The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.
Econometrica: Journal of the Econometric Society , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Generative AI advertising is reframed as a problem of trustworthy commercial intervention on the generative process, with a taxonomy of influence tiers from product mentions to long-term preference shaping.
citing papers explorer
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Harnessing Individual Motivation for Collective Efficiency: A Mechanism-Driven Distributed Optimization Method
The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.
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Generative AI Advertising as a Problem of Trustworthy Commercial Intervention
Generative AI advertising is reframed as a problem of trustworthy commercial intervention on the generative process, with a taxonomy of influence tiers from product mentions to long-term preference shaping.