An Empirical Analysis of an Evolutionary Game Theory Model for Trustworthy Information Collection and Distribution

https://doi.org/10.51867/ajernet.6.2.26

Authors

Keywords:

Honest, Dishonest, Evolutionary, Replicator Dynamics, Stable State

Abstract

This paper analyzes the honest-dishonest behavior of cloud data collection and dissemination system users, employing evolutionary game theory. It’s very important to study evolutionary game theory application in cloud data collection and dissemination systems. It tends to describe the trends of honest-dishonest behavior of system stakeholders based on their strategic choices during the game rounds. This study employs involvement and character as criterion to incentivize or reprimand stakeholders. The truthful stakeholder is incentivized while the untruthful stakeholder is penalized. The system is coded using MATLAB software, and several experiments carried out. The system user’s behavior is analyzed using replicator dynamics. The discoveries indicated that regardless of the number of stakeholders selecting an untruthful approach at beginning of the game, the mainstream of stakeholders is encouraged to select a truthful strategy after numerous game rounds. According to a comparative investigation of the evolution dynamics simulation outcomes for information suppliers and users indicated that, ultimately they select honest strategy, and the proportion of honest stabilizes. Consequently, the incentive approach can effectively encourage stakeholders to use the system honestly. The empirically analyzed evolutionary game theory model supports stakeholders' efficient participation and guarantee truthful use of the information collection and dissemination system.

Dimensions

Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6). http://www.wired.com/wired/archive/14.06/crowds_pr.html

Howe, J. (2008). Crowdsourcing: Why the power of the crowd is driving the future of business. New York, NY: Crown Business. http://www.amazon.com/Crowdsourcing-Power-Driving-Future-Business/dp/0307396207

Brabham, D. C. (2009). Crowdsourcing the public participation process for planning projects. Planning Theory, 8(3), 242-262. https://doi.org/10.1177/1473095209104824 DOI: https://doi.org/10.1177/1473095209104824

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189-200. https://doi.org/10.1177/0165551512437638 DOI: https://doi.org/10.1177/0165551512437638

Sanga, C., Masamaki, J., Mlozi, P. M. R. S., & Haug, R. (2016). Crowdsourcing platform 'Ushaurikilimo' enabling questions answering between farmers, extension agents and researchers. International Journal of Instructional Technology and Distance Learning, 13(10), 19-28.

Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (2014). Crowdsourcing applications for public health. American Journal of Preventive Medicine, 46(2), 179-187. https://doi.org/10.1016/j.amepre.2013.10.016 DOI: https://doi.org/10.1016/j.amepre.2013.10.016

Brabham, D. C. (2012). Motivations for participation in a crowdsourcing application to improve public engagement in transit planning. Journal of Applied Communication Research, 40(3), 307-328. https://doi.org/10.1080/00909882.2012.693940 DOI: https://doi.org/10.1080/00909882.2012.693940

Blohm, I., Leimeister, J. M., & Krcmar, H. (2013). Crowdsourcing: How to benefit from (too) many great ideas. MIS Quarterly Executive, 12(4), 199-211.

Pedersen, J., Kocsis, D., Tripathi, A., Tarrell, A., Weerakoon, A., Tahmasbi, N., Xiong, J., Deng, W., Oh, O., & De Vreede, G. J. (2013). Conceptual foundations of crowdsourcing: A review of IS research. Proceedings of the Annual Hawaii International Conference on System Sciences, 579-588. https://doi.org/10.1109/HICSS.2013.143 DOI: https://doi.org/10.1109/HICSS.2013.143

Bayus, B. L. (2013). Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management Science, 59(1), 226-244. https://doi.org/10.1287/mnsc.1120.1599 DOI: https://doi.org/10.1287/mnsc.1120.1599

Zhao, Y., & Zhu, Q. (2014). Evaluation on crowdsourcing research: Current status and future direction. Information Systems Frontiers, 16(3), 417-434. https://doi.org/10.1007/s10796-012-9350-4 DOI: https://doi.org/10.1007/s10796-012-9350-4

Ye, H., & Kankanhalli, A. (2013). Leveraging crowdsourcing for organizational value co-creation. Communications of the Association for Information Systems, 33(1), 225-244. https://doi.org/10.17705/1cais.03313 DOI: https://doi.org/10.17705/1CAIS.03313

Di, P. M., Wasko, M. M., & Hooker, R. E. (2010). Getting customers' ideas to work for you: Learning from Dell how to succeed with online user innovation communities. MIS Quarterly Executive, 9(4), 213-228.

