Book contents
- Sampling in Judgment and Decision Making
- Sampling in Judgment and Decision Making
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Historical Review of Sampling Perspectives and Major Paradigms
- Part II Sampling Mechanisms
- Part III Consequences of Selective Sampling
- Part IV Truncation and Stopping Rules
- Part V Sampling as a Tool in Social Environments
- Part VI Computational Approaches
- Chapter 20 An Introduction to Psychologically Plausible Sampling Schemes for Approximating Bayesian Inference
- Chapter 21 Approximating Bayesian Inference through Internal Sampling
- Chapter 22 Sampling Data, Beliefs, and Actions
- Index
- References
Chapter 22 - Sampling Data, Beliefs, and Actions
from Part VI - Computational Approaches
Published online by Cambridge University Press: 01 June 2023
- Sampling in Judgment and Decision Making
- Sampling in Judgment and Decision Making
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Historical Review of Sampling Perspectives and Major Paradigms
- Part II Sampling Mechanisms
- Part III Consequences of Selective Sampling
- Part IV Truncation and Stopping Rules
- Part V Sampling as a Tool in Social Environments
- Part VI Computational Approaches
- Chapter 20 An Introduction to Psychologically Plausible Sampling Schemes for Approximating Bayesian Inference
- Chapter 21 Approximating Bayesian Inference through Internal Sampling
- Chapter 22 Sampling Data, Beliefs, and Actions
- Index
- References
Summary
Sampling – using a stochastically drawn subset of possibilities – has been at the core of many influential modeling frameworks of human decision making for the last half century. Although these frameworks all refer to their core operation as “sampling,” they differ dramatically in the behaviors and inferences they aim to account for. Here we review this landscape of sampling models under a unified expected utility framework which treats diverse sampling accounts as approximating different terms in the expected utility calculation. We show that a broad range of sample-based models in psychology are built around sampled data, beliefs, or actions and can therefore support downstream expected utility maximization. To compare these models on an even footing, our review focuses on how the number of samples and the sample distribution differ within each element of the expected utility calculation. This integrated summary allows us to identify opportunities for fruitful cross-pollination across sampling domains, and to highlight outstanding challenges for accounts that might aim to integrate these disparate models.
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- Sampling in Judgment and Decision Making , pp. 513 - 548Publisher: Cambridge University PressPrint publication year: 2023