The Disappointment of Expectations and the Theory of Fluctuations. Paper presented at the Annual Meeting of the Associazione Italiana per la Storia dell'Economia Politica (STOREP), Rome, 1-3 June 2008; and at the Annual Conference of the History of Economics Society, Toronto, 27-30 June 2008
Bibliographic record
Abstract
The role of errors in time (Fanno, 1933) or disappointment of expectations (Hicks, 1933) in the theory of fluctuations was a major object of analysis in the years of high theory (Shackle, 1967) when the paradigm of General Equilibrium Theory was replaced by the new paradigm of the Economics of Uncertainty and Expectations. The scope of this paper is to re-evaluate this object of analysis in the light of the evolution of the theory of fluctuations ever since. The paper is divided in two Parts. Part I provides a unified account of how the main authors of the years of high theory (Keynes, Hayek, Hicks) dealt with expectations and their disappointment in their theory of fluctuations. Part II provides instead a brief account and assessment of the major constructions worked out by the main authors of what is here called the “years of rational expectations”. The paper shows how the focus on errors in time has been replaced in the latter period by a set of assumptions and arguments which either neglect the impact of the disappointment of expectations on macroeconomic disequilibrium and fluctuations or even exclude the very possibility of this disappointment. The paper concludes by highlighting the decreasing scope and relevance of the theory of fluctuations as one moves from the years of high theory to the recent years of rational expectations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".