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IBM Plays SimCity With Portland, Oregon
Hugh Pickens writes "Portland, Oregon will be the first city to use IBM's new software called Systems Dynamics for Smarter Cities, containing 3,000 equations which collectively seek to model cities' emergent behavior and help them figure out how policy can affect the lives of their citizens. The program seeks to quantify the cause-and-effect relationships between seemingly uncorrelated urban phenomena. 'What's the connection, for example, between ... obesity rates and carbon emissions?' writes Greg Lindsay. 'To find out, simply round up experts to hash out the linkages, translate them into algorithms, and upload enough historical data to populate the model. Then turn the knobs to see what happens when you nudge the city in one direction.' One of the drivers of the 'Portland Plan' is the city's commitment to a 40 percent decrease in carbon emissions by 2030, which necessitates less driving and more walking and biking. After running the model, planners discovered a positive feedback loop: More walking and biking would lead to lower obesity rates for Portlanders. In turn, a fitter population would find walking and biking a more attractive option. But as the field of urban systems gathers steam, it's important to remember that IBM and its fellow technology companies aren't the first to offer a quantitative toolkit to cities. In the 1970s, RAND built models they thought could predict fire patterns in New York, and then used them to justify closing fire stations in NYC's poorest sections in the name of efficiency, a decision that would ultimately displace 600,000 people as their neighborhoods burned."
Tear up all the roads. Replace with rail.
While I think that your dismissal of models is a bit excessive(in a sense, all of mathematics doesn't tell you anything you didn't assume in your axioms: it just so happens that there is a lot of interesting stuff that you didn't know you were assuming...); but one should certainly be cautious about them.
Both an accurate model and a shitty model are, in the hands of a suitably skilled consultant's graphic design team, essentially identical in their ability to provide a dense veneer of scientific rationality, 3D-rendered near-future utopias attractively large-format-printed on posters suitable for display at planning meetings, and other charming props to hang on your existing plans and prejudices...
Things can get particularly ugly if there are large fudge factors in your initial dataset: modeling material stresses, or aerodynamics or such is hard because it is easy to be wrong about difficult stuff, and easy for slight mistakes to cascade(at least, though, there are correct answers that you can hopefully find, even if you don't know them just yet); doing societal cost/benefit analysis is hard because there are lots of factors that don't have quantified costs or benefits, so you can shove the model around just by slapping different price tags on unquantified things.