Real options valuation with duopoly game-theoretic approach and regime switching
Bibliographic record
Abstract
New product demand continuously fluctuates through the life of the product. As a result, markets usually experience high volatility due to fluctuating demand. Hence, organizations must rapidly adapt and be flexible with such volatility. Management decisions cannot rely on traditional models that assume economic variables to be constant over time. Therefore, companies must advance and implement adequate strategies in resource allocation that help them survive in uncertain markets, and mitigate the associated risk from market uncertainty. This research aims to investigate product introduction strategies where two micro-economic theories can be applied: game-theoretic approach and a real options valuation. Specifically, a real options valuation framework with a flexible capacity that can be used to calculate the Net Present Value (NPV) and the optimal initial capacity to invest in a product, which is being introduced in a duopoly environment, is proposed. This framework is based on game theory applied to a duopoly market. A stochastic product life cycle characterized by a regime-switching approach and the consideration of two different game-theoretic models for the cases where competitors have perfect or imperfect information in the market are the main focal points of our study. Subsequently, different numerical examples comparing the results of NPV and optimal initial capacities from both game-theoretic models are presented. It is observed that the leader in the model with perfect information has an important advantage over its opponent because this company makes the first move to establish quantity to supply. Also, the leader in perfect information game has an advantage over the game with imperfect information since decisions with imperfect information have to be made simultaneously, and players need to balance all possible outcomes when making a choice. Consequently, in both models, one of the competitors obtains its maximum NPV when the other invests in the lowest level of capacity. Lastly, expansion and contraction costs play a critical role in the strategy of resource allocation as these costs are involved in capacity flexibility. Therefore, firms can adjust their production when product demand varies to maximize 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".