Bibliographic record
Abstract
This habilitation thesis presents advancements in computing exact and approximate solution concepts \nin dynamic games. Dynamic games model scenarios that evolve over time, players are able to \nperform actions that modify the environment, however, the players do not have perfect information \nabout the environment and receive only partial information as observations. We consider strictly \ncompetitive (or zero-sum) games where a gain of one player is a loss of the opponent as well as \ngeneral-sum games. Similarly, we consider both games with a finite, pre-defined number of moves \n(horizon) after which the game terminates, as well as games where the number of moves is not fixed. \nThere are several key contributions. For zero-sum games, we provide algorithmic contributions \nfor games with both finite and with infinite horizon. For finite games, we adopted the incremental \nstrategy-generation technique in order to scale-up to larger domains and also provided the first \nalgorithm for approximately solving games where players have imperfect memory (imperfect recall). \nFor games with infinite horizon, we provide the first algorithms for approximately solving games \nwhere at least one player has partial information about the environment. \nFor general-sum games, we provide several theoretical results determining the complexity of \ncomputing a Stackelberg Equilibrium and novel algorithms for its computation in finite dynamic \ngames. Moreover, we formally define a novel solution concept, a variant of Stackelberg Equilibrium \ntermed Stackelberg Extensive-Form Correlated Equilibrium, and we show that this solution concept \nis important both from the theoretical perspective, since the computational complexity is often lower \ncompared to Stackelberg Equilibrium, as well as from the practical perspective. To this end, we \npropose an algorithm that uses this new solution concept in order to quickly compute a Stackelberg \nEquilibrium.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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 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".