Bibliographic record
Abstract
I investigate whether the popular Krusell and Smith algorithm used to solve heterogeneous-agent economies with aggregate uncertainty and in- complete markets is likely to be subject to multiple self-fullling equilibria. In a benchmark economy, the parameters representing the equilibrium ag- gregate law of motion are randomly perturbed 500 times, and are used as the new initial guess to compute the equilibrium with this algorithm. In a sequence of cases, diering only in the magnitude of the perturbations, I do not nd evidence of multiple self-fullling equilibria. The economic reason behind the result lies in a self-correcting mechanism present in the algorithm: compared to the equilibrium law of motion, a candidate one implying a higher (lower) expected future capital reduces (increases) the equilibrium interest rates, increasing (reducing) the savings of the wealth- rich agents only. These, on the other hand, account for a small fraction of the population and cannot compensate for the opposite change triggered by the wealth-poor agents, who enjoy higher (lower) future wages and increase (reduce) their current consumption. Quantitatively, the change in behavior of the wealth-rich agents has a negligible impact on the de- termination of the change in the aggregate savings, inducing stability in the algorithm as a by-product.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".