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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.
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This content will become publicly available on March 3, 2026
Where the Rubber Meets the Road: Examining Efficiency and Equity in Designing Summer Youth Employment Programs
Summer Youth Employment Programs are known to significantly impact youth outcomes based on lotteries from oversubscribed programs. But most cities cannot use a lottery design due to heterogeneity across youth and jobs. How can programs achieve efficiency and equity under alternative assignment mechanisms? Using hiring platform data, we study youth applications and employer selection behavior to explore these design challenges. We find large mismatches between the distribution of youth versus jobs, leaving 10% to 25% of positions unfilled. Moreover, employers were nearly twice as likely to select white youth relative to their representation in the applicant pool. This disparity persisted when controlling for other demographics, the number and timing of applications, and job readiness. Our findings reveal that workforce development programs may perpetuate inequities without simple random assignment.
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- Award ID(s):
- 2244340
- PAR ID:
- 10598793
- Publisher / Repository:
- IZA Institute of Labor Economics
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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