You must
have heard about the “jelly bean experiment”. If one asks a large enough number
of people to guess the number of jelly beans in a jar, the averaged answer is
likely to be very close to the correct number. True, occasionally someone may
guess closer to the true number. But as you repeat the experiment, the same
person never is better every time - the crowd is smarter than any individual.
[2] “The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How
Collective Wisdom Shapes Business, Economies, Societies and Nations”, published
in 2004, is a book written by James Surowiecki about the aggregation of
information in groups, resulting in decisions that, he argues, are often better
than could have been made by any single member of the group. The book presents
numerous case studies and anecdotes to illustrate its argument, and touches on
several fields, primarily economics and psychology. [3]
Surowiecki
breaks down the advantages he sees in disorganized decisions into three main
types, which he classifies as :
Cognition - Thinking and information Processing
Market
judgment, which he argues can be much faster, more reliable, and less subject
to political forces than the deliberations of experts or expert committees. Such
problems arise when we can only guess the answer – as e.g. about the contents
of the jelly bean jar, or about the future.
Coordination
Coordination
of behavior includes optimizing the utilization of a popular bar and not
colliding in moving traffic flows. how to we coordinate behaviour with each
other – say in traffic – knowing that everyone else is trying to do the same? Common
understanding within a culture allows remarkably accurate judgments about
specific reactions of other members of the culture.
Cooperation
How groups
of people can form networks of trust without a central system controlling their
behavior or directly enforcing their compliance. How do we get self-interested,
distrustful people to work together, even when narrow self-interest would seem
to dictate that no individual should take part – as in politics?
Behavioural
economists and sociologists have gone beyond the anecdotic and systematically
studied the issues, and have come up with surprising answers. Capturing the
‘collective’ wisdom best solves cognitive problems. Four conditions apply.
There must be: (a) true diversity of opinions; (b) independence of opinion (so
there is no correlation between them); (c) decentralisation of experience; (d)
suitable mechanisms of aggregation. [2]
As it
happens, the possibilities of group intelligence, at least when it came to
judging questions of fact, were demonstrated by a host of experiments conducted
by American sociologists and psychologists between 1920 and the mid-1950s, the
heyday of research into group dynamics. Although in general, as we'll see, the
bigger the crowd the better, the groups in most of these early
experiments—which for some reason remained relatively unknown outside of
academia—were relatively small. Yet they nonetheless performed very well. The
Columbia sociologist Hazel Knight kicked things off with a series of studies in
the early 1920s, the first of which had the virtue of simplicity. In that study
Knight asked the students in her class to estimate the room's temperature, and
then took a simple average of the estimates. The group guessed 72.4 degrees,
while the actual temperature was 72 degrees. This was not, to be sure, the most
auspicious beginning, since classroom temperatures are so stable that it's hard
to imagine a class's estimate being too far off base. But in the years that
followed, far more convincing evidence emerged, as students and soldiers across
America were subjected to a barrage of puzzles, intelligence tests, and word
games. The sociologist Kate H. Gordon asked two hundred students to rank items
by weight, and found that the group's "estimate" was 94 percent
accurate, which was better than all but five of the individual guesses. In
another experiment students were asked to look at ten piles of buckshot—each a
slightly different size than the rest—that had been glued to a piece of white
cardboard, and rank them by size. This time, the group's guess was 94.5 percent
accurate. A classic demonstration of group intelligence is the
jelly-beans-in-the-jar experiment, in which invariably the group's estimate is
superior to the vast majority of the individual guesses. When finance professor
Jack Treynor ran the experiment in his class with a jar that held 850 beans,
the group estimate was 871. Only one of the fifty-six people in the class made
a better guess. [1]
There are
two lessons to draw from these experiments. First, in most of them the members
of the group were not talking to each other or working on a problem together.
They were making individual guesses, which were aggregated and then averaged.
This is exactly what Galton did, and it is likely to produce excellent results.
