Vehicle Cooperation Promotion Mechanism Based on Behavioral Economics Anchoring Theory

Vehicular Ad-hoc NETwork (VANET) is a wireless self-organized network composed of vehicles and Road Side Units (RSUs) providing users services. There are problems such as difficulty in maintaining end-to-end links between vehicles and RSUs, and limited bandwidth and channel resources. Incentive mechanisms are necessary to attract a sufficient number of vehicles to participate in data forwarding and share resources efficiently among vehicles in order to solve the above problems. There are two decision-making stages for a node. In the initial stage, a new node that has never participated decides whether to participate; in the following stage, the participating nodes decide whether to participate again. The current incentive mechanisms are based on traditional economics, which have two problems: 1) the role of anchors (that is, reference points) is not considered in both decision-making stages; 2) using the same utility function to evaluate two different decision-making stages which are actually affected by two different anchors. However, anchoring effect in behavioral economics has proved that people make decisions based on the experience of others (called Experimenter-Provided Anchoring, EPA) when they are first time to participate in data forwarding. They rely on their own experience (called Self-Generated Anchoring, SGA) when they already have forwarding experience. Therefore, the users' decision-making in the existing incentive mechanisms deviates from reality. Based on anchoring effect, we propose a Dual-Process Mechanism (DPM), which includes the user Attraction mechanism based on Experimenter-Provided Anchoring (AEPA) and the user Continuous Working mechanism based on Self-Generated Anchoring (CWSGA). AEPA is used to motivate those new nodes to participate in data forwarding. CWSGA is used to motivate those nodes which have participated to forward data again. Simulations show that DPM has a 10% increase in cooperation rate compared with the currently most used incentive mechanism based on traditional economics, and the node utility is also improved.