Models of public investment management at regional level
Bibliographic record
Abstract
Despite significant funding of current national projects and state programs designed to make a breakthrough in the socioeconomic and scientific-technological development of Russia, the problem of creating a unified methodology for the development and implementation of investment and industrial policy remains unresolved. The proportion of methods using relevant tools of economicmathematical modeling and information technologies is still quite low. This issue is particularly acute at regional level. The study aims to substantiate the regional investment model as an effective tool for strategic management of the national economy and its practical implementation using information technologies. We develop and implement a regional investment model based on agent-oriented modeling. This model will allow the executive authorities of any subject of the Russian Federation to make effective management decisions and update the provisions of the regional investment and industrial policy in conditions of limited investment resources (budget funds). The methodological platform of the research is the synthesis of strategic management, indicative planning and reproductive approach. In the study, the methods of agent-oriented modeling and the modeling based on production functions are applied. The study of investment activity in Russia is conducted according to regional statistics (using data for 2017) with the use of artificial intelligence by the method of self-organizing Kohonen maps in a special software product Deductor Studio Lite. Using data for the Republic of Bashkortostan, we establish the possibility of applying production functions to describe functional dependencies in the author’s regional investment model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".