I couldn’t help think about our lecture on agent-based modeling when I started to read Helen Couclelis’s article on coupling better models with land-use planning. I know that, here, Couclelis is thinking about a different kind of modeling than that often implied in agent-based modeling approaches. She first notes how “sour” the relationship between planning and the academy has gone (135). Then, after detailing the debate over whether or not a planning process utilizing models has any effect on actual land-use plans, Couclelis delves into how, in practice, actual physical planning has become a decentralized activity that doesn’t incorporate strategy (1357).
I get it and I agree. The work being done on the ground, at least in North America, Couclelis argues no longer resembles any of the model runs done in academia. I think the quote Outdoor Addict utilized from the article sums up this problem well. In sum, the models have failed to predict the future accurately enough or, at least, keep up with land-use in practice. Even the “systematic effort to understand what makes certain things about the future predictable and others not, or how to prepare for genuinely unpredictable futures, have so far had only a negligible impact on land-use planning and modeling” (1360). So, what do we do?
Perhaps this is the problem inherent in any modeling process. We do not know that the future will follow the path layed-out in a model. We do not even know that the models initial parameters catch all the various variables and their interactions. Clearly, although she doesn’t say it quite so directly, modelers in this field are struggling with this problem. As Madskiier_JWong points out, uncertainty is the name of the game when it comes to modeling or trying to predict the future (essentially what modeling is when you unpack all its sophistication).
My suggestion: perhaps this is where agent-based modeling might come in. It seems from our previous lecture that agent-based modeling excels at representing lots of competing variables (or agents) and representing the emergent phenomenon that result from their disparate actions. In a land-use context, this might help us to better understand how certain sites might be used in future years and how they might handle such use. Couclelis does suggest this approach on page 1369 when she talks about assessing the cognitive and social dimensions of model interpretation so that modelers can move away from the macro level they currently operate on. Yet, she doesn’t devote much time to it, and such an approach doesn’t fit within the confines of models she describes that try to predict varying types of land-use cover and changes to infrastructure. But it may add a valuable dimension. A quick search online shows that many people are, indeed, already trying to couple these two ideas.
–ClimateNYC