关键词: 社群智能, 信誉度模型, 参与式激励, 多维反向拍卖, 博弈论

Abstract: The sustained participation and service reliability provided by the node are essential to the data collection service provided by the social and community intelligence system. This paper proposes a reputation-based participatory incentive mechanism (RPIM) to promote the reliability of the collecting data and ensure the enthusiasm and persistence on the participation of the nodes. The proposed mechanism evaluates participants in terms of data reliability and bidding reliability to create a reputation model based on game theory. The incentive mechanism based on such reputation model motivates participants to collect reliable data in social and community intelligence systems, while minimizing incentive cost for maintaining the sufficient number of reliable participants. Simulations are conducted in different scenarios to test the performance of RPIM. The results show that RPIM remarkably increases the winning probability of participants who provide accurate data and reduces the cost for retaining the sufficient number of participants.

Key words: social and community intelligence, reputation model, participatory incentive, multidimensional reverse auction, game theory