User:Songm/sandbox
I will be experimenting with my edits for the Gambler's fallacy article below. I plan on dividing the "Psychology of the fallacy" section up into a number of subsections:
Psychology behind the fallacy
Origins
Gambler's fallacy arises out of a belief in the law of small numbers, or 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. [1] Amos Tversky and Daniel Kahneman first proposed that the gambler's fallacy is a cognitive bias produced by a psychological heuristic called the representativeness heuristic, which states that people evaluate the probability of a certain event by assessing how similar it is to events they have experienced before, and how similar the events surrounding those two processes are.[2][3] According to this view, "after observing a long run of red on the roulette wheel, for example, most people erroneously believe that black will result in a more representative sequence than the occurrence of an additional red",[4] so people expect that a short run of random outcomes should share properties of a longer run, specifically in that deviations from average should balance out. 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 0.5 in any short segment than would be predicted by chance (insensitivity to sample size);[5] Kahneman and Tversky interpret this to mean that people believe short sequences of random events should be representative of longer ones.[6] The representativeness heuristic is also cited behind the related phenomenon of the clustering illusion, according to which people see streaks of random events as being non-random when such streaks are actually much more likely to occur in small samples than people expect.[7]
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, otherwise known as the just-world hypothesis. [8] Other researchers believe that individuals with an internal locus of control - that is, people who believe that the gambling outcomes are the result of their own skill - are more susceptible to the gambler's fallacy because they reject the idea that chance could overcome skill or talent. [9]
Type II gambler's fallacy
Some researchers believe that there are actually two types of gambler's fallacy: Type I and Type II. Type I is the "classic" gambler's fallacy, when individuals believe that a certain outcome is "due" after a long streak of another outcome. Type II gambler's fallacy, as defined by Gideon Keren and Charles Lewis, occurs when a gambler underestimates how many observations are needed to detect a favorable outcome (such as watching a roulette wheel for a length of time and then betting on the numbers that appear most often). Detecting a favorable outcome is very difficult, if not impossible, to do and therefore people fall prey to the Type II gambler's fallacy. [10]
Relationship to hot-hand fallacy
Another psychological perspective states that gambler's fallacy can be seen as the counterpart to basketball's Hot-hand fallacy. In the hot-hand fallacy, people tend to predict the same outcome of the last event (positive recency) - that a high scorer will continue to score. In gambler's fallacy, however, people predict the opposite outcome of the last event (negative recency) - that, for example, since the roulette wheel has landed on black the last six times, 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." [11] Human performance is not perceived as "random," and people are more likely to continue streaks when they believe that the process generating the results is nonrandom. [12] Usually, when a person exhibits the gambler's fallacy, they are more likely to exhibit the hot-hand fallacy as well. [13]
Neurophysiology
While the representativeness heuristic and other cognitive biases are the most commonly cited cause of the gambler's fallacy, research suggests that there may be a neurological component to it as well. Functional magnetic resonance imaging has revealed that, after losing a bet or gamble ("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 - 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 (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.[14]
Possible solutions
Educating individuals about the nature of randomness has not proven effective in reducing or eliminating the gambler's fallacy. One possible solution comes from Roney and Trick, Gestalt psychologists who suggest that the fallacy may be eliminated as a result of grouping. When a future event (ex: a coin toss) is described as part of a sequence, no matter how arbitrarily, a person will automatically consider the event as it relates to the past events, resulting in the gambler's fallacy. When a person considers every event as independent, however, the fallacy is greatly reduced. [15]
References
- ^ Burns, B.D. and Corpus, B. (2004). Randomness and inductions from streaks: "Gambler's fallacy" versus "hot hand." Psychonomic Bulletin and Review. 11, 179-184
- ^ Tversky, Amos (1974). "Judgment under uncertainty: Heuristics and biases". Science. 185 (4157): 1124–1131. doi:10.1126/science.185.4157.1124. PMID 17835457.
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- ^ Tune, G.S. (1964). "Response preferences: A review of some relevant literature". Psychological Bulletin. 61 (4): 286–302. doi:10.1037/h0048618. PMID 14140335.
- ^ Tversky & Kahneman, 1971.
- ^ Gilovich, Thomas (1991). How we know what isn't so. New York: The Free Press. pp. 16–19. ISBN 0-02-911706-2.
- ^ Rogers, P. (1998). The cognitive psychology of lottery gambling: A theoretical review. Journal of Gambling Studies, 14, 111-134
- ^ Sundali, J. and Croson, R. (2006). Biases in casino betting: The hot hand and the gambler's fallacy. Judgment and Decision Making, 1, 1-12.
- ^ Keren, G. and Lewis, C. (1994). The two fallacies of gamblers: Type I and Type II. Organizational Behavior and Human Decision Processes, 60, 75-89.
- ^ Ayton, P. & Fischer, I. (2004). The hot hand fallacy and the gambler's fallacy: Two faces of subjective randomness? Memory and Cognition, 32, 1369-1378.
- ^ Burns, B.D. and Corpus, B. (2004). Randomness and inductions from streaks: "Gambler's fallacy" versus "hot hand." Psychonomic Bulletin and Review. 11, 179-184
- ^ Sundali, J. and Croson, R. (2006). Biases in casino betting: The hot hand and the gambler's fallacy. Judgment and Decision Making, 1, 1-12.
- ^ Xue, G., Lu, Z., Levin, I.P., and Bechara, A. (2011). An fMRI study of risk-taking following wins and losses: Implications for the gambler's fallacy. Human Brain Mapping, 32, 271-281.
- ^ Roney, C.J. and Trick, L.M. (2003). Grouping and gambling: A gestalt approach to understanding the gambler's fallacy. Canadian Journal of Experimental Psychology, 57, 69-75.