Matching markets with historical data are abundant in many applications,
e.g., matching candidates to jobs in hiring, workers to tasks
in crowdsourcing markets, and jobs to servers in cloud services. In
all these applications, a match consumes one or more shared and
limited resources and the goal is to best utilize these to maximize
a global objective. Additionally, one often has historical data and
hence some statistics (usually first-order moments) of the arriving
agents (e.g., candidates, workers, and jobs) can be learnt. To model
these scenarios, we propose a unifying framework, called Multi-
Budgeted Online Assignment with Known Adversarial Distributions.
In this model,we have a set of offline servers with different deadlines
and a set of online job types. At each time, a job of type j arrives.
Assigning this job to a server i yields a profit w(i, j) while consuming
a(i,j) -- a vector lying in [0, 1]^K -- quantities of distinct resources. The goal is to design an (online) assignment policy that maximizes the total expected
profit without violating the (hard) budget constraint. We propose
and theoretically analyze two linear programming (LP) based algorithms
which are almost optimal among all LP-based approaches.
We also propose several heuristics adapted from our algorithms and
compare them to other LP-agnostic algorithms using both synthetic
as well as real-time cloud scheduling and public safety datasets.
Experimental results show that our proposed algorithms are effective
and significantly out-perform the baselines. Moreover, we
show empirically the trade-off between fairness and efficiency of
our algorithms which does well even on fairness metrics without
explicitly optimizing for it.
more »
« less
Fairness Maximization among Offline Agents in Online-Matching Markets
Online matching markets (OMMs) are commonly used in today’s world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents. In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization. We derive further bounds based on fairness parameters, demonstrating conditions under which the competitive ratio can increase to 100%. There are two key ideas helping us break the barrier of 1-1/𝖾~ 63.2% for competitive ratio in online matching. One is boosting , which is to adaptively re-distribute all sampling probabilities among only the available neighbors for every arriving online agent. The other is attenuation , which aims to balance the matching probabilities among offline agents with different mass allocated by the benchmark LP. We conduct extensive numerical experiments and results show that our boosted version of sampling algorithms are not only conceptually easy to implement but also highly effective in practical instances of OMMs where fairness is a concern.
more »
« less
- Award ID(s):
- 1948157
- NSF-PAR ID:
- 10420152
- Date Published:
- Journal Name:
- ACM Transactions on Economics and Computation
- Volume:
- 10
- Issue:
- 4
- ISSN:
- 2167-8375
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Online bipartite matching and allocation models are widely used to analyze and design markets such as Internet advertising, online labor, and crowdsourcing. Traditionally, vertices on one side of the market are fixed and known a priori, while vertices on the other side arrive online and are matched by a central agent to the offline side. The issue of possible conflicts among offline agents emerges in various real scenarios when we need to match each online agent with a set of offline agents. For example, in event-based social networks (e.g., Meetup), offline events conflict for some users since they will be unable to attend mutually-distant events at proximate times; in advertising markets, two competing firms may prefer not to be shown to one user simultaneously; and in online recommendation systems (e.g., Amazon Books), books of the same type “conflict” with each other in some sense due to the diversity requirement for each online buyer. The conflict nature inherent among certain offline agents raises significant challenges in both modeling and online algorithm design. In this paper, we propose a unifying model, generalizing the conflict models proposed in (She et al., TKDE 2016) and (Chen et al., TKDE 16). Our model can capture not only a broad class of conflict constraints on the offline side (which is even allowed to be sensitive to each online agent), but also allows a general arrival pattern for the online side (which is allowed to change over the online phase). We propose an efficient linear programming (LP) based online algorithm and prove theoretically that it has nearly-optimal online performance. Additionally, we propose two LP-based heuristics and test them against two natural baselines on both real and synthetic datasets. Our LP-based heuristics experimentally dominate the baseline algorithms, aligning with our theoretical predictions and supporting our unified approach.more » « less
-
Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 - epsilon for any given epsilon > 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ridesharing systems. We also present heuristics that perform well in practice.more » « less
-
Bipartite-matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite-matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this article, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions ( OM-RR-KAD ) , in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based non-adaptive algorithm that achieves an online competitive ratio of ½-ϵ for any given constant ϵ > 0. We also show that no adaptive algorithm can achieve a ratio of ½ + o (1) based on the same benchmark LP. Through a data-driven analysis on a massive openly available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.more » « less
-
null (Ed.)more » « less
Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective). We proposed an online bipartite-matching model where riders are assumed to arrive sequentially following a distribution known in advance. The highlight of our model is the concept of acceptance rate between any pair of driver-rider types, where types are defined based on demographic factors. Specially, we assume each rider can accept or cancel the driver assigned to her, each occurs with a certain probability which reflects the acceptance degree from the rider type towards the driver type. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit. Experimental results on a real-world dataset are provided as well, which confirm our theoretical predictions.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3569705
