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A Crowd of Experts?

The manner in which people make decisions can be affected by many factors, and one of the most powerful influences is the decision of the majority.  In lecture, we learned how it is usually the case in which people will follow the crowd when it comes to picking from a set of alternatives because it inherently appears to be the better option.  This behavior can be seen in many real life situations from the simple choice of where to shop for clothes in a mall to the more complex trading markets of the New York Stock Exchange.  Because the behavior is so prevalent, it is now used by people in power and even large scale companies in making business decisions.  There are also prediction market websites like Intrade that allow people to bet on the outcomes of real life events which makes for a perfect model of how one person responds to the choice of another.  Similarly, companies like Google have their own internal prediction markets which have proved to be a useful aid to the company’s leadership.  While the premise behind siding with the crowd seems to be a good way to make a decision, it also begs the question of just how accurate and reliable these crowds are.

First, the article “Google Bets on Value of Prediction Markets” analyzes Google’s internal system that allows employees to bet on projects and issues within the company.  Employees use Goobles instead of real money which are then exchanged for prizes among individuals who frequently bet on the correct outcomes.  This creates a competitive environment within the internal market that often reveals some key insights to bosses about the company’s inner workings.  For example, should a lot of people all predict that some product will be delayed, managers can look into the issues that may cause a delay in production and try to find a solution before the problem is upon them.  The article also describes how this prediction market affects how Google stock is traded and here we can find one of the prevalent errors that goes along with crowd behavior.  When there came a question of whether something was good for Google or bad for Google, it turned out that traders were often particular towards something being good because they were biased towards the company.  In such a way, the traders in the minority who realized that something was bad for Google were actually able to earn much more money than those who succumbed to the popular “good” belief.  However, returning to the positive side, the prediction markets were able to help Google organize the structure of its offices.  Researchers wanted to understand what types of people tended to bet in a particular way, and they found that employees in close physical proximity to each other were the most likely to place the same bet.  As a result, Google tends to keep employees in tight knit packs to promote mutual cooperation and productivity.  Google’s prediction market shows how crowds work together but are not always completely correct when it comes to decision making.

The article “When the Crowd Isn’t Wise” also mentions Google’s prediction market but is more focused on worldwide popular opinion, and in particular virtual prediction markets that are created naturally by the Internet.  The two main examples given are the crowd behaviors that result from social media like Twitter and blogs and also the real world betting sites mostly found in Europe.  The author points out how crowds often do predict the best response for many situations and gives some insight from his own life in which he was able to win money by picking Oscar winning movies solely based off reviews from people on the Internet.  However, the article tries to be critical towards those who just go along with crowds.  The first issue raised is that of predictions being made by the general public on events in which only a limited amount of people truly know the specific details of what is happening.  This tends to occur when decisions are made by a small group of people without outside influence such as in rulings of the Supreme Court and the elections of popes.  The author demonstrates this perfectly when he talks about how more than 75% of predictors on Intrade believed that the Supreme Court would declare the health care laws unconstitutional as of the night before the ruling.  It turned out that the bill was ruled constitutional, and thus the crowd was wrong so the majority on the site lost money.  The other flaw of crowd behavior mentioned in the article has to do with the actual people who form the population that makes a prediction.  Some people may doubt the opinion of the majority if it is not a qualified group.  With the belief that a crowd of entirely ordinary citizens can be flawed, some people then choose to turn to experts. However, there are many cases in which the experts fail to make the right decision as compared to a prediction market.  For example, it is a stock broker’s job to make a trade between two parties based off his knowledge and prediction on whether or not the stock will succeed.  As such, a broker may often go against popular opinion in order to maximize his potential benefit and yet popular opinion may still prove the better result a majority of the time.  With so many flaws, it can difficult to decide when it would be right to follow the crowd and when to not.

The author of “When the Crowd Isn’t Wise” offers a suggestion for the optimal crowd which one should follow and uses Twitter as his prime example.  Twitter users can be experts in a certain field and yet they can also have many followers who are both experts and non-experts in the same field.  The result is a large population of connected users who will often all speak about the same “trending” subject.  Thus, within the same discussion, there is a multitude of opinions coming from people with all different kinds of backgrounds.  According to the author, when analyzing this “prediction market”, one will generally find that the predicted outcome is usually accurate due the mix of beliefs of the individuals involved.

Both of these sources provide insight into the benefit and problems with following the crowd.  This behavior has brought fortune to companies and yet also caused the loss of an uncountable amount of money demonstrating how there can be times for following and times for not.  Therefore, the ultimate decision depends on the thinking of each individual and their own personal analysis of a given situation.  In class, we said a person will follow the decisions of a crowd for one of two reasons: direct benefit or belief in a crowd’s additional information.  People can just potentially be looking for an easy way out of a problem by utilizing the knowledge of others or honestly believing that the majority has made the right decision.  Whatever the reason one has for going with the crowd, he/she should consider the following quote used in “When the Crowd Isn’t Wise”: “The only good alternative to a few flawed opinions, some researchers argued, was a vast number of flawed opinions.”

Sources:

http://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.html?_r=2&

http://www.networkworld.com/news/2008/030508-google-prediction-markets.html

—  drew1203

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