On the improbability of information efficient parimutuel betting markets in the presence of heterogeneous beliefs
Bibliographic record
Abstract
A very useful laboratory for the study of the informational efficiency in markets is a parimutuel betting market. Participants assess the relative likelihoods that various horses will win and then bet on the basis of this analysis. From this betting we can deduce the market's aggregate assessment of the probability that a particular class of horse will win (the so-called subjective probability) and this can be compared to that class's objective probability of winning. The conventional view is that, if parimutuel betting markets were efficient, these probabilities ought to coincide. Unfortunately, a significant number of empirical studies have found they do not. Most often, favourites are underbet and longshots overbet. However there have been studies which have reported a reverse bias (Busche and Hall, 1988; Woodland and Woodland, 1994). This mismatching of subjective and objective probabilities is termed the ‘favourite-longshot bias’. The instance where favourites are underbet is termed the usual bias; where favourites are overbet, it is termed the reverse bias.
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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 | 0.001 | 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 | 0.001 | 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".