Strange case of Dr. Bidder and Mr. Entrant: Consumer preference inconsistencies in costly price offers
Bibliographic record
Abstract
Consumers make price offers to sellers in a variety of domains, such as when buying cars or houses or when bidding in auctions for airline upgrades, art, or collectibles.Submitting an offer typically entails administrative, waiting, and opportunity costs.Making such costly price offers involves two intertwined decisions-in addition to determining how much to offer, consumers must also decide whether to make an offer in the first place.We examine the impact of offer-submission costs on consumer behavior using a series of incentivecompatible experiments.Our findings reveal a preference inconsistency, whereby the preferences implied by one of the decisions do not align with the preferences implied by the other.In particular, potential buyers enter more often than their offer amounts would predict based on standard economic models.This preference inconsistency is robust to two interventions designed to help consumers make offer-amount and entry decisions-(1) the provision of interactive-feedback decision aids and (2) the sequencing of the two sub-decisions in the normative order.Neither of these interventions resolves the inconsistency.Instead, the patterns of results suggest that consumers approach the offer-amount and entry decisions as if they were unrelated.We discuss the implications of our findings for the design of offer-submission interfaces, as well as for econometric attempts to infer consumer preferences from offer and bidding data.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".