Abstract
We consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical Turk, although additional sophistication to pricing rule can enhance budget efficiency. With the goal of designing efficient and simple pricing rules, we study the impact of the following two design features in pricing policies: (i) personalization tailoring policy worker-by-worker and (ii) bonus payment to qualified task completion. In the Bayesian setting, where the only prior distribution of workers' profiles is available, we first study the Price of Agnosticism (PoA) that quantifies the utility gap between personalized and common pricing policies. We show that PoA is bounded within a constant factor under some mild conditions, and the impact of bonus is essential in common pricing. These analytic results imply that complex personalized pricing can be replaced by simple common pricing once it is equipped with a proper bonus payment. To provide insights on efficient common pricing, we then study the efficient mechanisms of bonus payment for several profile distribution regimes which may exist in practice. We provide primitive experiments on Amazon Mechanical Turk, which support our analytical findings.
- Vineet Abhishek, Ian A Kash, and Peter Key. 2012. Fixed and market pricing for cloud services. In 2012 Proceedings IEEE INFOCOM Workshops. IEEE, 157--162.Google Scholar
Cross Ref
- Georgios Amanatidis, Pieter Kleer, and Guido Sch"afer. 2019. Budget-feasible mechanism design for non-monotone submodular objectives: Offline and online. In Proceedings of the 2019 ACM Conference on Economics and Computation .Google Scholar
Digital Library
- Nima Anari, Gagan Goel, and Afshin Nikzad. 2014. Mechanism design for crowdsourcing: An optimal 1--1/e competitive budget-feasible mechanism for large markets. In Proc. of FOCS .Google Scholar
Digital Library
- Eric T Anderson and Duncan I Simester. 2010. Price stickiness and customer antagonism. The quarterly journal of economics , Vol. 125, 2 (2010), 729--765.Google Scholar
- Moshe Babaioff, Michal Feldman, and Noam Nisan. 2006. Combinatorial agency. In Proceedings of the 7th ACM Conference on Electronic Commerce. 18--28.Google Scholar
Digital Library
- Moshe Babaioff, Yannai A Gonczarowski, and Noam Nisan. 2021. The menu-size complexity of revenue approximation. Games and Economic Behavior (2021).Google Scholar
- Eric Balkanski and Jason D Hartline. 2016. Bayesian budget feasibility with posted pricing. In Proc. of WWW .Google Scholar
Digital Library
- Siddhartha Banerjee, Ramesh Johari, and Carlos Riquelme. 2015. Pricing in ride-sharing platforms: A queueing-theoretic approach. In Proceedings of the Sixteenth ACM Conference on Economics and Computation . 639--639.Google Scholar
Digital Library
- Xiaohui Bei, Ning Chen, Nick Gravin, and Pinyan Lu. 2012. Budget feasible mechanism design: from prior-free to bayesian. In Proc. of STOC .Google Scholar
Digital Library
- Dirk Bergemann, Benjamin Brooks, and Stephen Morris. 2015. The limits of price discrimination. American Economic Review , Vol. 105, 3 (2015), 921--57.Google Scholar
Cross Ref
- Dirk Bergemann, Francisco Castro, and Gabriel Weintraub. 2019. Third-degree Price Discrimination Versus Uniform Pricing. arXiv preprint arXiv:1912.05164 (2019).Google Scholar
- Satyanath Bhat, Swaprava Nath, Sujit Gujar, Onno Zoeter, Yadati Narahari, and Chris Dance. 2014. A mechanism to optimally balance cost and quality of labeling tasks outsourced to strategic agents. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. 917--924.Google Scholar
Digital Library
- Michael Buhrmester, Tracy Kwang, and Samuel D Gosling. 2011. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on psychological science (2011).Google Scholar
- Colin F Camerer. 2011. Behavioral game theory: Experiments in strategic interaction .Princeton university press.Google Scholar
- Gabriel Carroll. 2015. Robustness and linear contracts. American Economic Review (2015).Google Scholar
- Juan Camilo Castillo, Dan Knoepfle, and Glen Weyl. 2017. Surge pricing solves the wild goose chase. In Proceedings of the 2017 ACM Conference on Economics and Computation. 241--242.Google Scholar
Digital Library
- Shuchi Chawla, Jason D Hartline, David Malec, and Balasubramanian Sivan. 2010. Sequential posted pricing and multi-parameter mechanism design. In Proc. of STOC .Google Scholar
- Shuchi Chawla, Yifeng Teng, and Christos Tzamos. 2020. Menu-size complexity and revenue continuity of buy-many mechanisms. In Proceedings of the 21st ACM Conference on Economics and Computation. 475--476.Google Scholar
Digital Library
- Xi Chen, Ilias Diakonikolas, Anthi Orfanou, Dimitris Paparas, Xiaorui Sun, and Mihalis Yannakakis. 2015. On the complexity of optimal lottery pricing and randomized mechanisms. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science. IEEE, 1464--1479.Google Scholar
