Bibliographic record
Abstract
This habilitation thesis presents advancements in computing exact and approximate solution concepts in dynamic games. Dynamic games model scenarios that evolve over time, players are able to perform actions that modify the environment, however, the players do not have perfect information about the environment and receive only partial information as observations. We consider strictly competitive (or zero-sum) games where a gain of one player is a loss of the opponent as well as general-sum games. Similarly, we consider both games with a finite, pre-defined number of moves (horizon) after which the game terminates, as well as games where the number of moves is not fixed. There are several key contributions. For zero-sum games, we provide algorithmic contributions for games with both finite and with infinite horizon. For finite games, we adopted the incremental strategy-generation technique in order to scale-up to larger domains and also provided the first algorithm for approximately solving games where players have imperfect memory (imperfect recall). For games with infinite horizon, we provide the first algorithms for approximately solving games where at least one player has partial information about the environment. For general-sum games, we provide several theoretical results determining the complexity of computing a Stackelberg Equilibrium and novel algorithms for its computation in finite dynamic games. Moreover, we formally define a novel solution concept, a variant of Stackelberg Equilibrium termed Stackelberg Extensive-Form Correlated Equilibrium, and we show that this solution concept is important both from the theoretical perspective, since the computational complexity is often lower compared to Stackelberg Equilibrium, as well as from the practical perspective. To this end, we propose an algorithm that uses this new solution concept in order to quickly compute a Stackelberg Equilibrium.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".