WebIn this paper, we study such incentive-aware learning problem in a general setting and show that it is possible to approximately optimize the objective function under two assumptions: (i) each individual agent is a "small" (part of the market); and (ii) there is a cost … WebKeywords: repeated auctions, learning with strategic agents, incentive-aware learning, pricing 1. Introduction We study the fundamental problem of designing pricing policies for highly heterogeneous items. This study is inspired by the availability of the massive amount of real-time data in online platforms 1
Incentive-Aware Machine Learning for Decision Making
WebDec 8, 2024 · Given the seller's goal, utility-maximizing buyers have the incentive to bid untruthfully in order to manipulate the seller's learning policy. We propose two learning policies that are robust to such strategic behavior. WebGolrezaei, Jaillet, and Liang: Incentive-aware Contextual Pricing with Non-parametric Market Noise 2 mation about items features/contexts. In such environments, designing optimal policies involves learning buyers’ demand, which is a mapping from item features and offered prices to the likelihood of the item being sold. income thresholds for medicare premiums 2020
[2002.11137] Dynamic Incentive-aware Learning: Robust Pricing in ...
WebOct 14, 2024 · The seller’s goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, … WebAug 19, 2024 · We design an incentive-aware learning objective that captures the distance of a market outcome from equilibrium. Using this objective, we analyze the complexity of learning as a function of preference structure, casting learning as a stochastic multi-armed bandit problem. WebIncentive-aware Contextual Pricing with Non-parametric Market Noise Negin Golrezaei SloanSchoolofManagement, Massachusetts InstituteofTechnology, … income threshold for third stimulus check