Adaptive Maximization of Social Welfare

Nicol`o Cesa-Bianchi, Roberto Colomboni and Maximilian Kasy

No bgcjk, SocArXiv from Center for Open Science

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 multi-armed 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). (Stone Center on Socio-Economic Inequality Working Paper)

Date: 2024-07-31
New Economics Papers: this item is included in nep-des, nep-pbe and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/66aa700f7ab35b8fae2ef11c/

Related works:
Working Paper: Adaptive maximization of social welfare (2024)
Working Paper: Adaptive Maximization of Social Welfare (2024)
Working Paper: Adaptive Maximization of Social Welfare (2024)
Working Paper: Adaptive Maximization of Social Welfare (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:bgcjk

DOI: 10.31219/osf.io/bgcjk

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().