Second, the group's guess will not be better than that of every single person
in the group each time. In many (perhaps most) cases, there will be a few
people who do better than the group. This is, in some sense, a good thing,
since especially in situations where there is an incentive for doing well
(like, say, the stock market) it gives people reason to keep participating. But
there is no evidence in these studies that certain people consistently
outperform the group. In other words, if you run ten different
jelly-bean-counting experiments, it's likely that each time one or two students
will outperform the group. But they will not be the same students each time.
Over the ten experiments, the group's performance will almost certainly be the
best possible. The simplest way to get reliably good answers is just to ask the
group each time. [1]
In
probability theory, the law of large numbers (LLN) is a theorem that describes
the result of performing the same experiment a large number of times. According
to the law, the average of the results obtained from a large number of trials
should be close to the expected value, and will tend to become closer as more
trials are performed. The LLN is important because it "guarantees"
stable long-term results for the averages of random events. For example, while
a casino may lose money in a single spin of the roulette wheel, its earnings
will tend towards a predictable percentage over a large number of spins. Any
winning streak by a player will eventually be overcome by the parameters of the
game. It is important to remember that the LLN only applies (as the name
indicates) when a large number of observations are considered. [4]
In an
interesting spin (that came up on a web search) I read that : “While the Bible
makes it clear that the wisdom of crowds may not be reliable and can be
dangerous (Matt. 7:13-14), there is another way collective wisdom can be helpful.
In Proverbs 11:14, we read, “Where there is no counsel, the people fall; but in
the multitude of counselors there is safety.” One of the benefits of the body
of Christ is that we can assist one another—in part by working together to seek
God’s wisdom. When we join together to pursue God’s purposes, we find safety in
His provision of each other and receive His wisdom for the challenges of life.”
[5]
On the
other hand many will argue that Collective Intelligence is the real
representation (the real life manifestation) of the inherent wisdom of crowds.
In 1907,
Sir Francis Galton asked 787 villagers to guess the weight of an ox. None of
them got the right answer, but when Galton averaged their guesses, he arrived
at a near perfect estimate. This is a classic demonstration of the “wisdom of
the crowds”, where groups of people pool their abilities to show collective
intelligence. Galton’s story has been told and re-told, with endless variations
on the theme. If you don’t have an ox handy, you can try it yourself with the
beans-in-a-jar experiment sited in the beginning. To Iain Couzin from Princeton
University, these stories are a little boring. Everyone is trying to solve a
problem, and they do it more accurately together than alone. Whoop-de-doo. By
contrast, Couzin has found an example of a more exciting type of collective
intelligence—where a group solves a problem that none of its members are even
aware of. Simply by moving together, the group gains new abilities that its
members lack as individuals. [6] That, in his case, is demonstrated with fish,
but more natural observations have been analysed and categorized as collective
intelligence (or lack thereof) with flocks of birds, or ants.
Collective
intelligence or in more general terms Crowd Dynamics have been the subjects of
intense socio and economic studies, as well as very practical people in motion
dynamics, that could help engineer better environments for us to move in high
traffic, or in congested areas.
Imagine
that you are French. You are walking along a busy pavement in Paris and another
pedestrian is approaching from the opposite direction. A collision will occur
unless you each move out of the other's way. Which way do you step? The answer
is almost certainly to the right. Replay the same scene in many parts of Asia,
however, and you would probably move to the left. It is not obvious why. There
is no instruction to head in a specific direction (South Korea, where there is
a campaign to get people to walk on the right, is an exception). There is no
simple correlation with the side of the road on which people drive: Londoners
funnel to the right on pavements, for example. Instead, says Mehdi Moussaid of
the Max Planck Institute in Berlin, this is a behaviour brought about by
probabilities. If two opposing people guess each other's intentions correctly,
each moving to one side and allowing the other past, then they are likely to
choose to move the same way the next time they need to avoid a collision. The
probability of a successful manoeuvre increases as more and more people adopt a
bias in one direction, until the tendency sticks. Whether it's right or left
does not matter; what does is that it is the unspoken will of the majority. [7]
To give an example : “The biggest test possible of crowd dynamics tools and
techniques is the haj, the annual pilgrimage to Mecca in Saudi Arabia that
Muslims are expected to carry out at least once in their lives if they can.
