Natural Selection and Market Efficiency in a Futures Market with Random Shocks
Bibliographic record
Abstract
Abstract Even when participants know very little about their environment, the market itself, by serving as a selection process of information, promotes an efficient aggregate outcome. To emphasize the role of the market and the importance of natural selection rather than the strategic actions of participants, an evolutionary model of a commodity futures market is presented, in which there is a continual inflow of unsophisticated traders with predetermined distributions of prediction errors with respect to the fundamental value of the spot price. The market acts as a selection process by constantly shifting wealth from traders with less accurate information to those with more accurate information. Consequently, with probability 1, if the volatility of the underlying spot market is sufficiently small, the proportion of time that the futures price is sufficiently close to the fundamental value converges to one. Furthermore, the width of the interval containing the fundamental value, where the futures price eventually lies, increases as the volatility of the underlying spot market increases. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:489–516, 2001
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".