# Mechanism Design for Locating a Facility under Partial Information

## TL;DR

This paper investigates robust mechanism design for locating a public facility on a line when agents only provide interval preferences, proposing mechanisms that perform well under partial information and different solution concepts.

## Contribution

It introduces a framework for designing robust deterministic mechanisms under partial agent preferences, extending classical mechanisms to uncertain preference settings.

## Key findings

- No better mechanisms than naive under very weak dominance.
- Significant improvements possible under minimax dominance.
- Provided bounds on mechanism performance for key objectives.

## Abstract

We study the classic mechanism design problem of locating a public facility on a real line. In contrast to previous work, we assume that the agents are unable to fully specify where their preferred location lies, and instead only provide coarse information---namely, that their preferred location lies in some interval. Given such partial preference information, we explore the design of robust deterministic mechanisms, where by robust mechanisms we mean ones that perform well with respect to all the possible unknown true preferred locations of the agents. Towards this end, we consider two well-studied objective functions and look at implementing these under two natural solution concepts for our setting i) very weak dominance and ii) minimax dominance. We show that under the former solution concept, there are no mechanisms that do better than a naive mechanism which always, irrespective of the information provided by the agents, outputs the same location. However, when using the latter, weaker, solution concept, we show that one can do significantly better, and we provide upper and lower bounds on the performance of mechanisms for the objective functions of interest. Furthermore, we note that our mechanisms can be viewed as extensions to the classical optimal mechanisms in that they perform optimally when agents precisely know and specify their preferred locations.