This use of building blocks to generate internal models is a pervasive feature of complex adaptive systems.

Model building is the art of selecting those aspects of a process that are relevant to the question being asked. As with any art, this selection is guided by taste, elegance, and metaphor; it is a matter of induction, rather than deduction. High science depends on this art.

With theory, we can separate fundamental characteristics from fascinating idiosyncrasies and incidental features. Theory supplies landmarks and guideposts, and we begin to know what to observe and where to act.

Holland's and Kauffman's work, together with Dawkins' simulations of evolution and Varela's models of autopoietic systems, provide essential inspiration for the new discipline of artificial life, This approach, initiated by Chris Langton (1989, 1992), tries to develop technological systems (computer programs and autonomous robots) that exhibit lifelike properties, such as reproduction, sexuality, swarming, and co-evolution.

If we are to understand the interactions of a large number of agents, we must first be able to describe the capabilities of individual agents.

Looking back to data, we can see if the consequences are plausible; looking forward to theory, we can see if general principles are suggested.

Particular individuals do not recur, but their building blocks do.