Digital Library
- Yan Chen, Teck-HUa Ho, and Yong-Mi Kim. 2010. Knowledge market design: A field experiment at Google Answers. Journal of Public Economic Theory (2010).Google Scholar
- Yiling Chen, Yiheng Shen, and Shuran Zheng. 2020. Truthful Data Acquisition via Peer Prediction. arXiv preprint arXiv:2006.03992 (2020).Google Scholar
- Yanjiao Chen, Xu Wang, Baochun Li, and Qian Zhang. 2019. An incentive mechanism for crowdsourcing systems with network effects. ACM Transactions on Internet Technology (TOIT) , Vol. 19, 4 (2019), 1--21.Google Scholar
Digital Library
- Vincent P Crawford. 1997. Theory and experiment in the analysis of strategic interaction. Econometric Society Monographs , Vol. 26 (1997), 206--242.Google Scholar
- Rachel Cummings, Nikhil R Devanur, Zhiyi Huang, and Xiangning Wang. 2020. Algorithmic price discrimination. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 2432--2451.Google Scholar
Cross Ref
- George B Dantzig. 1957. Discrete-variable extremum problems. Operations research (1957).Google Scholar
- Alexander Philip Dawid and Allan M Skene. 1979. Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied statistics (1979).Google Scholar
- Ludwig Dierks and Sven Seuken. 2021. Cloud pricing: the spot market strikes back. Management Science (2021).Google Scholar
- Lisa Drayer. 2018. Is sparkling water as hydrating as regular water? https://edition.cnn.com/2018/07/19/health/sparkling-water-hydration-drayer/index.html .Google Scholar
- David Easley and Arpita Ghosh. 2015. Behavioral mechanism design: Optimal crowdsourcing contracts and prospect theory. In Proceedings of the Sixteenth ACM Conference on Economics and Computation . 679--696.Google Scholar
Digital Library
- Liran Einav, Chiara Farronato, Jonathan Levin, and Neel Sundaresan. 2018. Auctions versus posted prices in online markets. Journal of Political Economy , Vol. 126, 1 (2018), 178--215.Google Scholar
Cross Ref
- Adam N Elmachtoub, Vishal Gupta, and Michael Hamilton. 2018. The value of personalized pricing. Available at SSRN 3127719 (2018).Google Scholar
- Michal Feldman, Christos Papadimitriou, John Chuang, and Ion Stoica. 2006. Free-riding and whitewashing in peer-to-peer systems. IEEE Journal on selected areas in communications (2006).Google Scholar
Digital Library
- Arpita Ghosh and Robert Kleinberg. 2014. Optimal contest design for simple agents. In Proceedings of the fifteenth ACM conference on Economics and computation. 913--930.Google Scholar
Digital Library
- Shuo Guo, Liang He, Yu Gu, Bo Jiang, and Tian He. 2014. Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. IEEE Trans. Comput. (2014).Google Scholar
Digital Library
- Kai Han, Yuntian He, Haisheng Tan, Shaojie Tang, He Huang, and Jun Luo. 2017. Online Pricing for Mobile Crowdsourcing with Multi-Minded Users. In Proc. of Mobihoc .Google Scholar
Digital Library
- Kai Han, He Huang, and Jun Luo. 2016. Posted pricing for robust crowdsensing. In Proc. of Mobihoc .Google Scholar
Digital Library
- Kai Han, He Huang, and Jun Luo. 2018. Quality-aware pricing for mobile crowdsensing. IEEE/ACM Transactions on Networking , Vol. 26, 4 (2018), 1728--1741.Google Scholar
Digital Library
- Chien-Ju Ho, Rafael Frongillo, and Yiling Chen. 2016a. Eliciting categorical data for optimal aggregation. In Proceedings of the 30th International Conference on Neural Information Processing Systems . 2450--2458.Google Scholar
- Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, and Jennifer Wortman Vaughan. 2015. Incentivizing high quality crowdwork. In Proc. of WWW .Google Scholar
Digital Library
- Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan. 2016b. Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems. Journal of Artificial Intelligence Research , Vol. 55 (2016), 317--359.Google Scholar
Digital Library
- Jiali Huang, Ankur Mani, and Zizhuo Wang. 2019. The value of price discrimination in large random networks. In Proceedings of the 2019 ACM Conference on Economics and Computation. 243--244.Google Scholar
Digital Library
- Grace YoungJoo Jeon, Yong-Mi Kim, and Yan Chen. 2010. Re-examining price as a predictor of answer quality in an online Q&A site. In Proceedings of the SIGCHI conference on human factors in computing systems . 325--328.Google Scholar
Digital Library
- Daniel Kahneman and Amos Tversky. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I. World Scientific, 99--127.Google Scholar
- David R Karger, Sewoong Oh, and Devavrat Shah. 2014. Budget-optimal task allocation for reliable crowdsourcing systems. Operations Research , Vol. 62, 1 (2014), 1--24.Google Scholar
Digital Library
- Robert E Kraut and Paul Resnick. 2012. Building successful online communities: Evidence-based social design .Mit Press.Google Scholar
