Bibliographic record
Abstract
Perhaps the best-known result from neoclassical economics is the 'law of supply and demand'. This depicts markets using curves of supply and demand that intersect at a unique equilibrium, whose value represents a kind of aggregate market decision about price. However, because it is impossible to separate supply and demand in practice, the model has little in the way of empirical backing. In finance, in contrast, the related question of price impact, where a large transaction results in a changed price, has been widely studied. This paper uses a probabilistic approach to obtain a model of price impact in the context of asset pricing. A model based on classical probability is first used to simulate economic decisions to buy or sell, and a quantum version is then developed that better captures the response of the system to perturbations. The result is then extended to the general question of supply and demand. The formula is used to obtain a relationship between price change and volatility which is illustrated using empirical stock market data, and implications for other areas such as option pricing and real estate are discussed.This article is part of the theme issue 'Quantum theory and topology in models of decision making (Part 1)'.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".