On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

8 results for “incentive-aware”

Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraintscs.GT

Study allocates resources to strategic agents while balancing cost and incentives.

problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.

method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.

result Achieves

ilde{\mathcal{O}}(\sqrt{T})

social welfare regret, satisfies all cost constraints, and ensures incentive alignment.

resource allocation strategic agents long-term cost constraints incentive-aware online learning

2025-07-13abs ↗pdf ↗

Collaborative Machine Learning with Incentive-Aware Model Rewardscs.LG

The paper proposes a model reward scheme for collaborative ML based on Shapley value and information gain.

problem Designing fair incentives for collaborative machine learning.

method The paper proposes a reward scheme based on Shapley value and information gain, with properties like fairness and stability.

result The proposed reward scheme satisfies fairness and trade-offs between desirable properties via an adjustable parameter.

collaborative machine learning incentive-aware model rewards Shapley value information gain fairness

2020-10-24abs ↗pdf ↗

Incentive-Aware Recommender Systems in Two-Sided Marketscs.IR

Incentive-aware recommender system for online platforms.

problem Myopic agents exploit optimal arms, not exploring alternatives.

method Model as multi-agent bandit problem, incentivizes exploration.

result Asymptotically optimal performance with ex-post fairness.

recommender systems multi-agent bandit incentive alignment opportunity costs fairness

2022-11-23abs ↗pdf ↗

Learning Equilibria in Matching Markets from Bandit Feedbackcs.LG

New algorithms learn stable matchings from uncertain user preferences.

problem Learning stable matchings from uncertain user preferences.

method Stochastic multi-armed bandit problem, incentive-aware learning objective, primal-dual formulation.

result Near-optimal regret bounds for learning stable matchings.

matching markets bandit feedback stable equilibria incentive-aware regret bounds

2021-08-19abs ↗pdf ↗

Microeconomic Foundations of Multi-Agent Learningstat.ML

The paper develops an economic foundation for multi-agent learning in markets.

problem Learning dynamics in markets with strategic externalities.

method A two-phase incentive mechanism that estimates and uses implementable transfers to steer long-run dynamics.

result The mechanism achieves sublinear social-welfare regret and asymptotically optimal welfare under mild rationality and exploration conditions.

multi-agent learning Markov decision process strategic externalities incentive mechanism social-welfare regret

2026-01-06abs ↗pdf ↗

Incentive-aware Contextual Pricing with Non-parametric Market Noisecs.LG

We consider a dynamic pricing problem for repeated contextual second-price auctions with multiple strategic buyers who aim to maximize their long-term time discounted utility. The seller has limited information on buyers' overall demand curves which depends on a non-parametric market-noise distribution, and buyers may …

2019-11-08abs ↗pdf ↗

Overcoming the Incentive Collapse Paradoxstat.ML

AI task delegation faces incentive collapse with unbounded payments as AI accuracy rises.

problem Incentive collapse in AI-assisted task delegation schemes.

method General impossibility result and sentinel-auditing payment mechanism.

result Sentinel-auditing mechanism enforces positive human effort at finite cost, independent of AI accuracy.

AI delegation incentive collapse sentinel auditing statistical inference active learning

2026-03-27abs ↗pdf ↗

Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctionscs.LG

Ad exchanges use CORP to set reserve prices against strategic buyers.

problem Setting optimal reserve prices in ad exchanges with strategic buyers.

method Proposes CORP policy to learn and set reserve prices robustly.

result Achieves sublinear regret in unknown noise distribution.

ad exchanges contextual auctions reserve pricing strategic buyers regret minimization

2020-02-25abs ↗pdf ↗