Multicomponent Systems Pricing: Rational Inattention and Downward Rigidities
Bibliographic record
Abstract
The authors examine the relative magnitude of price reductions for product systems and their constituent components (e.g., cameras, computers, monitors, lenses) and hypothesize that these price reductions systematically vary across different types of systems. The authors offer rational inattention as an explanation and document patterns of downward rigidity in online prices of computers and cameras that are consistent with this view. Their basic argument is that under certain circumstances, it is rational for consumers to ignore small price changes. This results in some price rigidity because firms would see no demand effect for small reductions. The authors suggest that this inattention systematically varies across different types of multicomponent systems, leading to specific hypotheses about sellers’ pricing behavior. They first check the validity of their theoretical arguments using data from two surveys of consumers and managers. They then examine 669,557 daily price listings for 1052 high-end cameras and computers from 102 online vendors and find evidence consistent with their predictions. Using publicly available web traffic data, the authors also find that their predicted pricing behavior is aligned with better traffic response for the firm.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".