TY - GEN
T1 - Crowdsourcing with heterogeneous workers in social networks
AU - Huang, Chao
AU - Yu, Haoran
AU - Huang, Jianwei
AU - Berry, Randall A.
N1 - Funding Information:
Chao Huang is with the Department of Information Engineering, the Chinese University of Hong Kong. Email: [email protected]. Jianwei Huang is a Presidential Chair Professor and the Associate Dean of the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. He is also the Associate Director of Shenzhen Institute of Artificial Intelligence and Robotics for Society, and a Professor in the Department of Information Engineering, The Chinese University of Hong Kong. Email: [email protected]. Haoran Yu and Randall A Berry are with Department of Electrical and Computer Engineering, Northwestern University. Email: [email protected], [email protected]. This work is supported by the General Research Fund CUHK 14219016 from Hong Kong UGC, Presidential Fund from the Chinese University of Hong Kong, Shenzhen, and the Shenzhen Institute of Artificial Intelligence and Robotics for Society.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.
AB - Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.
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U2 - 10.1109/GLOBECOM38437.2019.9013519
DO - 10.1109/GLOBECOM38437.2019.9013519
M3 - Conference contribution
AN - SCOPUS:85081959196
T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -