Game‐theoretic approaches to product introduction strategies for durable products
Bibliographic record
Abstract
Abstract New product demand continuously fluctuates throughout the life of the product. As a result, markets typically experience high volatility. Most companies operate in highly competitive industries where the product volume supplied by the competitors significantly affects production plans. These conditions necessitate companies to be flexible and rapidly adapt to such volatility. In this study, we investigate the product introduction strategies under two microeconomic theories: real‐option valuation and game theory. Specifically, we develop a real‐option valuation framework with a flexible capacity based on two game‐theoretic models, namely, Stackelberg and Cournot, in a duopoly market where competitors have either perfect or imperfect information. Furthermore, we construct a lattice to discretize the demand evolution and adopt a regime‐switching approach to characterize the stochastic product lifetime. We conduct an extensive numerical study and compare net present values (NPVs) and optimal capacities obtained from the game‐theoretic models. Our results show that under perfect information, the Stackelberg competition generates more supply with lower prices and less total than the Cournot competition. Moreover, we find that, compared to the fixed‐capacity system, the flexible capacity system increases the in the Stackelberg game (by 5.5%) more than in the Cournot game (by 3.3%). We provide further results on the performance of the companies based on detailed sensitivity analysis for various model parameters. In particular, we observe that fixed and variable production and installation costs majorly impact capacity allocation decisions and profits.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".