Gambler's Fallacies

Imagine you’ve flipped a coin three times and come up with three heads. What do you predict will come up next? According to Stephen Dubner, most people predict tails next, because they believe that too many heads has come up and that the coin must fall on the tails side, even though there is still a 50% percent chance the coin lands on heads. This is the basis of the gambler’s fallacy, which is when people make a bad decision because of the sequence of what has happens, and it happens everywhere from baseball games to immigration courts. For example, people often lose thousands of dollars in casinos everyday simply because they bet on an outcome that they believe is due, even though the game has been designed to be truly, completely random.
Bad decisions are often made because of what humans like to do: find patterns, even in a series of completely random events. It’s also human nature to get rewards and avoid punishments. When strong incentives are given to people, this tends to offset the gambler’s fallacy, because people are required to think more about the decision they are making. For example, in an Indian bank, a strong incentive was provided for approving or denying the correct loans. If you did something wrong, you lost a lot of money, but if you did something right, you got a little more than the usual salary. There had previously been a 9% chance that your loan would get denied if two loans had already been approved. However, because an incentive was provided, this percentage dropped down to just 1%. Yes, the gambler’s fallacy was still present, but most cases disappeared because the strong incentive discouraged bank officers from merely taking a glance.

Humans always want to find consistency in life, and people always make bad decisions because of this. The only way to prevent the gambler’s fallacy is to realize that not all things in life follow a pattern, and that most events are truly