Business Opportunity Assessment With Costly, Imperfect Information
Bibliographic record
Abstract
An increasing number of decision makers are required to assess business opportunities, as new technologies have been developing at an astonishing rate and technology transfer has been facilitated in numerous institutions. We present a partially observed Markov decision process model of the assessment. At each point in time, a reward-maximizing decision maker chooses to either accept the opportunity, reject it, or gather costly, imperfect information and update her belief on the status of the opportunity. The resulting optimal policy balances the costs and benefits of decision accuracy, information gathering, and the window of opportunity closing. We show that the optimal policy is characterized by a pair of probability thresholds, derive bounds for these thresholds, and investigate their sensitivity to changes in information cost, the payoff structure, and information quality. Our approach unifies and contributes to three distinct streams of research: technology adoption, partially observed optimal stopping, and comparison of experiments. We also articulate how real decision makers can implement this opportunity assessment framework and offer implications for those who fund these opportunities.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".