Resource Graph Games: A Compact Representation for Games with Structured Strategy Spaces
Bibliographic record
Abstract
In many real-world systems, strategic agents' decisions can be understood as complex - i.e., consisting of multiple sub-decisions - and hence can give rise to an exponential number of pure strategies. Examples include network congestion games, simultaneous auctions, and security games. However, agents' sets of strategies are often structured, allowing them to be represented compactly. There currently exists no general modeling language that captures a wide range of commonly seen strategy structure and utility structure. We propose Resource Graph Games (RGGs), the first general compact representation for games with structured strategy spaces, which is able to represent a wide range of games studied in literature. We leverage recent results about multilinearity, a key property of games that allows us to represent the mixed strategies compactly, and, as a result, to compute various equilibrium concepts efficiently. While not all RGGs are multilinear, we provide a general method of converting RGGs to those that are multilinear, and identify subclasses of RGGs whose converted version allow efficient computation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".