On the improbability of information efficient parimutuel betting markets in the presence of heterogeneous beliefs
Bibliographic record
Abstract
Introduction 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. Not surprisingly there have been a number of explanations for the bias. The interested reader is referred to Thaler and Ziemba (1988) and Sauer (1998) for excellent summaries of the literature. One class of explanation appeals to bettor preferences. In particular, they posit that bettors are risk-lovers. This line of research would include the work of Weitzman (1965), Ali (1977), Quandt (1986), and Kanto, Rosenqvist and Suvas (1992). More recently Golec and Tamarkin (1998) have suggested that gamblers prefer return skewness rather than risk.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".