Name
Abstract | ||
|---|---|---|
Recently, crowdsourcing system has emerged as an effective platform for performing tasks. However, the workers in crowdsourcing systems are selfish and aim to strategically maximize their own benefit, which damages the system performance. In this paper, we model the interaction between workers as an iterated game to attract the participation of workers with high-quality services. We propose two zero-determinant strategies for one worker who is able to maintain the social welfare at a desired value whatever the behaviors of the opponents, and find the conditions to reach the maximum social welfare. Numerical illustrations verify the performance of the proposed two zerodeterminant strategies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/DSC.2017.42 | 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC) |
Keywords | Field | DocType |
Crowdsourcing,Game Theory,Network Wisdom,Zero-determinant Strategy | Markov process,Wireless,Damages,Numerical models,Computer security,Crowdsourcing,Computer science,Repeated game,Enforcement,Social Welfare | Conference |
ISBN | Citations | PageRank |
978-1-5386-1601-7 | 0 | 0.34 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Miao | 1 | 0 | 0.34 |
| Changbing Tang | 2 | 36 | 8.07 |
| Jianfeng Lu | 3 | 26 | 7.61 |
| Xiang Li | 4 | 81 | 40.11 |