Causal Reasoning Under Ambiguity
An Illustration of Modeling Mixture Strategies
Bibliographic Data
| ID | 12166166 |
|---|---|
| Authors | Yiyun Shou (0000-0002-7386-0031, Research School of Psychology The Australian National University Canberra Australia, corresponding author), Michael Smithson (0000-0003-4455-2192, Research School of Psychology The Australian National University Canberra Australia) |
| Year | 2016 |
| Volume | 31 |
| Issue | 2 |
| Pages | 219-232 |
| Publication date | 2016-03-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Behavioral Decision Making (JOURNAL) |
| Journal identifiers | ISSN: 0894-3257 • E-ISSN: 1099-0771 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/bdm.1948 |
| OpenAlex | W2303367497 |
| Language | EN |
| Citations received | 1 |
| References cited | 47 |
Causal reasoning with ambiguous observations requires subjects to estimate and evaluate the ambiguous observations. Detecting how people process ambiguous observations can be complicated by individual differences in causal reasoning. This paper proposes a hierarchical model that accounts for the uncertainty in both the distribution of the functional form selection and the distribution of the ambiguity treatment selection. The model provides an alternative to self‐report measures for identifying subjects' strategic choices in reasoning about causal relationships under ambiguity. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. Our model is tested in a simulation study where it demonstrates its ability to recover the strategies and functional forms adopted by simulated subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study. Copyright © 2016 John Wiley & Sons, Ltd
Ambiguity · Causal model · Cognitive psychology · Econometrics · Machine learning · Process (computing · Range (aeronautics · Selection (genetic algorithm · Semantic reasoner · Statistics · Bayesian Modeling and Causal Inference · Child and Animal Learning Development · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Psychology · Artificial Intelligence
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Task complexity and contingent processing in decision making
Ambiguity and uncertainty in probabilistic inference.
Ambiguity aversion in the long run
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Individual differences and strategy selection in reasoning
Contingent decision behavior
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,13 |
| Citation span | 2018 - 2018 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |