UID:
almafu_9961119058902883
Format:
1 online resource (xx, 551 pages) :
,
digital, PDF file(s).
Edition:
First edition.
ISBN:
9781009007481
,
1009007483
,
9781009008259
,
1009008250
,
9781009002042
,
100900204X
Content:
Sampling approaches to judgment and decision making are distinct from traditional accounts in psychology and neuroscience. While these traditional accounts focus on limitations of the human mind as a major source of bounded rationality, the sampling approach originates in a broader cognitive-ecological perspective. It starts from the fundamental assumption that in order to understand intra-psychic cognitive processes one first has to understand the distributions of, and the biases built into, the environmental information that provides input to all cognitive processes. Both the biases and restriction, but also the assets and capacities, of the human mind often reflect, to a considerable degree, the irrational and rational features of the information environment and its manifestations in the literature, the Internet, and collective memory. Sampling approaches to judgment and decision making constitute a prime example of theory-driven research that promises to help behavioral scientists cope with the challenges of replicability and practical usefulness.
Note:
Includes index.
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Cover -- Half-title -- Title page -- Copyright information -- Contents -- List of Figures -- List of Tables -- List of Contributors -- Part I Historical Review of Sampling Perspectives and Major Paradigms -- Chapter 1 The Theoretical Beauty and Fertility of Sampling Approaches: A Historical and Meta-Theoretical Review -- 1.1 Introduction -- 1.2 Historical Review of Origins and Underpinnings of Sampling Approaches -- 1.2.1 Methodological and Meta-Theoretical Assets -- 1.2.1.1 Recording the Sampled Input -- 1.2.1.2 Impact of Sampling Constraints -- 1.2.2 Properties of Proximal Samples -- 1.2.2.1 Theoretical Progress and Explanatory Power -- 1.2.3 Strategies of Information Search -- 1.2.4 Specifying Computational Assumptions -- 1.3 New Developments in Recent Sampling Research -- 1.3.1 Conceptual Challenges -- 1.3.1.1 The Limiting Conditions on ''Metacognitive Myopia'' -- 1.3.1.2 Beyond the Idealizations of Probability -- 1.3.2 One, Two Decades Later . . . -- 1.3.3 Preview of all Chapters of the Present Volume -- 1.3.4 Outlook . . . Two More Decades Later -- References -- Chapter 2 Homo Ordinalus and Sampling Models: The Past, Present, and Future of Decision by Sampling -- 2.1 Introduction -- 2.1.1 DbS and Representative Samples -- 2.1.2 DbS and Biased Samples -- 2.2 History and Relation to Other Approaches -- 2.2.1 Absolute Magnitudes and Values -- 2.2.2 Reference-Level (Mean-Based) Models -- 2.2.3 Range-Based Models -- 2.2.4 Range Frequency Theory -- 2.3 Range Effects from Rank-Based Models -- 2.3.1 Distinctiveness -- 2.3.2 Inferred Distributions -- 2.3.3 Efficient Coding -- 2.4 Why Rank? -- 2.4.1 Rank as a Goal -- 2.4.2 Rank Coding as a Response to the Incommensurability of Value -- 2.4.3 Rank Coding as a Tool for Market Inference -- 2.4.4 Rank as Efficient Coding -- 2.4.4.1 Information-Theoretic Approaches -- 2.4.4.2 Neuroscience Approaches.