Digital Library
- Jean-Jacques Laffont and David Martimort. 2009. The theory of incentives: the principal-agent model .Princeton university press.Google Scholar
- Armando Levy and Tomislav Vukina. 2002. Optimal linear contracts with heterogeneous agents. European Review of Agricultural Economics , Vol. 29, 2 (2002), 205--217.Google Scholar
Cross Ref
- Leib Litman, Jonathan Robinson, and Cheskie Rosenzweig. 2015. The relationship between motivation, monetary compensation, and data quality among US-and India-based workers on Mechanical Turk. Behavior research methods (2015).Google Scholar
- Yang Liu and Yiling Chen. 2016. Learning to incentivize: eliciting effort via output agreement. In Proc. of AAAI .Google Scholar
- Qian Ma, Lin Gao, Ya-Feng Liu, and Jianwei Huang. 2018. Incentivizing Wi-Fi network crowdsourcing: A contract theoretic approach. IEEE/ACM Transactions on Networking , Vol. 26, 3 (2018), 1035--1048.Google Scholar
Digital Library
- Winter Mason and Duncan J Watts. 2009. Financial incentives and the" performance of crowds". In Proceedings of the ACM SIGKDD workshop on human computation. 77--85.Google Scholar
Digital Library
- Winter Mason and Duncan J Watts. 2010. Financial incentives and the performance of crowds. ACM SigKDD Explorations Newsletter (2010).Google Scholar
- MoralMachine. 2018. https://www.moralmachine.net/.Google Scholar
- Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, and Yung Yi. 2019. Iterative bayesian learning for crowdsourced regression. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1486--1495.Google Scholar
- Jungseul Ok, Sewoong Oh, Jinwoo Shin, and Yung Yi. 2016. Optimality of belief propagation for crowdsourced classification. In Proc. of ICML .Google Scholar
- Goran Radanovic, Boi Faltings, and Radu Jurca. 2016. Incentives for effort in crowdsourcing using the peer truth serum. Proc. of TIST (2016).Google Scholar
Digital Library
- Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010. Learning from crowds. Journal of Machine Learning Research (2010).Google Scholar
Digital Library
- Shreyas Sekar. 2016. Posted pricing sans discrimination. arXiv preprint arXiv:1609.06844 (2016).Google Scholar
- SETI. 2018. https://www.seti.org/.Google Scholar
- Benson Shapiro. 2002. Is Performance-Based Pricing the Right Price for You? Verfügbar: http://hbswk. hbs. edu/item/3021. html (Zugriff am 29.03. 2007, Erstellung am 22.07. 2002) (2002).Google Scholar
- Yaron Singer. 2010. Budget feasible mechanisms. In Proc. of FOCS .Google Scholar
Digital Library
- Jiayi Song and Roch Guérin. 2020. Pricing (and bidding) strategies for delay differentiated cloud services. ACM Transactions on Economics and Computation (TEAC) , Vol. 8, 2 (2020), 1--58.Google Scholar
Digital Library
- Jiajun Sun and Huadong Ma. 2014. Collection-behavior based multi-parameter posted pricing mechanism for crowd sensing. In Proc. of ICC .Google Scholar
Cross Ref
- Yongxin Tong, Libin Wang, Zimu Zhou, Lei Chen, Bowen Du, and Jieping Ye. 2018. Dynamic pricing in spatial crowdsourcing: A matching-based approach. In Proceedings of the 2018 International Conference on Management of Data . 773--788.Google Scholar
Digital Library
- Amazon Mechanical Turk. 2018. https://www.mturk.com/mturk/welcome.htm .Google Scholar
- Jan Vondrák, Chandra Chekuri, and Rico Zenklusen. 2011. Submodular function maximization via the multilinear relaxation and contention resolution schemes. In Proc. of STOC .Google Scholar
Digital Library
- Jie Zhang Zehong Hu. 2017. Optimal Posted-Price Mechanism in Microtask Crowdsourcing. In Proc. of IJCAI .Google Scholar
- Liang Zheng, Carlee Joe-Wong, Christopher G Brinton, Chee Wei Tan, Sangtae Ha, and Mung Chiang. 2016. On the viability of a cloud virtual service provider. ACM SIGMETRICS Performance Evaluation Review , Vol. 44, 1 (2016), 235--248.Google Scholar
Digital Library
- Liang Zheng, Carlee Joe-Wong, Chee Wei Tan, Mung Chiang, and Xinyu Wang. 2015. How to bid the cloud. ACM SIGCOMM Computer Communication Review , Vol. 45, 4 (2015), 71--84.Google Scholar
Digital Library
Index Terms
Power of Bonus in Pricing for Crowdsourcing
Recommendations
Power of Bonus in Pricing for Crowdsourcing
SIGMETRICS/PERFORMANCE '22: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer SystemsWe consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical ...
Power of Bonus in Pricing for Crowdsourcing
SIGMETRICS '22We consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical ...
Contingent Pricing to Reduce Price Risks
The price for a product may be set too low, causing the seller to leave money on the table, or too high, driving away potential buyers. Contingent pricing can be useful in mitigating these problems. In contingent pricing arrangements, price is ...






Comments