Centralized Versus Decentralized Optimization of Distributed Stochastic Differential Decision Systems With Different Information Structures—Part II: Applications
Bibliographic record
Abstract
In this second part of the two-part paper, a stochastic maximum principle, conditional Hamiltonians, and the coupled backward-forward stochastic differential equations of the first part [1] are employed to derive decentralized team optimal strategies for distributed decision systems with different information structures. Examples of such team problems are presented in nonlinear and linear quadratic forms. In many cases, the expressions of the optimal decentralized strategies are obtained. An interesting feature of any one optimal decentralized strategy is the dependence on the conditional estimates of the other optimal responses. For team problems of linear quadratic form and independent nonanticipative information structures, any optimal strategy depends linearly on the mean values of the other optimal responses. This property makes their computation feasible, even for large-scale distributed systems with many decision makers (DMs). It is also related to mean field stochastic optimal control problems, with finite number of DMs. The examples presented illustrate the effect of information signaling among the DMs in reducing the computational complexity of optimal decentralized strategies.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".