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
- Chapter 1 The Theoretical Beauty and Fertility of Sampling Approaches
- Chapter 2 Homo Ordinalus and Sampling Models
- Chapter 3 In Decisions from Experience What You See Is Up to Your Sampling of the World
- Chapter 4 The Hot Stove Effect
- 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
- Index
- References
Chapter 4 - The Hot Stove Effect
from Part I - Historical Review of Sampling Perspectives and Major Paradigms
Published online by Cambridge University Press: 01 June 2023
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
- Chapter 1 The Theoretical Beauty and Fertility of Sampling Approaches
- Chapter 2 Homo Ordinalus and Sampling Models
- Chapter 3 In Decisions from Experience What You See Is Up to Your Sampling of the World
- Chapter 4 The Hot Stove Effect
- 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
- Index
- References
Summary
People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) will be less likely to be corrected than errors of overestimation (an alternative is believed to be better than it is). Denrell & March (2001) called this asymmetry in error correction the “Hot Stove Effect.” This chapter explains the basic logic behind the Hot Stove Effect and how this bias can explain a range of judgment biases. We review empirical studies that illustrate how risk aversion and mistrust can be explained by the Hot Stove Effect. We also explain why even a rational algorithm can be subject to the same bias.
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- Sampling in Judgment and Decision Making , pp. 90 - 112Publisher: Cambridge University PressPrint publication year: 2023
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