activity
20192026
most citedIndependent and Decentralized Learning in Markov Potential Games

6 citations · 16 across the 25 of their papers we have counts for

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6 papers · 1 filter

cs.GT2026

Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management

Jihun Kim, Pan-Yang Su, Maria G. Mendoza +3

Indivisible resources allocated in advance may need to be reallocated when capacity reductions make existing assignments infeasible, yet monetary compensation is often impractical…

cs.GT2026

Game-to-Real Gap: Quantifying the Effect of Model Misspecification in Network Games

Bryce L. Ferguson, Chinmay Maheshwari, Manxi Wu +1

Game-theoretic models and solution concepts provide rigorous tools for predicting collective behavior in multi-agent systems. In practice, however, different agents may rely on dif…

cs.GT2024

Adaptive Incentive Design with Learning Agents

Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu +1

We propose an adaptive incentive mechanism that learns the optimal incentives in environments where players continuously update their strategies. Our mechanism updates incentives b…

cs.GT2024

Congestion Pricing for Efficiency and Equity: Theory and Applications to the San Francisco Bay Area

Chinmay Maheshwari, Kshitij Kulkarni, Druv Pai +3

Congestion pricing, while adopted by many cities to alleviate traffic congestion, raises concerns about widening socioeconomic disparities due to its disproportionate impact on low…

cs.GT2023★ 2 cited

Markov -Potential Games

Xin Guo, Xinyu Li, Chinmay Maheshwari +2

We propose a new framework of Markov -potential games to study Markov games. We show that any Markov game with finite-state and finite-action is a Markov -potential game, and…

cs.GT2022★ 1 cited

Inducing Social Optimality in Games via Adaptive Incentive Design

Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu +1

How can a social planner adaptively incentivize selfish agents who are learning in a strategic environment to induce a socially optimal outcome in the long run? We propose a two-ti…