The Hot Hand in Basketball: On the Misperception of Random Sequences

Gilovich, Thomas; Vallone, Robert P.; Tversky, Amos

doi:10.1017/cbo9780511808098.035

Cited by 229 publications

(396 citation statements)

References 1,299 publications

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“…People are also notoriously incompetent in interpreting outcomes that involve randomness. For example, both the 'gambler's fallacy' and the 'hot-hand fallacy' suggest that people hold illusory beliefs that a random sequence entails a pattern (Ayton & Fischer, 2004;Gilovich, Vallone, & Tversky, 1985;Tversky & Kahneman, 1974). Moreover, instead of seeing luck as an external, random element, people often interpret luck as a personal characteristic (Darke & Freedman, 1997;Maltby, Day, Gill, Colley, & Wood, 2008), and believing it can be manipulated (Tsang, 2004a(Tsang, , 2004b.…”

Section: Luck As Attributionmentioning

confidence: 99%

Good Night, and Good Luck: Perspectives on Luck in Management Scholarship

Liu

Rond

2016

The Academy of Management Annals

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It is not insignificant that seminal contributions to management scholarship have highlighted luck as an alternative explanation for performance differences between individuals and organizations. Yet it has rarely taken center-stage in scholarship. The principal purpose of this paper is to provide a systematic review of the application of luck in the management literature and in such foundation disciplines as economics, sociology, and psychology. Our analysis finds five common perspectives on luck: (a) luck as Attribution; (b) luck as Randomness; (c) luck as Counterfactual; (d) luck as Undeserved; and (e) luck as Serendipity. We outline various ways in which research on luck may be advanced along each of these perspectives, and develop an underexplored, sixth, perspective on (f) 'luck as Leveler' to provide a possible solution to such issues as social inequality and (unwarranted) executive compensation.

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Section: Luck As Attributionmentioning

confidence: 99%

Good Night, and Good Luck: Perspectives on Luck in Management Scholarship

Liu

Rond

2016

The Academy of Management Annals

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“…A number of studies have cast doubt on people's abilities to comprehend randomness, including such wellknown phenomena as the gambler's fallacy, the hot-hand fallacy (Gilovich, Vallone, & Tversky, 1985), and the law of small numbers (Tversky & Kahneman, 1971). In experiments 1-3, as part of clarifying the causal structure of the scenario, we moved the main source of randomness from the efficacy of the mechanism to the presence or absence of other causal variables.…”

Section: Deterministic Mechanisms and Randomly Occurring Causesmentioning

confidence: 99%

The role of causality in judgment under uncertainty.

Krynski

Tenenbaum

2007

Journal of Experimental Psychology: General

170

195

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Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (Tversky & Kahneman, 1974) or frequentist (Gigerenzer & Hoffrage, 1995) norms. We argue that these frameworks have limited ability to explain the success and flexibility of people's real-world judgments, and propose an alternative normative framework based on Bayesian inferences over causal models. Deviations from traditional norms of judgment, such as "base-rate neglect", may then be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Four experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms. Causality in Judgment 3The Role of Causality in Judgment Under Uncertainty Everywhere in life, people are faced with situations that require intuitive judgments of probability. How likely is it that this person is trustworthy? That this meeting will end on time?That this pain in my side is a sign of a serious disease? Survival and success in the world depend on making judgments that are as accurate as possible given the limited amount of information that is often available. To explain how people make judgments under uncertainty, researchers typically invoke a computational framework to clarify the kinds of inputs, computations, and outputs that they expect people to use during judgment. We can view human judgments as approximations (sometimes better, sometimes worse) to modes of reasoning within a rational computational framework, where a computation is "rational" to the extent that it provides adaptive value in real-world tasks and environments. However, there is more than one rational framework for judgment under uncertainty, and behavior that looks irrational under one framework may look rational under a different framework. Because of this, evidence of "errorprone" behavior as judged by one framework may alternatively be viewed as evidence that a different rational framework is appropriate. This paper considers the question of which computational framework best explains people's judgments under uncertainty. To answer this, we must consider what kinds of realworld tasks and environments people encounter, which frameworks are best suited to these environments (i.e., which we should take to be normative), and how well these frameworks predict people's actual judgments under uncertainty (i.e., which framework offers the best descriptive model). We will propose that a causal Bayesian framework, in which Bayesian inferences are made over causal models, represents a more appropriate normative standard and a more accurate descriptive model than previous frameworks for judgme...

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“…It is well documented in the literature that subjects' subjective perceptions of chance can be influenced by sequences or patterns of outcomes they observe (see, for example, Rabin 2002). Like the tendency of basketball spectators to overstate the degree to which players are streak shooters (Gilovich et al 1985;Wardrop 1995;Aharoni and Sarig 2008), subjects in our WRIS treatment who avoided the elimination of large prizes in a previous game may be too confident about their chances of avoiding the elimination of large prizes in the current game.…”

Section: Background and Motivationmentioning

confidence: 97%

Random incentive systems in a dynamic choice experiment

et al. 2011

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Experiments frequently use a random incentive system (RIS), where only tasks that are randomly selected at the end of the experiment are for real. The most common type pays every subject one out of her multiple tasks (within-subjects randomization). Recently, another type has become popular, where a subset of subjects is randomly selected, and only these subjects receive one real payment (betweensubjects randomization). In earlier tests with simple, static tasks, RISs performed well. The present study investigates RISs in a more complex, dynamic choice experiment. We find that between-subjects randomization reduces risk aversion. While within-subjects randomization delivers unbiased measurements of risk aversion, it does not eliminate carry-over effects from previous tasks. Both types generate an increase in subjects' error rates. These results suggest that caution is warranted when applying RISs to more complex and dynamic tasks.

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The Hot Hand in Basketball: On the Misperception of Random Sequences

Cited by 229 publications

References 1,299 publications

Good Night, and Good Luck: Perspectives on Luck in Management Scholarship

Good Night, and Good Luck: Perspectives on Luck in Management Scholarship

The role of causality in judgment under uncertainty.

Random incentive systems in a dynamic choice experiment

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