Ludwig Hamburger (1890–1968): From Relaxation Oscillations to Business Cycles
Bibliographic record
Abstract
Several authors have been interested in Ludwig Hamburger’s attempt to analyse business cycles with a nonlinear endogenous model in the early 1930s. Indeed, Hamburger was one of the first, if not the first, to suggest applying Van der Pol’s relaxation oscillations to business cycles. Ragnar Frisch was interested in his work when he was working on his 1933 seminal paper on a propagation-impulse model, in which we find some references to this suggestion. Despite the interest in Hamburger’s work, the breadth, scope and impact of his works remain unknown and misunderstood, for both historians of economics and sciences. Moreover, several errors, such as the reason why Hamburger did not continue his original work in economics, exist in the economic literature concerning this author and the diffusion of his work in economics. The present work provides a biography of Ludwig Hamburger and corrects the errors we find in the literature. It also sheds new light on the origins of his attempt to analyse business cycles with a nonlinear endogenous model.
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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) | 0.001 | 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) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".