What it means
The idea was popularised by the journalist James Surowiecki in his 2004 book, and it is often linked to an old story from a country fair in the early 1900s. Visitors guessed the weight of an ox, and the average of the guesses was very close to the true weight, even though individual guesses varied widely.
Errors in different directions tended to cancel out when the guesses were combined. Surowiecki said that crowds are wise when four conditions are met.
There must be diversity of opinion, so that people bring different information, and independence, so that people are not simply copying each other. There must also be decentralisation, so that people can draw on local knowledge, and a way to aggregate the individual views into one answer.
Finance uses the idea in many places. A stock price combines the views of millions of buyers and sellers, and surveys of analysts' forecasts are averaged into a consensus.
Prediction markets let people bet on outcomes, and the prices serve as probabilities that often beat individual experts. The idea is also practical inside a company.
Averaging independent forecasts from several people in sales, finance and operations often gives a better sales forecast than one person's estimate. The key is that people must give their views separately before they hear the boss's opinion.
The crowd can also be foolish. When people copy each other, follow the herd or share the same bias, errors add up instead of cancelling, as in asset bubbles and panics.
Good forecasters therefore protect independence and seek out different viewpoints. A practical rule for managers is to ask for numbers before discussion and to record them in writing.
Once people have heard what the senior person thinks, their forecasts tend to drift toward it, and the benefit of many separate views is lost. Collecting estimates through a short form or survey keeps the process simple.
In practice
Real-world examples.
Example
A retailer collects separate sales forecasts from 12 store managers before a holiday season. The average of the forecasts proves closer to actual sales than the head office model, so the company uses both numbers to plan inventory.
Example
A technology company runs an internal prediction market on whether a product will ship on time. Staff from engineering, sales and support trade on the outcome, and the market price warns management that the date is unrealistic.
Example
An investor uses the consensus estimate of dozens of analysts as a benchmark for a company's earnings. When the company reports, she compares the result with the consensus to see whether it has surprised the market.
Formula
Calculation
Crowd estimate = Sum of individual estimates / Number of estimates
Suppose five managers forecast next year's sales as $400,000, $480,000, $520,000, $560,000 and $590,000. The sum is 2,550,000, so the crowd estimate is 2,550,000 / 5 = $510,000. If actual sales are $500,000, the crowd's error is $10,000. The individual errors are $100,000, $20,000, $20,000, $60,000 and $90,000, with an average of 290,000 / 5 = $58,000, so the crowd estimate beat the typical individual.Case study
Seen in the real world.
Lakeside Foods is a fictional manufacturer, and this story is illustrative only. The finance director usually relied on a single sales forecast from the head of sales, which was often too optimistic. This year, he asked ten people from sales, production, finance and logistics to submit forecasts independently by email.
The forecasts ranged from $8,000,000 to $12,500,000, and their average was $10,000,000, compared with the head of sales' figure of $12,000,000. Actual sales came in at $9,800,000, so the average was off by $200,000 while the single forecast was off by $2,200,000. The finance director made the independent group forecast a standing part of the planning cycle. He also kept a record of each person's accuracy over time, which helped the group learn where its estimates tended to run high or low.
Watch out
Common mistakes.
- Gathering opinions in an open meeting, when the loudest voice influences the others and independence is lost.
- Assuming that a larger crowd is always wiser, when a large crowd of people with the same bias will share the same error.
- Using the average when the question needs special expertise, since a crowd of non-experts may not beat a true specialist on technical matters.
Questions
People also ask.
Who wrote the book on the wisdom of crowds?
James Surowiecki, a journalist, published a book with this title in 2004 that explained how and when groups make good decisions.
Why do markets sometimes fail despite the wisdom of crowds?
Bubbles and panics happen when investors copy each other, so the independence needed for accurate averages disappears.
How can a company apply the idea?
Collect forecasts separately from people with different roles, average them and compare the result with the usual single forecast.
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