On domination game analysis for microeconomic data mining
Bibliographic record
Abstract
Game theory is a powerful tool for analyzing the competitions among manufacturers in a market. In this article, we present a study on combining game theory and data mining by introducing the concept of domination game analysis. We present a multidimensional market model, where every dimension represents one attribute of a commodity. Every product or customer is represented by a point in the multidimensional space, and a product is said to “dominate” a customer if all of its attributes can satisfy the requirements of the customer. The expected market share of a product is measured by the expected number of the buyers in the customers, all of which are equally likely to buy any product dominating him. A Nash equilibrium is a configuration of the products achieving stable expected market shares for all products. We prove that Nash equilibrium in such a model can be computed in polynomial time if every manufacturer tries to modify its product in a round robin manner. To further improve the efficiency of the computation, we also design two algorithms for the manufacturers to efficiently find their best response to other products in the market.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".