Business Opportunity Assessment With Costly, Imperfect Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> 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. </para>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it