Stackelberg Strategy for Uncertain Markov Jump Delay Stochastic Systems
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Bibliographic record
Abstract
In this letter, a robust Stackelberg game for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) is investigated. After introducing some definitions and preliminaries, we derive the conditions for the existence of a robust Stackelberg strategy set by means of cross coupled stochastic bilinear matrix inequalities (CCSBMIs), such that the upper bound of each decision maker's cost function is minimized. To overcome difficulties in solving the CCSBMIs, a feasible numerical algorithm based on the Krasnoselskii iteration sequence is proposed, which consists of linear matrix inequalities and cross coupled stochastic matrix equations (CCSMEs). It is shown that the weakly convergence property is attained. Finally, a practical example is solved to demonstrate the effectiveness and efficiency of the proposed scheme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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