Mechanism Design for Facility Location Problems: A Survey

The study of approximate mechanism design for facility location problems has been in the center of research at the intersection of artificial intelligence and economics for the last decades, largely due to its practical importance in various domains, such as social planning and clustering. At a high level, the goal is to design mechanisms to select a set of locations on which to build a set of facilities, aiming to optimize some social objective and ensure desirable properties based on the preferences of strategic agents, who might have incentives to misreport their private information such as their locations. This paper presents a comprehensive survey of the significant progress that has been made since the introduction of the problem, highlighting the different variants and methodologies, as well as the most interesting directions for future research

Optimization in Strategic Environments

This work considers the problem faced by a decision maker (planner) trying to optimize over incomplete data. The missing data is privately held by agents whose objectives are dierent from the planner's, and who can falsely report it in order to advance their objectives. The goal is to design optimization mechanisms (algorithms) that achieve "good" results when agents' reports follow a game-theoretic equilibrium. In the first part of this work, the goal is to design mechanisms that provide a small worst-case approximation ratio (guarantee a large fraction of the optimal value in all instances) at equilibrium. The emphasis is on strategyproof mechanisms|where truthfulness is a dominant strategy equilibrium|and on the approximation ratio at that equilibrium. Two problems are considered|variants of knapsack and facility location problems. In the knapsack problem, items are privately owned by agents, who can hide items or report fake ones; each agent's utility equals the total value of their own items included in the knapsack, while the planner wishes to choose the items that maximize the sum of utilities. In the facility location problem, agents have private linear single sinked/peaked preferences regarding the location of a facility on an interval, while the planner wishes to locate the facility in a way that maximizes one of several objectives. A variety of mechanisms and lower bounds are provided for these problems. The second part of this work explores the problem of reassigning students to schools. Students have privately known preferences over the schools. After an initial assignment is made, the students' preferences change, get reported again, and a reassignment must be obtained. The goal is to design a reassignment mechanism that incentivizes truthfulness, provides high student welfare, transfers relatively few students from their initial assignment, and respects student priorities at schools. The class of mechanisms considered is permuted lottery deferred acceptance (PLDA) mechanisms, which is a natural class of mechanisms based on permuting the lottery numbers students initially draw to decide the initial assignment. Both theoretical and experimental evidence is provided to support the use of a PLDA mechanism called reversed lottery deferred acceptance (RLDA). The evidence suggests that under some conditions, all PLDA mechanisms generate roughly equal welfare, and that RLDA minimizes transfers among PLDA mechanisms

Proportional Fairness and Strategic Behaviour in Facility Location Problems

The one-dimensional facility location problem readily generalizes to many real world problems, including social choice, project funding, and the geographic placement of facilities intended to serve a set of agents. In these problems, each agent has a preferred point along a line or interval, which could denote their ideal preference, preferred project funding, or location. Thus each agent wishes the facility to be as close to their preferred point as possible. We are tasked with designing a mechanism which takes in these preferred points as input, and outputs an ideal location to build the facility along the line or interval domain. In addition to minimizing the distance between the facility and the agents, we may seek a facility placement which is fair for the agents. In particular, this thesis focusses on the notion of proportional fairness, in which endogenous groups of agents with similar or identical preferences have a distance guarantee from the facility that is proportional to the size of the group. We also seek mechanisms that are strategyproof, in that no agent can improve their distance from the facility by lying about their location. We consider both deterministic and randomized mechanisms, in both the classic and obnoxious facility location settings. The obnoxious setting differs from the classic setting in that agents wish to be far from the facility rather than close to it. For these settings, we formalize a hierarchy of proportional fairness axioms, and where possible, characterize strategyproof mechanisms which satisfy these axioms. In the obnoxious setting where this is not possible, we consider the welfare-optimal mechanisms which satisfy these axioms, and quantify the extent at which the system efficiency is compromised by misreporting agents. We also investigate, in the classic setting, the nature of misreporting agents under a family of proportionally fair mechanisms which are not necessarily strategyproof. These results are supplemented with tight approximation ratio and price of fairness bounds which provide further insight into the compromise between proportional fairness and efficiency in the facility location problem. Finally, we prove basic existence results concerning possible extensions to our settings