Misery Loves Company: Social Influence and the Supply/Pricing Decision of Popular Night Clubs
Bibliographic record
Abstract
This paper offers an explanation for why popular night clubs (restaurants) with excess demand (i.e., queues) do not raise prices or increase supply. Becker (1991) uses the social influence of a consumption externality or “bandwagon effect ” to explain this puzzle. However, he admits that his explanation may be weak. In this essay, I present a formal analysis of Becker’s argument based on a different kind of social influence (i.e., misery loves company). I also offer an alternative explanation of why some night clubs (restaurants) are popular and others are not. While Becker (1991) includes market demand and the gap between market demand and supply as separate arguments in the customers ’ demand function to explain why supply and price are not increased, I only include the gap between demand and supply in the customers ’ utility function to explain both puzzles. Although the essay focuses on night clubs (restaurants), it should be seen as a contribution to the broader literature of why some markets apparently do not clear or why some goods are rationed. Key words: cost of failure, excess demand, night clubs, social influence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".