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Record W4410327862 · doi:10.23952/asvao.8.2026.1.04

Adapting to climate change: A two-stage Nash equilibrium model of coalition formation

2025· article· en· W4410327862 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Set-Valued Analysis and Optimization · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersGruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro ApplicazioniIstituto Nazionale di Alta Matematica "Francesco Severi"
KeywordsNash equilibriumMathematical economicsStage (stratigraphy)Climate changeEconomicsGeologyOceanography

Abstract

fetched live from OpenAlex

We propose a two-stage noncooperative game theoretic model to describe the coalition formation of countries which decide to jointly invest in research and developments projects to mitigate the damages induced by climate changes.The first-stage game is a finite game where each country has only two strategies: sign or not an international agreement with other countries, while the second-stage game is a generalized Nash equilibrium problem, where each country aims to find the optimal levels of pollutant emission and effort in research and development projects.The variational equilibrium of the second-stage game is reformulated as an equivalent variational inequality with a reduced number of variables and the monotonicity of the corresponding operator is investigated.Finally, the impact of the coalition on the reduction of the environmental damage is numerically investigated.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.086
GPT teacher head0.284
Teacher spread0.198 · 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