
Contents
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FRAMEWORK AND MODELS FRAMEWORK AND MODELS
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THEORETICAL DEVELOPMENT THEORETICAL DEVELOPMENT
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The SV Model The SV Model
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Loss Functions Loss Functions
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EXPLORING EFFECTS OF DIFFERENT ENVIRONMENTS EXPLORING EFFECTS OF DIFFERENT ENVIRONMENTS
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Constructed and Simulated Environments: Methodology Constructed and Simulated Environments: Methodology
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Relative Model Performance: Expected Percentage Correct and Expected Losses Relative Model Performance: Expected Percentage Correct and Expected Losses
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The Effect of Human Error in Heuristics The Effect of Human Error in Heuristics
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Agreement Between Models Agreement Between Models
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Relative Model Performance: A Summary Relative Model Performance: A Summary
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Comparisons with Experimental Data Comparisons with Experimental Data
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SUMMARY SUMMARY
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GENERAL DISCUSSION GENERAL DISCUSSION
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ACKNOWLEDGMENTS ACKNOWLEDGMENTS
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NOTES NOTES
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APPENDIX A APPENDIX A
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The Expected Accuracies of LC, EW, CONF, and TTB The Expected Accuracies of LC, EW, CONF, and TTB
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The LC Model The LC Model
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The EW Model The EW Model
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The CONF Model The CONF Model
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The TTB Model The TTB Model
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APPENDIX B APPENDIX B
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The Expected Loss of the CONF and Take-the-Best (TTB) Models The Expected Loss of the CONF and Take-the-Best (TTB) Models
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APPENDIX C APPENDIX C
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Selected Three-Cue Studies Selected Three-Cue Studies
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APPENDIX D APPENDIX D
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Selected Two-Cue Studies Selected Two-Cue Studies
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23 On the Psychology of the Recognition Heuristic: Retrieval Primacy as a Key Determinant of Its Use
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13 Heuristic and Linear Models of Judgment: Matching Rules and Environments
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Published:April 2011
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Abstract
Much research has highlighted incoherent implications of judgmental heuristics, yet other findings have demonstrated high correspondence between predictions and outcomes. At the same time, judgment has been well modeled in the form of as if linear models. Accepting the probabilistic nature of the environment, the chapter uses statistical tools to model how the performance of heuristic rules varies as a function of environmental characteristics. They further characterize the human use of linear models by exploring effects of different levels of cognitive ability. The chapter illustrates with both theoretical analyses and simulations. Results are linked to the empirical literature by a meta-analysis of lens model studies. Using the same tasks, the chapter gives an estimation of the performance of both heuristics and humans where the latter are assumed to use linear models. The results emphasize that judgmental accuracy depends on matching characteristics of rules and environments and highlight the trade-off between using linear models and heuristics. Whereas the former can be cognitively demanding, the latter are simple to implement. However, heuristics require knowledge to indicate when they should be used.
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