A research framework for generalized congestions and market power
Bibliographic record
Abstract
Besides the transmission congestion reflecting the physical stability of a power system, power market is also constrained by factors like primary energy, emissions, technical support and multi-game. These factors affecting both competition level and efficiency of a power market can be called by a generic term as generalized congestions. The purpose of regulation is to avoid the abuse of market power and reduce the risk in social welfare. However, inappropriate regulation may also degrade the efficiency of a market; even endanger the market and social stability. So regulation is also a kind of generalized congestions. The capability of market participants to influence (increase or decrease) market efficiency through taking the advantage of generalized congestions can be called as market power, while generalized market power reflects the capability to influence social welfare. Generalized congestions, market power and generalized market power are analyzed in aspects such as classification, evaluation indexes, control measures and research methods. A framework is proposed for the mechanism study of generalized congestions, market power and generalized market power. With this framework, experimental economics can be implemented, and comprehensive analysis can be taken to study the mechanism for generalized congestions to influence the market power.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".