With as many as 3m pilgrims making the journey each year, the haj has a long
history of crowd stampedes and deaths.”
Applications
of the wisdom-of-crowds effect exist in three general categories: Prediction
markets, Delphi methods, and extensions of the traditional opinion poll.
The
prediction market, is essentially a speculative or betting market created to
make verifiable predictions. Assets are cash values tied to specific outcomes
(e.g., Candidate X will win the election) or parameters (e.g., Next quarter's
revenue). The current market prices are interpreted as predictions of the
probability of the event or the expected value of the parameter. Betfair is the
world's biggest prediction exchange, with around $28 billion traded in 2007.
NewsFutures is an international prediction market that generates consensus
probabilities for news events. Several companies now offer enterprise class
prediction marketplaces to predict project completion dates, sales, or the
market potential for new ideas. A number of Web-based quasi-prediction
marketplace companies have sprung up to offer predictions primarily on sporting
events and stock markets but also on other topics. Those companies include
Piqqem, Cake Financial, Covestor, Predictify, and the Motley Fool (with its
Fool CAPS product). The principle of the prediction market is also used in
project management software such as Yanomo to let team members predict a
project's "real" deadline and budget. [3]
The Delphi
method is a systematic, interactive forecasting method which relies on a panel
of independent experts. The carefully selected experts answer questionnaires in
two or more rounds. After each round, a facilitator provides an anonymous
summary of the experts’ forecasts from the previous round as well as the
reasons they provided for their judgments. Thus, participants are encouraged to
revise their earlier answers in light of the replies of other members of the
group. It is believed that during this process the range of the answers will
decrease and the group will converge towards the "correct" answer. Delphi
is based on the principle that forecasts (or decisions) from a structured group
of individuals are more accurate than those from unstructured groups. [8]
From a
corporate perspective, the wisdom of crowds is systematically misinterpreted,
predominately as another way of companies getting the idea of what their
(potential) customers want. In arithmetic terms X people want A feature, while
Y people desire B featre. If X larger than Y, then let’s go with the A feature.
This of course has nothing to do with the arguments, observations and analysis,
as those were discussed in all the above paragraphs. To give you an example :
“The
internet is harnessing crowds like never before. Nowhere is this more evident
than in the recent crowdfunding movement. Sites like Lending Club, Indiegogo
and Kickstarter show just how powerful crowds can be in turning ideas and
dreams into reality — sometimes paying dividends at the same time. But the
wisdom of crowds is manifesting itself in other ways too, namely through
initiatives run by larger brands. And in many cases, they’re making bold moves
to give people the tools they need to take action en masse for social good. These
tools come in all different shapes and sizes. Some are social media campaigns. Other
brands help crowds do social good simply by making a purchase. And some brands
have opened up APIs to their customers and developer communities that can be
harnessed for good. The incredible news here is that crowds are driving all of
this positive action. Brands wouldn’t be investing in these sorts of programs,
initiatives, challenges and giveaways if their customers weren’t asking for
them.” [9]
“A problem shared is a
problem halved”, goes the old saying. But what happens if you share a problem
with millions of people? Are you left with a millionth of a problem? Or just
lots of rubbish suggestions? [10]
James Surowiecki is a staff writer at The New Yorker, where he writes
the popular business column, "The Financial Page." His work has
appeared in a wide range of publications, including The New York Times, The
Wall Street Journal, Artforum, Wired, and Slate. He lives in Brooklyn, New
York. Follow him on TedX here.
[1] J. Surowiecki, The wisdom of crowds, 2004,
Anchor
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