Predictive Possibilities of Entropy Indicators of Complexity (Прогнозні можливості ентропійних показників складності)
Bibliographic record
Abstract
English abstract: The financial and economic crises that have accompanied mankind throughout the existence of commodity-money relations and their consequences have revealed the ineffectiveness of existing economic paradigms. At the same time, the crises have given impetus to update existing and create new interdisciplinary methods in the arsenal of the economy. The paper proposes to apply the econophysical method of studying economic systems, namely entropy analysis. Entropy methods are an important area of mathematical modeling of complex systems. These methods are based on the use of entropy as a criterion for assessing the functioning of systems, as entropy is a universal indicator for systems of any nature. The use of entropy as a tool for research and analysis of complex economic systems allows to understand the specific features of different characteristics of complex systems, to assess the adequacy of tools for modeling and predicting the behavior of systems. The object of research is the processes of functioning of stock markets. The daily values of stock indices of Europe (Ukraine, Germany, France), America (USA, Canada, Argentina) and Asia (Japan, China, India) for the period from 2004 to 2017 were chosen as the basis of the study. Calculations were performed in Matlab environment using procedures of a moveable window that allows to receive values of indicators in dynamics. The use of entropy indicators and their characteristics of behavior in pre-crisis periods in the process of monitoring economic systems allows to obtain forecast information and create a softening cushion for the market and the economy as a whole. All entropy indicators considered in the work showed an early response to crisis phenomena. Therefore, we can recommend them as indicators of crisis. Ukrainian Abstract: Передбачення поведінки економічної системи на основі індикаторів-передвісників та аналізу часових рядів є актуальною та цікавою проблемою. За результатами проведеного дослідження доведено, що використання ентропійних показників та їх характерних особливостей поведінки у передкризові періоди в процесі моніторингу світових фондових ринків дозволяє отримати передпрогнозну інформацію та створити «пом’якшуючу подушку» для ринку та економіки в цілому. Всі ентропійні показники, розглянуті в роботі, продемонстрували завчасне реагування на кризові явища.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".