Abstract
The existing incentive mechanisms of crowdsourcing construct the expected utility function based on the assumption of rational people in traditional economics. A large number of studies in behavioral economics have demonstrated the defects of the traditional utility function and introduced a new parameter called loss aversion coefficient to calculate individual utility when it suffers a loss. In this paper, combination of behavioral economics and a payment algorithm based on the loss aversion is proposed. Compared with usual incentive mechanisms, the node utility function is redefined by the loss aversion characteristic of the node. Experimental results show that the proposed algorithm can get a higher rate of cooperation with a lower payment price and has good scalability compared with the traditional incentive mechanism.
Highlights
Crowdsensing, which can be described as people/humancentric sensing, has gradually become the ideal method for large-scale data collection [1]
Being different from the view of source independence, behavioral economics holds that when external information enters the individual cognitive mental accounting, it can effectively reflect the trade-off between expected return and possible loss and confirm whether the threshold value boundary has been reached [42]
The performance of the completely rational algorithm (CRA) and the loss aversion algorithm (LAA) is similar, because nodes do not have to worry about the fact that they would find no suitable work when there is a surplus of resources
Summary
Crowdsensing, which can be described as people/humancentric sensing, has gradually become the ideal method for large-scale data collection [1]. The loss aversion is an important branch of the prospect theory of behavioral economics What it describes is loss is more unbearable than gain that has the same amount of value. In order to motivate the nodes, this paper, from the point of view of the nodes, analyzes the decisionmaking process of the nodes, models them, introduces the loss aversion coefficient into the utility function of them, adjusts the payment mode in the traditional incentive mechanisms, effectively stimulates nodes to participate in perception, and enhances the performance of the crowdsensing system. (i) The paper uses the loss aversion to build the incentive mechanism, which revises the cooperative behavior researches based on traditional economics, so as to make up for the basic assumption insufficiency in the Scientific Programming traditional economics about human rationality, selfinterest, complete information, utility maximization, and preference consistent. (ii) By using the influence of the loss aversion psychology on decision-making, a compensation payment algorithm based on the loss aversion is proposed in the crowdsensing
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