The Benefits of Statistical Noise

Article by Ruth Schmidt: “The year was 1999. Chicago’s public housing was in distress, with neglect and gang activity hastening the decline of already depressed neighborhoods. In response, the city launched the Plan for Transformation to offer relief to residents and rejuvenate the city’s public housing system: residents would be temporarily relocated during demolition, after which the real estate would be repurposed for a mixed-income community. Once the building phase was completed, former residents were to receive vouchers to move back into their safer and less stigmatized old neighborhood.

But a billion dollars and over 20 years later, the jury is still out about the plan’s effectiveness and side effects. While many residents do now live in safer, more established communities, many had to move multiple times before settling, or remain in high-poverty, highly segregated neighborhoods. And the idealized notion of former residents as “moving on up” in a free market system rewarded those who knew how to play the game—like private real estate developers—over those with little practice. Some voices were drowned out.

Chicago’s Plan for Transformation shared the same challenges—cost, time, a diverse set of stakeholders—as many similar large-scale civic initiatives. But it also highlights another equally important issue that’s often hidden in plain sight: informational “noise.”

Noise, defined as extraneous data that intrudes on fair and consistent decision-making, is nearly uniformly considered a negative influence on judgment that can lead experts to reach variable findings in contexts as wide-ranging as medicine, public policy, court decisions, and insurance claims. In fact, Daniel Kahneman himself has suggested that for all the attention to bias, noise in decision-making may actually be an equal-opportunity contributor to irrational judgment.

Kahneman and his colleagues have used the metaphor of a target to explain how both noise and bias result in inaccurate judgments, failing to predictably hit the bull’s-eye in different ways. Where bias looks like a tight cluster of shots that all consistently miss the mark, the erratic judgments caused by noise look like a scattershot combination of precise hits and wild misses…(More)”.