Simulation versus optimization in knowledge‐induced fields
Bibliographic record
Abstract
Pervasive complementarity among agents, variables and their relations is a strong manifestation of unity in the real world. It is explained in various ways within scientific systems and in alternative ways of viewing resource allocation from that in neoclassical economic theory and its various prototypes. Complementarity among goods, services and factors in neoclassical resource allocation is simply a localized phenomenon. Despite this, bundles of similar goods collect together to re‐establish marginal substitution with other bundles. In systems science, the cessation of complementarity among variables causes the demise of process. Indeed, the most significant influence of economic complementarity is to be found in decision‐making systems. Here strongly interactive ethical principles showing pervasive and strong complementarity reveal themselves. Hence a knowledge‐induced scientific methodology emerges. Yet these scientific dynamic methods that are merely premised on time‐phase, are found to be inadequate in explaining pervasive interactions. Instead, simulation methods reveal important and interesting results premised on the epistemological premise of systemic unity and interactions. We will examine these questions in this paper with respect to the optimal control problem of the calculus of variations, and for multi‐objective decision problems.
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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 | 0.008 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".