An agent-based "proof of principle" for Walrasian macroeconomic theory
Bibliographic record
Abstract
Macroeconomic models are typically solved through the imposition of a top-down general equilibrium solution constraining agents' rational be- havior. This is customarily obtained by recurring, explicitly or not, to the Walrasian auctioneer (WA) artifice. In this paper we aim at contributing to the small but burgeoning literature that deals with the consequences of removing it from the start by means of agent-based techniques. We let the textbook full-employment neoclassical macroeconomic model be populated by a large number of bounded-rational, autonomous agents, who are repeatedly engaged in decentralized transactions in interrelated markets. We set up a computational laboratory to perform several exper- iments, whose designs di�er as regards the way we treat learning on the one side, and the institutional arrangement determining who - between firms and workers - is bound to bear the risk associated to incomplete markets on the other one. We show that our fully decentralized multi- market system admits the possibility to attain the WA full-employment solution, but also that serious coordination failures emerge endogenously as learning mechanisms and institutional settings are varied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".