The evolutionary game analysis of incentive mechanism for crowd sensing of public environment

• Published in: Journal of physics. Conference series Vol. 1187; no. 5; pp. 52073 - 52080
• Main Authors: Zhang, Qiang, Zhang, Qingqing, Liu, Xueyan, Dai, Jian, Zhang, Xujuan
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.04.2019
• Subjects: Citizen participation; Dynamic stability; Environmental organizations; Evolution; Game theory; Incentives; Perception; Perceptions; Public participation; Strategy
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1187/5/052073

The public environment perception model regards people as a "data perceptron" and a human-centred participatory perception model. The enthusiasm and initiative of public participation will directly determine the effective operation of the model. This paper aims to understand how to stimulate public participation in data sensibility in public environment perception and establish an effective incentive mechanism. Based on the evolutionary game theory, a public environment perception evolutionary game model is established. The game selection between the data subject and the perceived participants is analysed. The group strategy selection and influencing factors of establishing effective incentive mechanism are studied. According to the replication dynamic equation, the behaviour evolution law and evolutionary stability strategy of public environmental group intelligence perception are obtained. The research results show that data users increase the proportion of investment in participants' incentive strategies, which will motivate participants to share more data and thus help them to evolve to the desired results. Encouraging participants to actively share data is necessary to reduce participants' participation costs, which requires data users to select target groups to participate in perceived tasks, and to select people who are more convenient to provide data; the data user's incentives should be sufficient to offset the participant's participation cost. The data user should ensure that both the quantity and quality of the public participation in the perceived task are optimal when the data user minimizes the payment.