I think one of the most interesting aspects of our exploration of ABMs isn’t the models themselves but the concept of emergence which lies at the heart of this whole methodology. Thinking of systems as the whole of a good many moving parts implies a startling paradigm shift whereby many systems might simply arrive at a destination through no prior planning or intentionality. Instead, these systems create complex patterns or phenomenon simply due to a variety of independent agents going about their business.
So far, we’ve thought about emergence in terms of banks or traffic which allows for such an explanation without too much hesitation (although, as a bank manager, I would certainly like to think I have much more control over the functioning of my business). But what about when we apply the idea of emergent phenomenon to more natural science-based systems?
I realize both of our authors write about ABM – for the most part – as a new tool for social scientists. Eric Bonabeau, for example, appears more likely to discuss crowd panic (7282) or the role of ABM in social sciences (7287) than ant hill dynamics or starlings (cool video, by the way). Yet many of the examples of ABMs that we saw in class (such as Boids) could just as easily involve natural systems. One might easily consider biological systems as models for how complex behavior can stem uncreated from far simpler behaviors such as the chemistry of carbon compounds.
Our world is filled with both natural and sociological situations that display patterns of emergence, as both Bonabeau, O’Sullivan and Peter have pointed out. This idea – emergence – provides a useful paradigm for understanding and exploring this phenomenon wherever it may occur. ABMs may just be a part of this exploration.
–ClimateNYC
Tags: emergence, emergent phenomenon
I completely agree with you. There is something about the ABM approach that makes it very suitable for exploring emergent patterns. Without it, emergent phenomena are mysterious and remains just outside our reach. However, exploration is not much fun if you cannot eventually draw any conclusions. Is it possible to build a model that presents convincing outcomes, without it also making a prediction? How can we address this tension?