MétaCan
Menu
Back to cohort
Record W4408141514 · doi:10.1093/restud/rdaf014

Simultaneous Search and Adverse Selection

2025· article· en· W4408141514 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Review of Economic Studies · 2025
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning and Algorithms
Canadian institutionsUniversity of Toronto
FundersDeutsche Forschungsgemeinschaft
KeywordsAdverse selectionEconomicsSelection (genetic algorithm)EconometricsMicroeconomicsComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Abstract We study the effect of diminishing search frictions in markets with adverse selection by presenting a model in which agents with private information can simultaneously contact multiple trading partners. We highlight a new trade-off: facilitating contacts reduces coordination frictions but also the ability to screen agents’ types. We find that, when agents can contact sufficiently many trading partners, fully separating equilibria obtain only if adverse selection is sufficiently severe. When this condition fails, equilibria feature partial pooling and multiple equilibria co-exist. We show that facilitating contacts can lead to a reduction in welfare. In the limit, as the number of contacts becomes large, some of the equilibria converge to the competitive outcomes of Akerlof, including Pareto-dominated ones; other pooling equilibria continue to feature frictional trade in the limit, where entry is inefficiently high. Our findings provide a basis to assess the effects of recent technological innovations that have made meetings easier.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.332
Teacher spread0.313 · 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