Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
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In non-modular polymatroidal service markets, revenue-optimal DSIC mechanisms cannot also be credible for strategic operators, with tight welfare-loss bounds on the Cost of Non-Credibility across network topologies.
citing papers explorer
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Optimal scenario design for climate emulation
Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
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Credibility Trilemma in Polymatroidal Service Markets
In non-modular polymatroidal service markets, revenue-optimal DSIC mechanisms cannot also be credible for strategic operators, with tight welfare-loss bounds on the Cost of Non-Credibility across network topologies.