Truthful incentives in crowdsourcing tasks using regret minimization mechanisms

Singla, Adish; Krause, Andreas

doi:10.1145/2488388.2488490

Cited by 234 publications

(164 citation statements)

References 23 publications

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“…Several recent papers (e.g., [Abraham et al 2013;Badanidiyuru et al 2012Badanidiyuru et al , 2013Ho et al 2013;Singla and Krause 2013]) have drawn a connection between MAB models and crowd-sourcing of tasks. Contrary to our model, these papers typically treat the agents as arms in the MAB instance, i.e., the algorithm's goal is to learn the quality of the agents' work from observing them while providing enough incentives for them to work.…”

Section: Related Workmentioning

confidence: 99%

Incentivizing exploration

Frazier

Kempe

Kleinberg

et al. 2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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We study a Bayesian multi-armed bandit (MAB) setting in which a principal seeks to maximize the sum of expected time-discounted rewards obtained by pulling arms, when the arms are actually pulled by selfish and myopic individuals. Since such individuals pull the arm with highest expected posterior reward (i.e., they always exploit and never explore), the principal must incentivize them to explore by offering suitable payments. Among others, this setting models crowdsourced information discovery and funding agencies incentivizing scientists to perform high-risk, high-reward research.We explore the tradeoff between the principal's total expected time-discounted incentive payments, and the total time-discounted rewards realized. Specifically, with a time-discount factor γ ∈ (0, 1), let OPT denote the total expected time-discounted reward achievable by a principal who pulls arms directly in a MAB problem, without having to incentivize selfish agents. We call a pair (ρ, b) ∈ [0, 1] 2 consisting of a reward ρ and payment b achievable if for every MAB instance, using expected time-discounted payments of at most b·OPT, the principal can guarantee an expected time-discounted reward of at least ρ·OPT. Our main result is an essentially complete characterization of achievable (payment, reward) pairs: ifis achievable, and ifIn proving this characterization, we analyze so-called time-expanded policies, which in each step let the agents choose myopically with some probability p, and incentivize them to choose "optimally" with probability 1 − p. The analysis of time-expanded policies leads to a question that may be of independent interest: If the same MAB instance (without selfish agents) is considered under two different time-discount rates γ > η, how small can the ratio of OPTη to OPTγ be? We give a complete answer to this question, showing that OPTη ≥(1−γ) 2(1−η) 2 · OPTγ , and that this bound is tight.

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Section: Related Workmentioning

confidence: 99%

Incentivizing exploration

Frazier

Kempe

Kleinberg

et al. 2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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“…Many pieces of works have specially studied the incentive mechanism in participatory sensing [10][11][12][13]. Most of existing incentive mechanisms mainly focus on utility maximization and cost minimization of the server.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

Niu¹,

Wang²,

Ye³

et al. 2018

Security and Communication Networks

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The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: "privacy" and "incentive" which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

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“…Optimal payment strategies, reward schemes, and incentive mechanisms for crowdsourcing have been studied [19,29] and may also be applied in combination to crowdsourcing scheduling techniques in order to maximize throughput as well as the quality of the results.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems

Difallah

Demartini

Cudré-Mauroux

2016

Proceedings of the 25th International Conference on World Wide Web

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Micro-task crowdsourcing has become a popular approach to e↵ectively tackle complex data management problems such as data linkage, missing values, or schema matching. However, the backend crowdsourced operators of crowd-powered systems typically yield higher latencies than the machineprocessable operators, this is mainly due to inherent efficiency di↵erences between humans and machines. This problem can be further exacerbated by the lack of workers on the target crowdsourcing platform, or when the workers are shared unequally among a number of competing requesters; including the concurrent users from the same organization who execute crowdsourced queries with di↵erent types, priorities and prices. Under such conditions, a crowd-powered system acts mostly as a proxy to the crowdsourcing platform, and hence it is very di cult to provide e ency guarantees to its end-users.Scheduling is the traditional way of tackling such problems in computer science, by prioritizing access to shared resources. In this paper, we propose a new crowdsourcing system architecture that leverages scheduling algorithms to optimize task execution in a shared resources environment, in this case a crowdsourcing platform. Our study aims at assessing the e ciency of the crowd in settings where multiple types of tasks are run concurrently. We present extensive experimental results comparing i) di↵erent multi-tenant crowdsourcing jobs, including a workload derived from real traces, and ii) di↵erent scheduling techniques tested with real crowd workers. Our experimental results show that task scheduling can be leveraged to achieve fairness and reduce query latency in multi-tenant crowd-powered systems, although with very di↵erent tradeo↵s compared to traditional settings not including human factors. General Terms

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Truthful incentives in crowdsourcing tasks using regret minimization mechanisms

Cited by 234 publications

References 23 publications

Incentivizing exploration

Incentivizing exploration

A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems

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