Optimizing Expenditure Functions Through Economic Cybernetics: Analyzing Linear and Non-Linear Programming Approaches
Bibliographic record
Abstract
This study explores the analysis of expenditure functions within the framework of economic cybernetics, a discipline that applies automatic control theory principles to manage economic processes, a concept particularly pertinent to socialist economies.The investigation delves into total expenditures, average expenditures, and marginal expenditures, and presents a methodology for determining the elasticity coefficient for each expenditure type.There are several coefficients and particularly they serve as a key metrics for economists to asses importance of variables and their impact in each other having in mind the relationships they have in economy.The paper introduces a novel approach to associating these expenditures with linear and non-linear programming.It is known that for a given amount of production, the corresponding amount of production elements must be consumed.Therefore, this is predicted on the idea that consumption of resources (factors of production) dictates the amount of output produced because production necessitates the usage of resources.The examination of the expenditure function through the lens of economic cybernetics offers deeper insights into the evolving economic landscape of the 21st century.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".