Generalized Assignment Problem: Truthful Mechanism Design without Money

TL;DR

This paper develops truthful, approximation algorithms for strategic variants of the generalized assignment problem, ensuring no agent benefits from hiding compatibility, with applications in auction settings without payments.

Contribution

It introduces the first truthful mechanisms for strategic GAP variants, providing approximation algorithms without monetary transfers.

Findings

Achieved a 4-approximation for multiple knapsack and density-invariant GAP.

Proposed an $O( ext{log}(U/L))$-approximation for the general problem.

Ensured truthfulness in strategic environments without payments.

Abstract

In this paper, we study a problem of truthful mechanism design for a strategic variant of the generalized assignment problem (GAP) in a both payment-free and prior-free environment. In GAP, a set of items has to be optimally assigned to a set of bins without exceeding the capacity of any singular bin. In the strategic variant of the problem we study, bins are held by strategic agents, and each agent may hide its compatibility with some items in order to obtain items of higher values. The compatibility between an agent and an item encodes the willingness of the agent to receive the item. Our goal is to maximize total value (sum of agents' values, or social welfare) while certifying no agent can benefit from hiding its compatibility with items. The model has applications in auctions with budgeted bidders. For two variants of the problem, namely \emph{multiple knapsack problem} in which each item has the same size and value over bins, and \emph{density-invariant GAP} in which each item has the same value density over the bins, we propose truthful $4$-approximation algorithms. For the general problem, we propose an $O(\ln{(U/L)})$-approximation mechanism where $U$ and $L$ are the upper and lower bounds for value densities of the compatible item-bin pairs.