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2.5 Theoretical Implications, Applications, and Future Directions -- 2.5.1 Theoretical Issues -- 2.5.2 Developments and Future Directions -- 2.6 Conclusion -- References -- Chapter 3 In Decisions from Experience What You See Is Up to Your Sampling of the World -- 3.1 How Do People's Actions Shape What They See? -- 3.1.1 People Take Small Samples -- 3.1.2 People Learn Adaptively -- 3.1.3 People Engage in Similarity-Based Sampling -- 3.2 How Does What People See Shape How They Act? -- 3.2.1 The Mere Presentation of an Outcome Impacts Choices -- 3.2.2 The Saliency of Experienced Outcomes Shapes Actions -- 3.2.3 The Explanatory Power of Studying the Dynamic Interplay between Perceiving and Acting -- 3.2.4 Experiencing Macroeconomic Shocks and Risk-Taking in the Stock Market -- 3.2.5 Measuring Risk Preferences in Adolescence through the Act of Sampling -- 3.2.6 Experimenting with a 'Descriptive' or 'Experiential' Methodology and Human Competence and Rationality -- 3.3 Conclusion -- References -- Chapter 4 The Hot Stove Effect -- 4.1 Introduction -- 4.2 The Basic Logic behind the Hot Stove Effect -- 4.2.1 Negativity Bias Resulting from Avoidance -- 4.2.2 A Multi-period Learning Model -- 4.2.3 Changes in Sample Size -- 4.2.4 Even Bayesian Algorithms are Subject to the Hot Stove Effect -- 4.2.5 When the Hot Stove Effect Does Not Occur -- 4.3 Empirical Evidence for a Hot Stove Effect in Human Behavior -- 4.4 Impact on Risk Taking -- 4.4.1 Evidence on the Impact of the Hot Stove Effect on Risk Taking -- 4.4.2 Myopic Loss Aversion -- 4.5 Implications and Applications -- 4.5.1 Trust -- 4.5.2 Negotiation -- 4.6 Behavioral Nuances That Impact the Hot Stove Effect -- 4.6.1 Recency, Primacy, and Pattern Identification -- 4.6.2 Impact of Descriptions -- 4.7 Future Prospects: Generalization -- References -- Part II Sampling Mechanisms.
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Chapter 5 The J/DM Separation Paradox and the Reliance on the Small Samples Hypothesis -- 5.1 The Mere Presentation Effect -- 5.1.1 The Sampling Explanation of the Mere Presentation Effect -- 5.2 Reliance on Small Samples -- 5.2.1 Opportunity Cost -- 5.2.2 The Amplification Effect -- 5.2.3 The Hot Stove Effect -- 5.2.4 Reaction to Patterns -- 5.3 The Impact of Feedback -- 5.4 Implications for Descriptive Models -- 5.5 Summary -- References -- Chapter 6 Sampling as Preparedness in Evaluative Learning -- 6.1 Traditional Conceptualization of Preparedness -- 6.1.1 A Constructivist Perspective on Preparedness -- 6.2 Introduction of Sampling to Evaluative Learning -- 6.2.1 Methodology -- 6.2.2 Results -- 6.3 Insights Gained From the Sampling Approach -- 6.4 Sampling as Preparedness in Evaluative Conditioning -- 6.5 Conclusion and Outlook -- References -- Chapter 7 The Dog that Didn't Bark: Bayesian Approaches to Reasoning from Censored Data -- 7.1 The Role of Sampling Assumptions in Property Induction -- 7.1.1 Inductive Inference as Social-Cognition: Assumptions about Intentionality -- 7.1.2 Sampling Frames: When Is Absence of Evidence, Evidence of Absence? -- 7.1.3 A Bayesian Model of Inference with Biased Samples -- 7.2 Testing the Boundaries of the Bayesian Approach -- 7.2.1 Inferences in More Complex Sampling Environments -- 7.2.2 Sensitivity to Sampling Frames: When and Who? -- 7.3 Future Directions -- 7.4 Conclusions -- References -- Chapter 8 Unpacking Intuitive and Analytic Memory Sampling in Multiple-Cue Judgment -- 8.1 Introduction -- 8.2 Identification of the Cognitive Processes in Multiple-Cue Judgment -- 8.3 Returning to Brunswik: The PNP Model -- 8.4 Combining Multiple-Cue Integration and the PNP Model -- 8.5 Analytic and Intuitive Sampling from Memory -- 8.6 Identification of the Sampling Mechanisms from Judgment Data -- 8.6.1 Method.
