arXiv research

No-regret learning with strategic experts, incentivized.

Study optimal reinsurance contracts to prevent moral hazard under non-concave premium principles.

Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.

This paper optimizes reinsurance contracts with belief differences between insurer and reinsurer.

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

Two-stage mechanism designs reduce regret in recommender systems with stochastic covariates.

Optimal bidding strategy for multi-platform ad auctions under budget constraints.

Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

New algorithm for learning preferences in decentralized matching markets reduces regret to logarithmic levels.

New algorithm reduces regret in strategic prediction problem.

Optimal insurance contract limits insurer's risk exposure variance.

New neural network architecture for auction design exploiting permutation symmetry.

A new mechanism optimizes data marketplace pricing efficiently.

Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.

The paper analyzes regret in bilateral trade mechanisms without prior valuations.

This paper optimizes liquidity provision in automated market makers using auction theory.

We consider the problem of Probably Approximate Correct (PAC) learning of a binary classifier from noisy labeled examples acquired from multiple annotators (each characterized by a respective classification noise rate). First, we consider the complete information scenario, where the learner knows the noise rates of all…

The paper solves an insurance problem using mean-variance and rank-dependent utility theory.

New auction design uses statistical learning to reduce costs and improve fairness.

The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.

Conditional forecasts improve performative prediction accuracy.

Study on learning strategies in matching markets with uncertain preferences.

We propose the development of a prediction market for forecasting prices for "toxic assets" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are cr…

It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice. This is primarily due to the cognitive loads required for human agents t…

This paper investigates Pareto optimal (PO, for short) insurance contracts in a behavioral finance framework, in which the insured evaluates contracts by the rank-dependent utility (RDU) theory and the insurer by the expected value premium principle. The incentive compatibility constraint is taken into account, so the …

Study incentive efficiency in monopoly insurance markets with hidden information.

Calibrated models can lead to miscalibrated aggregations in strategic interactions.

Paper proposes FMore to incentivize edge nodes in federated learning with MEC.

Extends PoS proof-of-stake transaction fee mechanism with miner utility model.

Incentive-aware recommender system for online platforms.

The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.

Foresight Arena benchmarks AI forecasting on real-world markets, isolating predictive edge.

A new mechanism reduces expert belief regret in online forecasting.

New model for recovering unverifiable signals from observers in decentralized networks.