Distributed Risk Aversion Parameter Estimation for First-Price Auction in Sensor Networks

Following the Internet, the Internet of Things (IoT) becomes a prime vehicle for supporting auction. The use of market mechanisms to solve computer science problems is gaining significant traction. More and more clues show that the bidders tend to be risk-averse ones. However, traditional nonparametric approach is only applicable for the case of risk neutrality in a centralized server. This study proposes a generalized nonparametric structural estimation procedure for the first-price auctions in the distributed sensor networks. To evaluate the performance of the aggregated parameter estimators, extensive Monte Carlo simulation experiments are conducted for ten different values of risk aversion parameters including the risk neutrality case in multiple classic scenes. Moreover, in order to improve the usability of the aggregated parameter estimators, some guidance is also given for real-world applications.