MétaCan
Menu
Back to cohort
Record W4382245184 · doi:10.11647/obp.0361.13

Equilibrium with prices and expectations

2023· book-chapter· en· W4382245184 on OpenAlexaff
Martin J. Osborne, Ariel Rubinstein

Bibliographic record

VenueOpen Book Publishers · 2023
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomicsMicroeconomicsQuality (philosophy)Unit (ring theory)Fish <Actinopterygii>Supply and demandEconometricsMathematicsFishery

Abstract

fetched live from OpenAlex

In the models of markets we have discussed so far, equilibrium prices make the individuals’ decisions compatible. Each individual takes the prices as given when deciding on her action, and at the equilibrium prices the demand and supply of every good are equal. In this chapter, an individual’s behavior is affected not only by the prices but also by her expectations regarding other parameters. Each individual takes these expectations, like the prices, as given. In equilibrium, each individual behaves optimally, the supply and demand for each good are equal, and the expectations of individuals are correct. We present three models. In the first model, each individual chooses one of two bank branches. Her decision is affected only by her belief about the expected service time in each branch. In the second model, potential buyers of a used car, who cannot observe the quality of the cars for sale, take into account their expectation of the average quality of these cars as well as the price. In the third model, the unit cost of catching fish depends on the total amount of fish caught. Each fisher makes her decision taking as given both the price of fish and her expectation about the unit cost she will incur.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.003

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.

Opus teacher head0.049
GPT teacher head0.219
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOpen Book PublishersSame topicEconomic theories and modelsFrench-language works237,207