Take the 1968 decision by New York Mayor John V. Lindsay to hire the RAND Corporation to streamline city management through computer models. It built models for the Fire Department to predict where fires were likely to break out, and to decrease response times when they did. But, as the author Joe Flood details in his book “The Fires,” thanks to faulty data and flawed assumptions — not a lack of processing power — the models recommended replacing busy fire companies across Brooklyn, Queens and the Bronx with much smaller ones.

What RAND could not predict was that, as a result, roughly 600,000 people in the poorest sections of the city would lose their homes to fire over the next decade. Given the amount of money and faith the city had put into its models, it’s no surprise that instead of admitting their flaws, city planners bent reality to fit their models — ignoring traffic conditions, fire companies’ battling multiple blazes and any outliers in their data.

The final straw was politics, the very thing the project was meant to avoid. RAND’s analysts recognized that wealthy neighborhoods would never stand for a loss of service, so they were placed off limits, forcing poor ones to compete among themselves for scarce resources. What was sold as a model of efficiency and a mirror to reality was crippled by the biases of its creators, and no supercomputer could correct for that.

Despite its superior computing power and life-size footprint, Pegasus’ project is hobbled by the equally false assumption that such smart cities are relevant outside the sterile conditions of a computer lab. There’s no reason to believe the technologies tested there will succeed in cities occupied by people instead of Sims.

The bias lurking behind every large-scale smart city is a belief that bottom-up complexity can be bottled and put to use for top-down ends — that a central agency, with the right computer program, could one day manage and even dictate the complex needs of an actual city.

Instead, the same lesson that New Yorkers learned so painfully in the 1960s and ’70s still applies: that the smartest cities are the ones that embrace openness, randomness and serendipity — everything that makes a city great.