Option Games The Key to Competing in Capital-Intensive Industries
Bibliographic record
Abstract
Reprint: R0903H All companies making big-budget investment decisions face the same basic dilemma: On the one hand, they must make timely, strategic investments to prevent rivals from gaining ground. On the other, they must avoid tying up too much cash in risky projects, especially during times of market uncertainty. The traditional valuation methods—namely, discounted cash flow and real options—fall short in resolving this dilemma. Neither one, on its own, properly incorporates the impact of demand and price volatility in an industry while also taking into account additional investments that the firm and its competitors may make. In this article, Nelson Ferreira, an associate principal at McKinsey & Company in São Paulo; Jayanti Kar, an associate at McKinsey & Company in Toronto; and Lenos Trigeorgis, a professor of finance at the University of Cyprus and the president of the Real Options Group, present a valuation tool that overcomes the shortfalls of those analytic approaches. The tool, called option games , combines real options (which predict the evolution of prices and demand) and game theory (which captures competitors’ moves) to quantify the value of both flexibility and commitment, allowing managers to make rational choices between alternative investment strategies. Option games will be of particular value to companies facing high-stakes decisions, such as those involving millions of dollars in capital investment, in a volatile environment in which their moves and those of their competitors clearly affect each other.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".