One and Done? Optimal Decisions From Very Few Samples
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Tenenbaum_One and done.pdf
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Author(s)
Vul, Edward
Goodman, Noah
Griffiths, Thomas L.
Tenenbaum, Joshua B.
Date Issued
January 2014
Journal
Cognitive Science
Publisher
Wiley Blackwell
Citation
Vul, Edward, Noah Goodman, Thomas L. Griffiths, and Joshua B. Tenenbaum. “One and Done? Optimal Decisions From Very Few Samples.” Cogn Sci 38, no. 4 (January 28, 2014): 599–637.
Version
Original manuscript
Abstract
In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1111/cogs.12101
