An excitation method of a crowd sensing system based on a behavioral economics preference theory

Abstract

The invention discloses an incentive method of a crowd sensing system based on a behavioral economics preference theory. The incentive method mainly comprises the following steps: 1) issuing a task bya platform; 2) selecting a task by a user; 3) determining a participation plan; 4) selecting a participating user by the platform; 5) reporting perception data by the user; 6) reward payment; Aimingat the platform end, a context effect in behavior economics is introduced into a task release mechanism, and a function of the attraction degree of the task to the user in crowd sensing is established, thereby improving the enthusiasm of the user; Aiming at the user side, a new reward payment mechanism is designed to correct a utility function based on traditional economics, so that the utility ofthe user is improved; According to the theory and experiment about the scene effect and fairness preference in the behavior economics, parameter debugging is carried out on the new model, the platform cost is reduced to a certain degree, the benefits of the platform side and the user side are improved, and therefore the social benefits are further improved.

Description

Technical field [0001] The present invention relates to the technical field of group intelligence perception, in particular to an incentive mechanism and an incentive method that introduces behavioral economics preference theory into a group intelligence perception system. Background technique [0002] Group intelligence perception is a system that combines humans and virtual networks, and acquires perception data by recruiting a large number of participating users. The group intelligence perception system consists of two parts: the platform and the user. On the platform side, it is necessary to recruit a sufficient number of participants to perform the perception task; on the user side, it is necessary to ensure the user's level of participation; whether it is on the platform side or the user side, it is necessary to ensure its utility level, so it is very important to design an incentive mechanism necessary. [0003] In the traditional incentive mechanism, it is usually based o...

AI Technical Summary

Problems solved by technology

However, the situational effect proves that if the added task meets certain conditions, it will increase the probability of a specific task being selected; on the user side, traditional economics considers that the basis for users to make decisions is only the actual rewards obtained by users, while behavioral economics Reciprocal altruistic preferences and fair preferences in learning will also become important factors affecting user decision-making, and sometimes these factors may cause the mechanism to be ineffective, or even counterproductive

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