trying to improve our ability to describe traffic synoptically through increasingly elaborate differential equations (I have a now dated review of such examples in my dissertation). However, by creating an individual-based model of a large number of automobiles in a road network, some modelers have been able to recreate the complex pattersn of moving traffic jams, congestions, and other aspects of traffic flow through three simple rules given to each car in the simulation (described in Resinck:1996 and Barret:1996):
Many social and biological processes are examples of such decentralized systems. If you have ever purchased stock, voted in an election, or sat in the crowd at a sporting event or rock concert you have probably been aware of some greater consciousness that is not in anyone's control. An ecosystem, with its infinite number of interactions, is another such system where very minor perturbations can effect profound or even catastrophic changes, even when the same system can rebound with little ill effect from other seemingly major changes. The mind itself, which is sometimes regarded as being too complex to properly understand, can be thought of as the interplay of a very large numbers of neurons. No single neuron is particularly complex, and there is no central spot in the brain where consciousness resides, so it could be that conscousness itself is the result of the interaction of neurons behaving by individually simply rules.
My interest in linking this to geography is the idea that many of the complex spatial patterns that emerge in human and biological landscapes is really the result of locally simple rules. One major tool for exploring this is cellular automata, which allows for properties of any cell in gridded space to be modeled as a function of that cell's neighbors. The dynamics of cellular automata are so similar to living processes that many of the famous examples are referred to by biological metaphors (Conway's game of life and Langtons ants are two good examples).
None of these ideas are new or novel. However, we will appreciate these ideas more as there get to be more and more people playing with them. Up to the last few years, exploration of these processes was limited to a small number of people who had the programming skills, patience, and resources to try these ideas out on expensive computers. Now that computers are cheap and widespread, and software tools are avaible, there will be a large number of people experimenting with the same ideas who are not necessarily computer programmers. Software tools such as Swarm and StarLogo assist in these explorations, making them available to a wider user community. it is my home that Kenge, which should allow the easy integration of GIS, agent-based models, and cellular automata, will fill a similar niche.