Bibliographic record
Abstract
A conceptual framework is provided by the "states of the world" approach (reviewed in section 1). The powerful normative analysis of individual decision-making under uncertainty extends the theory of consumer choice under certainty into an adequate specification of individual norms of behavior (section 2). But the link with observable market phenomena would require the existence of a complete set of insurance markets, one for every commodity conditionally on every state of the world. Although some disagreement persists on this point, the author feels that the insurance and asset markets which exist in western economies fall substantially short of offering trading opportunities comparable to those implied by a complete set of insurance markets. Consequently, consumer preferences are incompletely revealed by market prices. And business firms lack the information required to reach decisions by mere arithmetic comparisons of alternative profit levels (section 3). Management under uncertainty and incomplete markets acquires genuine significance. What norms of managerial behavior should be assumed for positive economic analysis is a disputed issue. From a normative viewpoint, managerial decisions must be viewed as group decisions, with consequences affecting many individuals—and the theory of such decisions is by necessity more complex (section 4). Deprived of the powerful clarification introduced by the competitive markets lamppost, the economic analysis of uncertainty must fall back on the elementary principle that all risks are ultimately borne by the individual economic agents, in their triple capacity as consumers, workers and investors. Alternative policies by firm managers or public officials must be evaluated in terms of their consequence for individuals on these three levels. The absence of market references and of reliable positive models causes difficulties in eliciting these consequences. A major concern of policy makers should be to understand better what forms of uncertainty are most costly to bear for individuals, so as to design institutions and policies aimed at transforming these into less costly alternatives (section 5).
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".