Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

DOI10.1613/jair.4940zbMath1351.68293arXiv1405.2875OpenAlexW2326254517WikidataQ114367340 ScholiaQ114367340MaRDI QIDQ3467653

Aleksandrs Slivkins, Chien-Ju Ho, Jennifer Wortman Vaughan

Publication date: 4 February 2016

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.2875

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Agent technology and artificial intelligence (68T42)

Related Items (13)

Bayesian agency: linear versus tractable contracts ⋮ Incentivizing Exploration with Heterogeneous Value of Money ⋮ Bayesian Exploration: Incentivizing Exploration in Bayesian Games ⋮ Unnamed Item ⋮ Learning approximately optimal contracts ⋮ Learning approximately optimal contracts ⋮ Designing menus of contracts efficiently: the power of randomization ⋮ An optimal bidimensional multi-armed bandit auction for multi-unit procurement ⋮ A Differential Privacy Mechanism that Accounts for Network Effects for Crowdsourcing Systems ⋮ Robustness and approximation for the linear contract design ⋮ Bayesian Incentive-Compatible Bandit Exploration ⋮ The Complexity of Contracts ⋮ Principal-agent problem under the linear contract

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