Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach -the Reputation-aware Task Subdelegation approach with dynamic worker effort Pricing (RTS-P) -with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers' current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches. The organization of social and economic activities to efficiently coordinate participants' effort is an important topic of economic theory. Thanks to the Internet, social media and online social networks, social mobilization through crowdsourcing has achieved unprecedented success. Crowdsourcing refers to the process whereby clients (a.k.a. crowdsourcers) obtain needed services by soliciting contributions from a large group of people (a.k.a. 1 . Crowdsourcing communities based around social networks tend to have hierarchical structures 2,3 . These hierarchical crowdsourcing networks (HCNs) have been used to mobilize the masses in many significant real-world applications including political rallies 4 , scientific research 5 , mapping out natural environment features 6, 7 , and large-scale search-and-rescue missions 8 . In essence, crowdsourcing systems can be treated as resource allocation ecosystems containing a large number of interacting workers (i.e., resources) and crowdsourcers. Crowdsourcers are typically self-interested; their primary intention is to maximize their own utilities. This will usually lead them to only select workers with high perceived reputation, leading to the emergence of herding 9 . Herding refers to the situation in which a large number of task requests concentrate on a small group of reputable workers, causing them to be overloaded while leaving other workers idle. It can lead to cascading failures and eventually result in catastrophic system breakdown 10 . The risk of herding is especially pronounced in HCNs in which crowdsourcers lack global knowledge and workers have limited resources to be tapped into 11 . Mitigating herding in HCNs is important to ensure sustainable operation of these problem solving ecosystems 12 . In general, workers in an HCN make three important decisions in a distributed manner: 1) how much new workload to accept, 2) how much existing workload to sub-delegate to others in the HCN (and to whom), and 3) how to price their services. The collective effect of these joint decisions made by all HCN participants determines
Han et al. REF proposed a Lyapunov optimization based on a decision support approach, the reputation-aware task subdelegation approach with dynamic worker effort pricing (RTS-P) to address the fact that spontaneous evolution of the complex resource allocation dynamics may lead to undesirable herding behaviors.
Mitigating Herding in Hierarchical Crowdsourcing Networks
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