Keynesian expectations, epistemic authority and pluralism in economics: placebo and nocebo effects in normal and abnormal times
Bibliographic record
Abstract
Abstract Prominent economists may provide expert guidance to assist the public in forming expectations. Using both Keynes’ theory of conventional expectations formation and lay epistemology, this article argues that prominent economists may have sufficient ‘epistemic authority’ to encourage a self-fulfilling ‘placebo/nocebo effect’, meaning that widely and confidently-held expectations congruent with prominent economists’ guidance encourage economic behaviours that promote the economic outcomes predicted by these economists. This article examines the peripherality of pluralism in the economics discipline as supporting these self-fulfilling dynamics insofar as it: (i) contributes to the public’s capacity to identify and attribute epistemic authority to prominent economists, (ii) encourages sufficient convergence of prominent economists’ expectational guidance that the public can adopt coherent and confident expectations based on this guidance and (iii) facilitates the public dissemination of this expectational guidance. The conclusion considers Keynesian ‘abnormal times’ (such as a Minskian expectational scenario) that may discredit the epistemic authority of prominent economists (and perhaps expert economic knowledge in general) and considers some implications of these circumstances for disciplinary pluralism.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".