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Demography, 57 4. Clark, Michael , Jolley, David , Benbow, Susan , Greaves, Nicola and Greaves, Ian Exploring the scope for Normalisation Process Theory to help evaluate and understand the processes involved when scaling up integrated models of care: a case study of the scaling up of the Gnosall Memory Service. Journal of Integrated Care. When flipping a fair coin 21 times, the outcome is equally likely to be 21 heads as 20 heads and then 1 tail.

These two outcomes are equally as likely as any of the other combinations that can be obtained from 21 flips of a coin. All of the flip combinations will have probabilities equal to 0. Assuming that a change in the probability will occur as a result of the outcome of prior flips is incorrect because every outcome of a flip sequence is as likely as the other outcomes. The fallacy leads to the incorrect notion that previous failures will create an increased probability of success on subsequent attempts.

If a win is defined as rolling a 1, the probability of a 1 occurring at least once in 16 rolls is:. According to the fallacy, the player should have a higher chance of winning after one loss has occurred. The probability of at least one win is now:.

By losing one toss, the player's probability of winning drops by two percentage points. With 5 losses and 11 rolls remaining, the probability of winning drops to around 0. The probability of at least one win does not increase after a series of losses; indeed, the probability of success actually decreases , because there are fewer trials left in which to win.

After a consistent tendency towards tails, a gambler may also decide that tails has become a more likely outcome. This is a rational and Bayesian conclusion, bearing in mind the possibility that the coin may not be fair; it is not a fallacy. Believing the odds to favor tails, the gambler sees no reason to change to heads.

However it is a fallacy that a sequence of trials carries a memory of past results which tend to favor or disfavor future outcomes. The inverse gambler's fallacy described by Ian Hacking is a situation where a gambler entering a room and seeing a person rolling a double six on a pair of dice may erroneously conclude that the person must have been rolling the dice for quite a while, as they would be unlikely to get a double six on their first attempt.

Researchers have examined whether a similar bias exists for inferences about unknown past events based upon known subsequent events, calling this the "retrospective gambler's fallacy". An example of a retrospective gambler's fallacy would be to observe multiple successive "heads" on a coin toss and conclude from this that the previously unknown flip was "tails". In his book Universes , John Leslie argues that "the presence of vastly many universes very different in their characters might be our best explanation for why at least one universe has a life-permitting character".

All three studies concluded that people have a gamblers' fallacy retrospectively as well as to future events. In , Pierre-Simon Laplace described in A Philosophical Essay on Probabilities the ways in which men calculated their probability of having sons: "I have seen men, ardently desirous of having a son, who could learn only with anxiety of the births of boys in the month when they expected to become fathers.

Imagining that the ratio of these births to those of girls ought to be the same at the end of each month, they judged that the boys already born would render more probable the births next of girls. This essay by Laplace is regarded as one of the earliest descriptions of the fallacy. After having multiple children of the same sex, some parents may believe that they are due to have a child of the opposite sex. While the Trivers—Willard hypothesis predicts that birth sex is dependent on living conditions, stating that more male children are born in good living conditions, while more female children are born in poorer living conditions, the probability of having a child of either sex is still regarded as near 0.

Perhaps the most famous example of the gambler's fallacy occurred in a game of roulette at the Monte Carlo Casino on August 18, , when the ball fell in black 26 times in a row. Gamblers lost millions of francs betting against black, reasoning incorrectly that the streak was causing an imbalance in the randomness of the wheel, and that it had to be followed by a long streak of red. The gambler's fallacy does not apply in situations where the probability of different events is not independent.

In such cases, the probability of future events can change based on the outcome of past events, such as the statistical permutation of events. An example is when cards are drawn from a deck without replacement. If an ace is drawn from a deck and not reinserted, the next draw is less likely to be an ace and more likely to be of another rank.

This effect allows card counting systems to work in games such as blackjack. In most illustrations of the gambler's fallacy and the reverse gambler's fallacy, the trial e. In practice, this assumption may not hold. For example, if a coin is flipped 21 times, the probability of 21 heads with a fair coin is 1 in 2,, Since this probability is so small, if it happens, it may well be that the coin is somehow biased towards landing on heads, or that it is being controlled by hidden magnets, or similar.

Bayesian inference can be used to show that when the long-run proportion of different outcomes is unknown but exchangeable meaning that the random process from which the outcomes are generated may be biased but is equally likely to be biased in any direction and that previous observations demonstrate the likely direction of the bias, the outcome which has occurred the most in the observed data is the most likely to occur again.

The opening scene of the play Rosencrantz and Guildenstern Are Dead by Tom Stoppard discusses these issues as one man continually flips heads and the other considers various possible explanations. If external factors are allowed to change the probability of the events, the gambler's fallacy may not hold.

