Bibliographic record
Abstract
Keynes begun his scientifïc career with probability theory. But, he had not the idea, as far as we can know, to give a probabilistic interpretation of his famous multiplier. This article is aimed at showing that probability theory, and especially finite Markov chains theory, gives an easier and even more natural interpretation of the keynesian multiplier than the traditional methods. Multiplier theory may be looked on as old-fashioned today, but it is still at the heart of most of macroeconometric models. So, we define first the relative position of the multiplier, which is linear and actually static, inside these models which are non-linear and dynamic. Secondly, we give a markovian interpretation of the income multiplier in both cases of the simple multiplier and the matrix multiplier. We compare it with the traditional interpretation: in the probabilistic interpretation every kind of economic agents (banks and firms, and not only households) take a part in the process of incomes which leads to the multiplier. Finally, we enlarge our method to the neighbouring analysis of the money multiplier and of the velocity of money. Our conclusion is that the markovian method could also be used for a keynesian crisis analysis.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".