Facility Location Games with Scaling Effects
- URL: http://arxiv.org/abs/2402.18908v1
- Date: Thu, 29 Feb 2024 07:08:18 GMT
- Title: Facility Location Games with Scaling Effects
- Authors: Yu He, Alexander Lam and Minming Li
- Abstract summary: We take the classic facility location problem and consider a variation in which each agent's individual cost function is equal to their distance from the facility multiplied by a scaling factor.
We find results on the total and maximum cost approximation ratios achievable by strategyproof and anonymous mechanisms.
- Score: 69.28397508730046
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We take the classic facility location problem and consider a variation, in
which each agent's individual cost function is equal to their distance from the
facility multiplied by a scaling factor which is determined by the facility
placement. In addition to the general class of continuous scaling functions, we
also provide results for piecewise linear scaling functions which can
effectively approximate or model the scaling of many real world scenarios. We
focus on the objectives of total and maximum cost, describing the computation
of the optimal solution. We then move to the approximate mechanism design
setting, observing that the agents' preferences may no longer be single-peaked.
Consequently, we characterize the conditions on scaling functions which ensure
that agents have single-peaked preferences. Under these conditions, we find
results on the total and maximum cost approximation ratios achievable by
strategyproof and anonymous mechanisms.
Related papers
- Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence [51.54175067684008]
This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks.
We first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes.
Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods.
arXiv Detail & Related papers (2024-03-17T07:02:55Z) - An Algorithm and Complexity Results for Causal Unit Selection [16.307996243413967]
Unit selection problem aims to identify objects, called units, that are most likely to exhibit a desired mode of behavior when subjected to stimuli.
We propose the first exact algorithm for finding optimal units given a broad class of causal objective functions and a fully specified structural causal model.
arXiv Detail & Related papers (2023-02-28T08:46:51Z) - Variable Importance Matching for Causal Inference [73.25504313552516]
We describe a general framework called Model-to-Match that achieves these goals.
Model-to-Match uses variable importance measurements to construct a distance metric.
We operationalize the Model-to-Match framework with LASSO.
arXiv Detail & Related papers (2023-02-23T00:43:03Z) - Adaptive LASSO estimation for functional hidden dynamic geostatistical
model [69.10717733870575]
We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hiddenstatistical models (f-HD)
The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (GMSOLAS) penalty function, wherein the weights are obtained by the unpenalised f-HD maximum-likelihood estimators.
arXiv Detail & Related papers (2022-08-10T19:17:45Z) - Bayesian Optimization with Informative Covariance [13.113313427848828]
We propose novel informative covariance functions for optimization, leveraging nonstationarity to encode preferences for certain regions of the search space.
We demonstrate that the proposed functions can increase the sample efficiency of Bayesian optimization in high dimensions, even under weak prior information.
arXiv Detail & Related papers (2022-08-04T15:05:11Z) - Facility Location Games Beyond Single-Peakedness: the Entrance Fee Model [18.86193543684732]
We introduce a novel model where each facility charges an entrance fee.
In our model, the entrance fee function is allowed to be an arbitrary function.
We design strategyproof mechanisms with favorable approximation ratios.
arXiv Detail & Related papers (2022-04-24T13:56:35Z) - Top-$k$ Regularization for Supervised Feature Selection [11.927046591097623]
We introduce a novel, simple yet effective regularization approach, named top-$k$ regularization, to supervised feature selection.
We show that the top-$k$ regularization is effective and stable for supervised feature selection.
arXiv Detail & Related papers (2021-06-04T01:12:47Z) - Automated Mechanism Design for Classification with Partial Verification [64.69418921224529]
We study the problem of automated mechanism design with partial verification.
We focus on truthful mechanisms in the setting where all types share the same preference over outcomes.
arXiv Detail & Related papers (2021-04-12T03:29:31Z) - VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit
Feedback [104.06766271716774]
We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values.
We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism.
Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
arXiv Detail & Related papers (2020-04-19T18:00:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.