The application of rational inattention theory in modelling residential location choices: A cross-sectional investigation using a stated preference dataset
Bibliographic record
Abstract
The rational inattention theory aims to evaluate instances in which a decision is made in an information-rich environment where consumers cannot process all information due to limited cognitive capacity. In contrast to classical random utility-maximizing models, rational inattention discrete choice models do not assume that decision-makers make choices with complete knowledge of the alternatives. Today’s information technology tools create a decision-making environment in which information is plentiful and easily accessible. Yet, it is cognitively impossible for households to be aware of every aspect of available options. This study uses rational inattention theory to investigate residential location choices in the Greater Toronto Area (GTA) during the COVID-19 pandemic, using an efficient-adaptive stated preference dataset collected in July 2021. The rational inattention theory requires identifying information processing costs and marginal probabilities as decision-makers’ prior beliefs. The empirical model of this paper proposes using the time respondents spend on choice problems to measure their attention span and the latent preferences produced from the efficient-adaptive survey to measure their prior beliefs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".