On Markov Equilibria in Dynamic Inventory Competition
Bibliographic record
Abstract
We provide a review of the types of equilibria typically found in operations management inventory papers and a discussion on when the commonly used stationary infinite-horizon (open-loop) equilibrium may be sufficient for study. We focus particularly on order-up-to and basestock equilibria in the context of inventory duopolies. We give conditions under which the stationary infinite-horizon equilibrium is also a Markov perfect (closed-loop) equilibrium. These conditions are then applied to three specific duopolies. The first application is one with stockout-based substitution, where the firms face independent direct demand but some fraction of a firm's lost sales will switch to the other firm. The second application is one where shelf-space display stimulates primary demand and reduces demand for the other firm's product. The final application is one where the state variables represent goodwill rather than inventory. These specific problems have been previously studied in both the single period and/or stationary infinite-horizon (open-loop) settings but not in Markov perfect (closed-loop) settings. Under the Markov perfect setting, a variety of interesting dynamics may occur, including that there may be a so-called commitment value to inventory.
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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 | 0.007 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".