 Adaptive Maximization of Social WelfareNicolò Cesa‐Bianchi, Roberto Colomboni and Maximilian Kasy Econometrica, 2025, vol. 93, issue 3, 1073-1104 Abstract: We consider the problem of repeatedly choosing policies to maximize social welfare. Welfare is a weighted sum of private utility and public revenue. Earlier outcomes inform later policies. Utility is not observed, but indirectly inferred. Response functions are learned through experimentation. We derive a lower bound on regret, and a matching adversarial upper bound for a variant of the Exp3 algorithm. Cumulative regret grows at a rate of T2/3. This implies that (i) welfare maximization is harder than the multiarmed bandit problem (with a rate of T1/2 for finite policy sets), and (ii) our algorithm achieves the optimal rate. For the stochastic setting, if social welfare is concave, we can achieve a rate of T1/2 (for continuous policy sets), using a dyadic search algorithm. We analyze an extension to nonlinear income taxation, and sketch an extension to commodity taxation. We compare our setting to monopoly pricing (which is easier), and price setting for bilateral trade (which is harder). Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:emetrp:v:93:y:2025:i:3:p:1073-1104 Ordering information: This journal article can be ordered from Access Statistics for this article Econometrica is currently edited by Guido W. Imbens More articles in Econometrica from Econometric Society Contact information at EDIRC. |
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