Bachelier: Not the forgotten forerunner he has been depicted as.An analysis of the dissemination of Louis Bachelier's work in economics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents the results of new research on the history of financial economics by analysing the dissemination of Louis Bachelier's work. Louis Bachelier is doubtless the best known French mathematician in the history of modern finance theory. While recent studies have given us a fairly complete picture of the man himself, his work and the results he arrived at, knowledge of his contribution to the development of ideas remains imprecise. Although the direct influence of his work is analysed on occasion, no study has assessed the dissemination of Bachelier's work, and hence its impact on all scientific disciplines. This is precisely the purpose of this article: to examine the dissemination of Bachelier's work in order to better assess his impact on the development of financial economics (Jovanovic (2010 Jovanovic, Franck, and Schinckus, Christophe, 2010. Financial economics birth in the 1960s. Unpublished working paper. 2010. [Google Scholar]) makes a similar analysis of the dissemination of Bachelier's work in mathematics). Based on a bibliometric analysis of Bachelier's work, this study aims at shedding light on his influence and explaining how the idea of his ‘rediscovery’ in the 1950s gained credence. This article demonstrates that, contrary to the widely accepted view, Bachelier's work has never been forgotten; it also shows that the discovery of Bachelier's work by economists has had no significant influence on the development of financial economics.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it