Topics: behavioral science

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)”.

Essay by Emir Efendić and Philippe Van de Calseyde: “Take your best guess for the questions below. Without looking up the answers, jot down your guess in your notes app or on a piece of paper. 

1. What is the weight of the Liberty Bell? 
2. Saudi Arabia consumes what percentage of the oil it produces? 
3. What percent of the world’s population lives in China, India, and the European Union combined?

Next, we want you to take a second guess at these questions. But here’s the catch, this time try answering from the perspective a friend whom you often disagree with. (For us, it’s the colleague with whom we shared an office in grad school, ever the contrarian.) How would your friend answer these questions? Write down the second guesses. 

Now, the correct answers. The Liberty Bell weighs 2,080 pounds, and, when we conducted the study in 2021, Saudi Arabia consumed 32.5 percent of the oil it produced, and 43.2 percent of the world’s population lived in China, India, and the European Union combined.

For the final step, compare your first guess with the average of both your guesses.

If you’re like most of the participants in our experiment, averaging the two guesses for each question brings you closer to the answer. Why this is has to do with the fascinating way in which people make estimates and how principles of aggregation can be used to improve numerical estimates. 

A lot of research has shown that the aggregate of individual judgements can be quite accurate, in what has been termed the “wisdom of the crowds.” What makes a crowd so wise? Its wisdom relies on a relatively simple principle: when people’s guesses are sufficiently diverse and independent, averaging judgments increases accuracy by canceling out errors across individuals. 

Interestingly, research suggests that the same principles underlying wise crowds also apply when multiple estimates from a single person are averaged—a phenomenon known as the “wisdom of the inner crowd.” As it turns out, the average guess of the same person is often more accurate than each individual guess on its own.

Although effective, multiple guesses from a single person do suffer from a major drawback. They are typically quite similar to one another, as people tend to anchor on their first guess when generating a second guess….(More)”.