For example, a change in the game rules might favour one player over the other, improving his or her win percentage. Similarly, an inexperienced player's success may decrease after opposing teams learn about and play against their weaknesses. This is another example of bias. The gambler's fallacy arises out of a belief in a law of small numbers , leading to the erroneous belief that small samples must be representative of the larger population.

According to the fallacy, streaks must eventually even out in order to be representative. When people are asked to make up a random-looking sequence of coin tosses, they tend to make sequences where the proportion of heads to tails stays closer to 0. The gambler's fallacy can also be attributed to the mistaken belief that gambling, or even chance itself, is a fair process that can correct itself in the event of streaks, known as the just-world hypothesis.

When a person believes that gambling outcomes are the result of their own skill, they may be more susceptible to the gambler's fallacy because they reject the idea that chance could overcome skill or talent. For events with a high degree of randomness, detecting a bias that will lead to a favorable outcome takes an impractically large amount of time and is very difficult, if not impossible, to do.

Another variety, known as the retrospective gambler's fallacy, occurs when individuals judge that a seemingly rare event must come from a longer sequence than a more common event does. The belief that an imaginary sequence of die rolls is more than three times as long when a set of three sixes is observed as opposed to when there are only two sixes.

This effect can be observed in isolated instances, or even sequentially. Another example would involve hearing that a teenager has unprotected sex and becomes pregnant on a given night, and concluding that she has been engaging in unprotected sex for longer than if we hear she had unprotected sex but did not become pregnant, when the probability of becoming pregnant as a result of each intercourse is independent of the amount of prior intercourse.

Another psychological perspective states that gambler's fallacy can be seen as the counterpart to basketball's hot-hand fallacy , in which people tend to predict the same outcome as the previous event - known as positive recency - resulting in a belief that a high scorer will continue to score.

In the gambler's fallacy, people predict the opposite outcome of the previous event - negative recency - believing that since the roulette wheel has landed on black on the previous six occasions, it is due to land on red the next. Ayton and Fischer have theorized that people display positive recency for the hot-hand fallacy because the fallacy deals with human performance, and that people do not believe that an inanimate object can become "hot.

The difference between the two fallacies is also found in economic decision-making. A study by Huber, Kirchler, and Stockl in examined how the hot hand and the gambler's fallacy are exhibited in the financial market. The researchers gave their participants a choice: they could either bet on the outcome of a series of coin tosses, use an expert opinion to sway their decision, or choose a risk-free alternative instead for a smaller financial reward. The participants also exhibited the gambler's fallacy, with their selection of either heads or tails decreasing after noticing a streak of either outcome.

This experiment helped bolster Ayton and Fischer's theory that people put more faith in human performance than they do in seemingly random processes. While the representativeness heuristic and other cognitive biases are the most commonly cited cause of the gambler's fallacy, research suggests that there may also be a neurological component.

Functional magnetic resonance imaging has shown that after losing a bet or gamble, known as riskloss, the frontoparietal network of the brain is activated, resulting in more risk-taking behavior. In contrast, there is decreased activity in the amygdala , caudate , and ventral striatum after a riskloss. Activation in the amygdala is negatively correlated with gambler's fallacy, so that the more activity exhibited in the amygdala, the less likely an individual is to fall prey to the gambler's fallacy.

These results suggest that gambler's fallacy relies more on the prefrontal cortex, which is responsible for executive, goal-directed processes, and less on the brain areas that control affective decision-making. The desire to continue gambling or betting is controlled by the striatum , which supports a choice-outcome contingency learning method. The striatum processes the errors in prediction and the behavior changes accordingly. After a win, the positive behavior is reinforced and after a loss, the behavior is conditioned to be avoided.

In individuals exhibiting the gambler's fallacy, this choice-outcome contingency method is impaired, and they continue to make risks after a series of losses. The gambler's fallacy is a deep-seated cognitive bias and can be very hard to overcome. Educating individuals about the nature of randomness has not always proven effective in reducing or eliminating any manifestation of the fallacy.

Participants in a study by Beach and Swensson in were shown a shuffled deck of index cards with shapes on them, and were instructed to guess which shape would come next in a sequence. The experimental group of participants was informed about the nature and existence of the gambler's fallacy, and were explicitly instructed not to rely on run dependency to make their guesses. The control group was not given this information.

The response styles of the two groups were similar, indicating that the experimental group still based their choices on the length of the run sequence. This led to the conclusion that instructing individuals about randomness is not sufficient in lessening the gambler's fallacy. An individual's susceptibility to the gambler's fallacy may decrease with age.

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