Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model

Kernekoncepter

The authors aim to design strategyproof mechanisms leveraging predictions for improved approximation guarantees in the Generalized Assignment Problem (GAP) within the private graph model.

Resumé

The paper explores designing strategyproof mechanisms using predictions to enhance performance guarantees in GAP variants, focusing on consistency and robustness trade-offs. It introduces a lower bound on achievable consistency and robustness for deterministic strategyproof mechanisms.
The study delves into lexicographic extensions, stable matching theory, and impossibility results, highlighting the challenges of achieving optimal approximation guarantees in strategic environments.

Statistik

The Bipartite Matching Problem (BMP) requires a mechanism that is (1 + 1/γ)-consistent and (1 + γ)-robust.
For BMP, no deterministic strategyproof mechanism can achieve both 1-consistency and bounded robustness.
In terms of error parameter η, no deterministic strategyproof mechanism for BMP can be (1+ 1/γ)-consistent and (1/(1-η+ϵ))-approximate for any η ∈ (0, γ/(1+γ)].

Citater

"The challenge here is to devise algorithms that achieve optimal approximation guarantees as the prediction quality varies from perfect to imperfect."
"Our goal is to design strategyproof mechanisms that leverage predictions to achieve improved approximation guarantees."
"In light of this, we turn towards a slightly more restrictive model for GAP called the private graph model."
"We study how to leverage learning-augmented predictions in the domain of mechanism design without money."

Vigtigste indsigter udtrukket fra

To Trust or Not to Trust

by Ricc... kl. arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03725.pdf

Dybere Forespørgsler

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