A easy to follow article, Learning about life, on decentralized systems, has been written for students by Mitchel Resnick of the Epistemology and Learning Group, The Media Laboratory, Massachusetts Institute of Technology. This puts the concept of decentralized complex systems nicely into perspective and is well worth a read. Here are a few snippets from the article:
In recent years, a new set of models and metaphors have begun to spread through the scientific community, and gradually into the culture at large. Many of these new ideas come not from physics, but from biology. In a growing number of disciplines, researchers are now viewing the systems they study less like clockwork mechanisms, and more like complex ecosystems. Increasingly, ideas from ecology, ethology, and evolution are spreading beyond their disciplinary boundaries. Ideas like self-organization and emergence are affecting the direction and nature of research in many other fields, from economics to engineering to anthropology. In general, there is a pronounced shift toward decentralized models, in which patterns are determined not by some centralized authority, but by local interactions about decentralized components. The growing interest in the field of Artificial Life is both a reflection of and a contributor to this broader intellectual shift.
Biology-inspired models and metaphors will have their greatest influence when they spread outside of the scientific community and into the general culture. For children growing up in the world today, learning about living systems is taking on a new urgency. The point is not just to understand the biological world (though that, of course, is a worthy endeavor). Rather, decentralized models of living systems provide a basis for understanding many other systems and phenomena in the world. As these ideas seep out of the scientific community, they are likely to cause deep changes in how children (and adults too) make sense of the world. This paper explores ways to help make that happen.
...many students are fascinated by decentralized phenomena. But they also have a difficult time understanding and creating such phenomena. They often slip back into centralized ways of thinking. As I have worked with students, I have developed a list of "guiding ideas" that seem to help students make sense of decentralized phenomena. These guiding ideas are not very "strong." They are neither prescriptive nor predictive. They don't tell you precisely how to think about decentralized systems, nor do they tell you how to make accurate predictions about such systems. Rather, they are ideas to keep in mind as you try to make sense of an unfamiliar system, or to design a new one. They highlight some pitfalls to avoid, and some possibilities not to overlook. In this section, I discuss five of these guiding ideas.
The centralized mindset has undoubtedly affected many theories and trends in the history of science. Just as children assimilate new information by fitting it into their pre-existing models and conceptions of the world, so do scientists. As Keller (1985) puts it: "In our zealous desire for familiar models of explanation, we risk not noticing the discrepancies between our own predispositions and the range of possibilities inherent in natural phenomena. In short we risk imposing on nature the very stories we like to hear." In particular, we risk imposing centralized models on a decentralized world.
For many years, there has been a self-reinforcing spiral. People saw the world in centralized ways, so they constructed centralized tools and models, which further encouraged a centralized view of the world. Until recently, there was little pressure against this centralization spiral. For many things that people created and organized, centralized approaches tended to be adequate, even superior to decentralized ones. Even if someone wanted to experiment with decentralized approaches, there were few tools or opportunities to do so.
But the centralization spiral is now starting to unwind. As organizations and scientific models grow more complex, there is a greater need for decentralized ideas. At the same time, new decentralized tools (like StarLogo) are emerging that enable people to actually implement and explore such ideas. Still, many challenges lie ahead. We need to develop better explanations of why people are so committed to centralized explanations. And we to develop better tools to help people visualize and manipulate decentralized interactions. Ultimately, we need to develop new tools and theories that avoid the simple dichotomy between centralization and decentralization, but rather find ways to integrate the two approaches, drawing on the best of both. Only then will we truly be ready to move beyond the centralized mindset.