An Empirical Specification of Rational Inattention Multinomial Logit (RI- MNL) Model for Panel Datasets
Bibliographic record
Abstract
Abstract Classical discrete choice models are based on Random Utility Maximization (RUM), which assumes that decision makers have complete information on all of the attributes of their choices. Nowadays, this assumption is becoming less relevant with the expansion of the internet and social media, which provide a universal platform that grants anyone access to an endless amount of information. As a result, decision-making environments are transforming into settings in which individuals can be overwhelmed with information sources competing for their attention, causing them to become less attentive to the alternatives in their choice set. In such cases, individuals find themselves in scenarios where they wish to maximize utility but are uncertain about the payoffs associated with each action. There have been recent theoretical advancements in the study of the behaviour of inattentive decision makers in discrete choice contexts with the introduction of the Rational Inattention Multinomial Logit (RI-MNL) model. This paper proposes a closed-form empirical specification in the case of panel choice datasets to supplement the theoretical foundation of the (RI-MNL) model. Following this proposal is a sample application of the proposed specification using data from a panel survey on residential location preferences for the Greater Toronto Area in 2020 and 2021. This paper illustrates how the findings of the empirical model can be interpreted in the context of rational inattention theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".