PREDICTION OF BIRD FLU A(H5N1) OUTBREAKS IN TAIWAN BY ONLINE AUCTION: EXPERIMENTAL RESULTS
Bibliographic record
Abstract
The ability of accurate epidemic prediction facilitates early preparation for the disease and minimizes losses due to any strikes. We devised a platform on the Web for users to exchange their information/opinions on the possible avian flu outbreaks in Taiwan. The likelihood of the first human infection from bird flu in Taiwan in, say, December 2005 is securitized in the form of a futures contract. Incentives are introduced via a tournament: users trade the futures in the market on our Web server in order to win the awards at the end of the tournament. We ran such a tournament during the period between December 2005 and February 2006. The results of the futures' prices correctly predicted no outbreaks of bird flu among the residents in Taiwan during the 3-month period, suggesting that the design of the futures exchange on the Web be a potentially useful tool for event forecasting. Another crucial aspect of the experiment is that, associated with the price convergence, the transaction volume also quickly converges to zero, which is closely related to the famous no-trade theorem in theoretical economics.
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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 | 0.000 |
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 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".