An Adaptive Learning Game Model for Interacting Electric Power Markets
Bibliographic record
Abstract
In the present paper, a simulation model of two interacting electric power markets is being introduced, with or with no restriction in the interconnection capacity, in order to study the behavior of the energy price under two different pricing methods: Uniform Pricing and Pay-As-Bid. The model simulates the operation of the two markets as a stochastic adaptive Nash game, where players use a learning algorithm to maximize their profit and counterbalance their lack of information. The comparison of the results between the independent operation of the markets and the one of the interacting operation shows that lower prices are recorded when both interconnected systems apply Uniform Pricing and markets are oligopolies, whereas higher prices arise when both markets apply the pay-as-bid rule and tend towards perfect competition. In the case where the two interacting markets apply different pricing methods the differences observed in the independent market operation are blunted and prices tend to converge in intermediary price levels. Finally, constrained interconnection capacity leads to slightly higher prices at all instances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".