Keep Your Options Open: Extreme Programming and the Economics of Flexibility
Bibliographic record
Abstract
Financial evaluation and strategic analysis have long been considered two distinct approaches to evaluating new capital initiatives. An emerging valuation approach, known as real options, attempts to align finance and strategy through a new perspective: The value of an asset lies not only in the amount of direct revenues that it is expected to generate, but also in the options that it creates for flexible decision making in the future. In general, the more uncertain the future is, the higher the value of flexibility embedded in an asset, whether financial or real. This perspective has significant implications for the economics of flexible processes. Applied to software development, it could imply that a lightweight process that is well positioned to respond to change and future opportunities creates more value than a heavy-duty process that tends to freeze development decisions early. Thus, the feasibility of Extreme Programming (XP) can be supported by the option value of flexibility inherent in it. What is the theory that underlies this statement? How does it relate to the fundamental assumptions of XP? How does it impact the value of an XP project? What are the implications of such value propositions for project decisions? If you are curious, read on …
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".