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8.6.1.1 Participants -- 8.6.1.2 Design and Material -- 8.6.1.3 Procedure -- 8.6.1.4 Cognitive Modeling -- 8.6.2 Results -- 8.6.3 Were the Processes Intuitive or Analytic? -- 8.7 Discussion -- References -- Part III Consequences of Selective Sampling -- Chapter 9 Biased Preferences through Exploitation -- 9.1 Exploration or Exploitation? -- 9.1.1 Initial Biases -- 9.1.2 Environments -- 9.2 Persisting Biases -- 9.2.1 Persisting Biases When Choice Alternatives Are Equal -- 9.2.2 Backup from Learning Models -- 9.2.3 Persisting Biases Favoring an Inferior Alternative -- 9.3 How Our Experiences Shape the World We Perceive -- 9.3.1 Satisficing or Maximizing -- 9.3.2 Exploration and Exploitation on a Continuum -- 9.3.3 An Iterative Model of Decision Making -- References -- Chapter 10 Evaluative Consequences of Sampling Distinct Information -- 10.1 Evaluative Consequences of Sampling Distinct Information -- 10.2 Distinct Information -- 10.2.1 Priority of Distinct Information -- 10.2.2 Learning -- 10.2.3 Category Formation -- 10.2.4 Choice and Attitude Formation -- 10.2.5 Communication -- 10.2.6 News Reporting -- 10.2.7 Distinct Information Is Usually Negative -- 10.2.8 Frequency -- 10.2.9 Diversity -- 10.3 The Range Principle -- 10.3.1 Distinct and Redundant Features -- 10.3.2 Biased by Samples of Distinct Information -- 10.3.3 Novelty and Familiarity -- 10.3.4 The Need to Differentiate -- 10.3.5 The Media -- 10.4 An Alternative to Motivational Explanations -- 10.4.1 Hedonic Sampling and Sampling Distinct Information -- 10.5 Conclusion -- References -- Chapter 11 Information Sampling in Contingency Learning: Sampling Strategies and Their Consequences for (Pseudo-)Contingency Inferences -- 11.1 Introduction -- 11.1.1 Contingency Assessment -- 11.1.2 Pseudocontingency Inference -- 11.2 Information Sampling Strategies.
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11.3 Contingency Learning Based on Sampled Information -- 11.3.1 Sampling Individual Co-occurrences and Genuine Contingency Assessment -- 11.3.2 Sampling at the ''Wrong'' Level of Aggregation -- 11.4 Effects of Prior Information -- 11.5 Discussion -- References -- Chapter 12 The Collective Hot Stove Effect -- 12.1 Introduction -- 12.2 Modeling the Collective Hot Stove Effect -- 12.2.1 Differences between Models of the Individual and Collective Hot Stove Effects -- 12.2.1.1 Locus of Valuations -- 12.2.1.2 Who Obtains the Information Samples Used for Valuation Updating -- 12.2.1.3 Valuation Updating Mechanism -- 12.2.2 Valuation Patterns Predicted by the Model -- 12.2.2.1 Collective Hot Stove Effect and Negative Bias in Collective Valuations -- 12.2.2.2 Spurious Association between Number of Ratings and Collective Valuation -- 12.2.2.3 Ranking Mistakes -- 12.2.3 Discussion -- 12.3 A Collective Hot Stove Experiment -- 12.3.1 Design -- 12.3.1.1 Manipulating the Effect of Collective Valuation on the Occurrence of New Ratings -- 12.3.1.2 Estimating the ''True'' Quality of Options -- 12.3.2 Participants -- 12.3.3 Manipulation Check -- 12.3.4 Results -- 12.4 Evidence from Review Websites: Items with Higher Average Ratings Attract More Additional Ratings -- 12.4.1 Analytical Approach -- 12.4.1.1 Modeling the Arrival Rate of Rating Instances -- 12.4.2 Results -- 12.5 Causal Evidence for the Effect of Collective Valuation on the Occurrence of New Ratings -- 12.5.1 Data -- 12.5.2 Analytical Approach: Effect of Average Ratings on Occurrence of Subsequent Ratings -- 12.5.3 Results: Effect of Average Ratings on Occurrence of Subsequent Ratings -- 12.5.4 Analytical Approach: Relation between Number of Ratings and Biases in Average Ratings -- 12.5.5 Results: Relation between Number of Ratings and Biases in Average Ratings -- 12.5.6 Discussion.
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12.6 Discussion and Conclusion.
Additional Edition:
ISBN 9781009009867
Additional Edition:
ISBN 1009009869
Additional Edition:
ISBN 9781316518656
Additional Edition:
ISBN 1316518655
Language:
English
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