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Record W2110214742 · doi:10.1142/s0219198908001984

A CLASS OF GAMES WITH COUPLED CONSTRAINTS TO MODEL INTERNATIONAL GHG EMISSION AGREEMENTS

2008· article· en· W2110214742 on OpenAlexafffund
Olivier Bahn, Alain Haurie

Bibliographic record

VenueInternational Game Theory Review · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsGroup for Research in Decision AnalysisHEC Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreenhouse gasNegotiationConstraint (computer-aided design)Nash equilibriumYield (engineering)Kyoto ProtocolPareto principleEconomicsClass (philosophy)Mathematical economicsMicroeconomicsMathematicsComputer scienceOperations managementEcologyPhysics

Abstract

fetched live from OpenAlex

This paper deals with the design of equilibrium solutions with coupled constraints in dynamic games of greenhouse gas (GHG) emissions abatement. Self enforcing International Environmental Agreements (IEA) among different groups of countries call for Nash equilibrium solutions when the abatement strategies of the countries are defined. In this paper we study the effect of having another party, like e.g. the United Nations which would impose to all players a coupled constraint on the total emissions allowed over the 21st century, or on the concentration of carbon reached at the end of the century. We show, using different formulations of environmental game, that the normalized equilibria obtained under a coupled constraint on emissions or concentration is close to Pareto optimality. This gives a clue on the way the post Kyoto negotiations could yield an agreement which could be close to efficiency.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.107
GPT teacher head0.294
Teacher spread0.187 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations22
Published2008
Admission routes2
Has abstractyes

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