6 papers
Matching Tasks to Agents: Mechanism Design for Multi-Agent Contracts
Matteo Bollini, Matteo Castiglioni, Alberto Marchesi
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 ef…
Replicable Constrained Bandits
Matteo Bollini, Gianmarco Genalti, Francesco Emanuele Stradi +2
Algorithmic \emph{replicability} has recently been introduced to address the need for reproducible experiments in machine learning. A \emph{replicable online learning} algorithm is…
Learning in Bayesian Stackelberg Games With Unknown Follower's Types
Matteo Bollini, Francesco Bacchiocchi, Samuel Coutts +2
We study online learning in Bayesian Stackelberg games, where a leader repeatedly interacts with a follower whose unknown private type is independently drawn at each round from an…
Online Bayesian Persuasion Without a Clue
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-rel…
Contracting With a Reinforcement Learning Agent by Playing Trick or Treat
Matteo Bollini, Francesco Bacchiocchi, Matteo Castiglioni +2
We study principal-agent problems where a farsighted agent takes costly actions in an MDP. The core challenge in these settings is that agent's actions are hidden to the principal,…
The Sample Complexity of Stackelberg Games
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
Stackelberg games (SGs) constitute the most fundamental and acclaimed models of strategic interactions involving some form of commitment. Moreover, they form the basis of more elab…