The Monte Carlo fallacy also Gambler’s fallacy is the idea that deviation from normal behaviour are seen in repetitive trials of a random event then these uncommon event are expected to balance out by an opposing deviation in the upcoming event. For example, a coin is flipped again and again and tails come up a majority of the time than expected, gambler’s fallacy predicts that heads will likely come out more in future tosses. The expectation is commonly referred to being due. This is informal fallacy or more commonly known as the law of averages.
The monte carlo fallacy completely involves an affirmation of negative association between random events and involves a refusal of the commutability of outcomes of the random event. The inverse gambler’s fallacy is the concept that an improbable result of a random event such as tossing snake eyes or double one on a dice pair indicates that the event is to have happened several times before attaining that outcome.
The fallacy is based on the same error, namely, a failure to comprehend statistical independence. Two actions are statistically independent when the incidence of one has no statistical effect upon the incidence of the other. Statistical independence is linked to the notion of chance in the following way: what makes a sequence random is that its members are statistically independent of each other. For instance, a list of random events is such that a person cannot predict any better than chance any other member of the list based upon a knowledge of the other list members.
Many games of chance are based upon the ability to generate a random event or outcome, statistically independent sequences, for example the series of numbers a roulette wheel can generate, or by tossing unloaded dice. A coin can also generate a random series of “tails” or ” heads “, each toss is statistically independent of any other toss. This is what is usually meant by calling the coin “fair”, namely, that it is not biased in such a way as to generate a predictable sequence.
For example, if the roulette wheel at the Casino was fair, then the probability of the ball landing on black was a little less than one-half on any given turn of the wheel. Since the roulette wheel is unbiased, the resulting colors are statistically independent of each other, thus even if the results are black for a large amount of times, the probability is still the same. If it were possible to predict one color from others, then the wheel would not be a good randomizer. Remember that neither a roulette wheel nor the ball has a memory.
Every gambling “system” is based on this gambler’s fallacy or Monte Carlo fallacy. Any gambler who believes that he can document the results of a roulette wheel, or lotto numbers, and or the throws at a craps table, use this data to predict results is probably doing some form of the gambler’s fallacy or monte carlo fallacy.
This article was written by Alexis.