More or Less: A Model and Empirical Evidence on Preferences for Under- and Overpayment in Trade-In Transactions
Bibliographic record
Abstract
Trade-in transactions typically involve an exchange of an old, used version for a new or newer version of the product. When consumers trade in their used model for a new model, the firm faces the choice of paying the consumer a relatively low price for the used model and charging a commensurately low price for the new model or paying a relatively high price for the used model and charging a commensurately high price for the new model. The extant literature suggests that consumers always prefer to be overpaid in trade-in transactions because they disproportionately value the gain associated with the revenues from the sale of the used version of the product. The authors draw from the prospect theory value function to develop a simple analytical model that identifies a condition under which this preference for overpayment is reversed. Their model predicts that even when faced with economically equivalent price formats, consumers prefer to be overpaid when the ratio of the price of their used product to the price of the new product is low, but when that ratio is high, the preference for overpayment is reversed. They observe support for the predictions that emerge from the model in laboratory experiments.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".