Bayesians Commit the Gambler's Fallacy
- 1Department of Linguistics and Philosophy, Massachusetts Institute of Technology.
- 0Department of Linguistics and Philosophy, Massachusetts Institute of Technology.
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View abstract on PubMed
Summary
This summary is machine-generated.The gambler's fallacy, the belief that random events will correct themselves, may not be irrational. It can be a rational response to uncertainty and limited data, even with large datasets.
Area Of Science
- Cognitive Science
- Behavioral Economics
- Probability Theory
Background
- The gambler's fallacy is commonly viewed as a cognitive bias indicating irrationality.
- This fallacy involves expecting random processes to self-correct, such as predicting a different outcome after a streak.
Purpose Of The Study
- To investigate whether the gambler's fallacy can be explained as a rational response.
- To model Bayesian agents with causal uncertainty and limited memory encountering random data.
Main Methods
- Simulating Bayesian agents with varying degrees of causal uncertainty.
- Observing agent responses to statistically independent random processes.
- Analyzing agent predictions after different sequences of outcomes.
Main Results
- Bayesian agents with initial causal uncertainty exhibited the gambler's fallacy.
- Agents ruled out 'streaky' hypotheses faster than 'switchy' ones, leading to asymmetric confidence.
- Limited memory exacerbated the fallacy, persisting even with large amounts of data.
Conclusions
- The gambler's fallacy can arise from rational Bayesian inference under conditions of uncertainty and limited memory.
- Observed empirical trends in gambler's fallacy studies align with these rational, albeit limited, Bayesian responses.
- This suggests that apparent irrationality may stem from adaptive strategies to incomplete information.
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