The impact of inventory risk on market prices under competition
Bibliographic record
Abstract
Abstract Firms often must procure inventory/capacity before knowing what the demand will be, so there is a potential for a mismatch between inventory and demand, the “inventory risk.” We show that, because of inventory risk, an increase in the number of competitors can lead to an increasing trend in market prices. Furthermore, we show that, ceteris paribus, because of how inventory risk impacts competitive behavior, firms may prefer to incur inventory risk rather than to avoid it. To illustrate the robustness of our results, we establish these findings using three complementary methodologies: (i) using data from a classroom experiment, (ii) using a quantal response equilibrium simulation to capture realistic irrationalities in managerial decisions under competition, and (iii) using a fully rational Nash equilibrium model to capture the impact of the competition per se. That all three methods lead to identical qualitative findings reinforces the main message of our paper: Inventory risk reverses the standard intuition for how an increase in the number of competitors impacts prices.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".