Evolutionary Game Analysis of Citizen Data Collection Under Different Reward- Penalty Mechanisms
With the advancement of Internet of Things (IoT) technology, both the methods of data collection by citizen data collectors and the willingness of citizens to share data are evolving. To analyze the long-term impact of government reward-penalty mechanisms on citizen data collection, this paper const...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10737347/ |
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| Summary: | With the advancement of Internet of Things (IoT) technology, both the methods of data collection by citizen data collectors and the willingness of citizens to share data are evolving. To analyze the long-term impact of government reward-penalty mechanisms on citizen data collection, this paper constructed three evolutionary game models under scenarios of no reward-penalty, static reward-penalty, and dynamic reward-penalty mechanisms. The focus is on comparing and analyzing evolutionarily stable strategies of a collector and a citizen, followed by computational simulations. Key findings include: 1) Under no reward-penalty and static reward-penalty mechanisms, evolutionarily stable strategies typically result in either consistently active or consistently passive behaviors by both the collector and the citizen. Mixed strategies, which are evolutionarily stable, emerge only under dynamic reward-penalty mechanisms. 2) The balance between the risk associated with data security for the citizen and the benefits they gain from public services significantly influences evolutionary outcomes. 3) Policy directions may be influenced by initial conditions in the pre-collection stage, which can be mitigated by improving the benefit to citizen or reducing the cost during passive data collection. 4) While reward-penalty mechanisms may not directly enhance data collection success rates, they do accelerate the evolutionary process of data collection. |
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| ISSN: | 2169-3536 |