In this paper, we consider a setting inspired by spatial crowdsourcing platforms, where both workers and tasks arrive at different times, and each worker-task assignment yields a given reward. The key challenge is to address the uncertainty in the stochastic arrivals from both workers and the tasks. In this work, we consider a ubiquitous scenario where the arrival patterns of worker “types” and task “types” are not erratic but can be predicted from historical data. Specifically, we consider a finite time horizon T and assume that in each time-step the arrival of a worker and a task can be seen as an independent sample from two (different) distributions. Our model, called "Online Task Assignment with Two-Sided Arrival" (OTA-TSA), is a significant generalization of the classical online task-assignment problem when all the tasks are statically available. For the general case of OTA-TSA, we present an optimal non-adaptive algorithm (NADAP), which achieves a competitive ratio (CR) of at least 0.295. For a special case of OTA-TSA when the reward depends only on the worker type, we present two adaptive algorithms, which achieve CRs of at least 0.343 and 0.355, respectively. On the hardness side, we show that (1) no non-adaptive can achieve a CR larger than that of NADAP, establishing the optimality of NADAP among all non-adaptive algorithms; and (2) no (adaptive) algorithm can achieve a CR better than 0.581 (unconditionally) or 0.423 (conditionally on the benchmark linear program), respectively. All aforementioned negative results apply to even unweighted OTA-TSA when every assignment yields a uniform reward. At the heart of our analysis is a new technical tool, called "two-stage birth-death process", which is a refined notion of the classical birth-death process. We believe it may be of independent interest. Finally, we perform extensive numerical experiments on a real-world ride-share dataset collected in Chicago and a synthetic dataset, and results demonstrate the effectiveness of our proposed algorithms in practice.
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On Benchmarking for Crowdsourcing and Future of Work Platform
Online crowdsourcing platforms have proliferated over the last few years and cover a number of important domains, these platforms include worker-task platforms such as Amazon Mechanical Turk, worker-for hire platforms such as TaskRabbit to specialized platforms with specific tasks such as ridesharing like Uber, Lyft, Ola, etc. An increasing proportion of the human workforce will be employed by these platforms in the near future. The crowdsourcing community has done yeoman’s work in designing effective algorithms
for various key components, such as incentive design, task assignment, and quality control. Given the increasing importance of these crowdsourcing platforms, it is now time to design mechanisms so that it is easier to evaluate the effectiveness of these platforms. Specifically, we advocate developing benchmarks for crowdsourcing research. Benchmarks often identify important issues for the community to focus on and improve upon. This has played a key role in the development of research domains as diverse as databases and deep learning. We believe that developing appropriate benchmarks for crowdsourcing will ignite further innovations. However, crowdsourcing – and future of work, in general – is a very diverse field that makes developing benchmarks much more challenging. Substantial effort is needed that spans across developing benchmarks for datasets, metrics, algorithms, platforms, and so on. In this article, we initiate some discussion into this important problem and issue a call-to-arms for the community to work on this important initiative.
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- Award ID(s):
- 1840052
- NSF-PAR ID:
- 10187036
- Date Published:
- Journal Name:
- A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering
- ISSN:
- 1053-1238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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