Simulating New Markets by Introducing New Accepting Policies for the Conventional Continuous Double Auction
Bibliographic record
Abstract
In recent years a huge number of online auctions that use Multi Agent systems have been created. As a result there are numerous auctions that provide the same product. In this case each customer can buy a product with the lowest possible price. But searching between auctions in terms of finding the suitable product can be time consuming for consumers and also providing products in different markets is a difficult task for suppliers. So the need for an autonomous agent in these types of markets is deeply felt. On the other side the structure of an auction mechanism that provides the environment for traders to operate their trades is vital. Despite all the research that has been done about online auctions, most of them were about single markets. But in real world the stocks and commodities of companies are listed and traded in different markets. There is a growing tendency towards research about online auctions and Market Design. Particularly in recent years CAT (CATallactics) game has provided an important opportunity to develop and test new techniques in this field. In this paper after introducing CAT game and PersianCAT agent, we want to challenge the conventional accepting policy used in stock markets like New York Stock Exchange and provide a better solution that improves the general performance of the markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".