10 citations · 14 across the 7 of their papers we have counts for
7 papers
Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards
Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani
In practice, incentive providers (i.e., principals) often cannot observe the reward realizations of incentivized agents, which is in contrast to many principal-agent models that ha…
Simulation-Based Evaluation of Energy-Constrained Quantum-Classical Competition
Junyu Liu, Hansheng Jiang, Zuo-Jun Max Shen
This paper develops a simulation-based framework for evaluating the energy implications of quantum and classical computing firms competing in a market with limited energy resources…
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
Mengzi Amy Guo, Donghao Ying, Javad Lavaei +1
This work is dedicated to the algorithm design in a competitive framework, with the primary goal of learning a stable equilibrium. We consider the dynamic price competition between…
Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents
Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani
Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games wi…
Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm
Jingchuan Chen, Wei Chen, Jing Li +4
Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately,…
Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction
Donghao Ying, Mengzi Amy Guo, Hyunin Lee +3
We study Concave Constrained Markov Decision Processes (Concave CMDPs) where both the objective and constraints are defined as concave functions of the state-action occupancy measu…
Integrated Conditional Estimation-Optimization
Meng Qi, Paul Grigas, Zuo-Jun Max Shen
Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the sta…