We study task assignment under adverse selection and moral hazard. In the single-task setting, agents privately know their types, which determine their success probabilities and effort costs. Based on the agents'reported types, the principal selects an agent to perform the task and offers a success-contingent payment....
Matteo Bollini, Matteo Castiglioni, A. Marchesi· 0 citations
We study online profit maximization in multilateral trade, a setting that generalizes both dynamic pricing and bilateral trade. At each round, an intermediary posts prices to buyers and offers payments to sellers; trade occurs only if every agent accepts. We consider valuations drawn from an unknown joint distribution,...
Francesco Bacchiocchi, Matteo Castiglioni, Anna Lunghi et al.· 0 citations
We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both decisions, while observing only whether each offer leads to a purchase. We propose an onl...
Alessandro Barro, Francesco Bacchiocchi, F. Stradi et al.· 0 citations
It is shown that when both market prices and trader valuations are chosen adversarially, sublinear regret is impossible even under full feedback, thereby motivating the stochastic assumption on the valuations.
Maria Elena Vischi, F. Stradi, A. Marchesi· 0 citations
Online Resource Allocation (ORA) is a fundamental framework for sequential decision-making problems under budget constraints. Classical ORA models typically assume that resources are monotonic, meaning that selecting actions can only decrease the available budget. In this work, we study a more general setting with repl...
Eleonora Fidelia Chiefari, F. Stradi, A. Marchesi· 0 citations
An algorithm achieving regret $\widetilde{\mathcal{O}}(T^{7/10})$ is designed, improving over the previous best-known bound of $\widetilde{\mathcal{O}}(T^{3/4})$ and showing that the curse of dimensionality can be at least partially lifted for this class of objectives.
Matteo Castiglioni, Anna Lunghi, A. Marchesi· arXiv.org· 0 citations
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