Bibliographic record
Abstract
Sharply increased uncertainty and possibility of catastrophes warrant a new approach to decision-making. To survive Superintelligence, mankind should downgrade its role - from ¡°an agent¡± that has a will and a preservation goal of its own, to just a tool that yields the power of making decisions to humans ¨C possibly Risk-Constrained Optimization (RCO). RCO is a fundamentally novel system dealing with decision-making under radical uncertainty. Instead of ¡°the best strategy¡± RCO constructs a ¡°strategy, most acceptable to decision-makers.¡± RCO develops a number of candidate strategies, filters them and presents to the decision-makers a few reasonably good and safe candidates, easily adaptable to a broad range of future scenarios - likely, ¡°black swan, ¡± and even improbable. The final selection of the strategy to be implemented is performed judgmentally by decision-makers. RCO overturns upside down Economics, Operations Research/Management Science, Decision Analysis, Scenario Planning, and Risk Management. The new paradigm of Superintelligence becomes preservation of mankind. RCO is just a toolkit. It can be used in any system. But, as far as this author knows, RCO is presently unique in its capability to deal with radical uncertainty ¨C moreover, by simple operations. It is therefore irreplaceable for Superintelligence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".