Djelassi, S., & Decoopman, I. (2013). Customers' participation in product development through crowdsourcing: Issues and implications. Industrial Marketing Management, 42(5), 683-692. https://doi.org/10.1016/j.indmarman.2013.05.006 DOI: https://doi.org/10.1016/j.indmarman.2013.05.006

Terwiesch, C., & Xu, Y. (2008). Innovation contests, open innovation, and multiagent problem solving. Management Science, 54(9), 1529-1543. https://doi.org/10.1287/mnsc.1080.0884 DOI: https://doi.org/10.1287/mnsc.1080.0884

Blohm, I., Zogaj, S., Bretschneider, U., & Leimeister, J. M. (2018). How to manage crowdsourcing platforms effectively? California Management Review, 60(2), 122-149. https://doi.org/10.1177/0008125617738255 DOI: https://doi.org/10.1177/0008125617738255

Zheng, H., Li, D., Wu, J., & Xu, Y. (2014). The role of multidimensional social capital in crowdfunding: A comparative study in China and US. Information & Management, 51(4), 488-496. https://doi.org/10.1016/j.im.2014.03.003 DOI: https://doi.org/10.1016/j.im.2014.03.003

Morgan, J., & Wang, R. (2010). Tournaments for ideas. California Management Review, 52(2), 76-98.

https://doi.org/10.1525/cmr.2010.52.2.77 DOI: https://doi.org/10.1525/cmr.2010.52.2.77

Bullinger, A. C., Neyer, A. K., Rass, M., & Moeslein, K. M. (2010). Community-based innovation contests: Where competition meets cooperation. Creativity and Innovation Management, 19(3), 290-303. https://doi.org/10.1111/j.1467-8691.2010.00565.x DOI: https://doi.org/10.1111/j.1467-8691.2010.00565.x

Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., & Zeinalipour-Yazti, D. (2012). Crowdsourcing with smartphones. IEEE Internet Computing, 16(5), 36-44. https://doi.org/10.1109/MIC.2012.70 DOI: https://doi.org/10.1109/MIC.2012.70

Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the World-Wide Web. Communications of the ACM, 54(4), 86-96. https://doi.org/10.1145/1924421.1924442 DOI: https://doi.org/10.1145/1924421.1924442

Alt, F., Shirazi, A. S., Schmidt, A., Kramer, U., & Nawaz, Z. (2010). Location-based crowdsourcing: Extending crowdsourcing to the real world. Proceedings of the 6th Nordic Conference on Human-Computer Interaction, 13-22. https://doi.org/10.1145/1868914.1868921 DOI: https://doi.org/10.1145/1868914.1868921

Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., & Mao, X. (2016). Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys and Tutorials, 18(1), 54-67. https://doi.org/10.1109/COMST.2015.2415528 DOI: https://doi.org/10.1109/COMST.2015.2415528

Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., & Li, X. (2014). Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3190-3200. https://doi.org/10.1109/TPDS.2013.2297112 DOI: https://doi.org/10.1109/TPDS.2013.2297112

Zhang, Q., Wen, Y., Tian, X., Gan, X., & Wang, X. (2015). Incentivize crowd labeling under budget constraint. Proceedings - IEEE INFOCOM, 2812-2820. https://doi.org/10.1109/INFOCOM.2015.7218674 DOI: https://doi.org/10.1109/INFOCOM.2015.7218674

Wen, Y., Shi, J., Zhang, Q., Tian, X., Huang, Z., Yu, H., Cheng, Y., & Shen, X. (2015). Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Transactions on Vehicular Technology, 64(9), 4203-4214. https://doi.org/10.1109/TVT.2014.2363842 DOI: https://doi.org/10.1109/TVT.2014.2363842

Kusyama, S. L., Machuve, D., Kisangiri, M., & Mfanga, A. (2020). Participation-reputation based incentive game model (PRIGM) for trustworthy fisheries information collection and dissemination framework. International Journal of Advanced Technology and Engineering Exploration, 7(70), 137-146. https://doi.org/10.19101/IJATEE.2020.762060 DOI: https://doi.org/10.19101/IJATEE.2020.762060

Ma, X., Ma, J., Li, H., Jiang, Q., & Gao, S. (2016). RTRC: A reputation-based incentive game model for trustworthy crowdsourcing service. China Communications, 13(3), 199-215. https://doi.org/10.1109/CC.2016.7445512 DOI: https://doi.org/10.1109/CC.2016.7897544

Published

2025-05-01

How to Cite

Kusyama, S. L. (2025). An Empirical Analysis of an Evolutionary Game Theory Model for Trustworthy Information Collection and Distribution. African Journal of Empirical Research, 6(2), 297–314. https://doi.org/10.51867/ajernet.6.2.26