The impact of honey fraud information on the valuation of honey origin: Evidence from an incentivized economic experiment

While the adulteration or mislabeling of products as honey, or honey fraud, is a major concern for industry and regulators—leading to a burgeoning research literature on documenting characteristics of authentic, place-specific honey and developing new methods of detecting honey fraud, there is little evidence about the impact of honey fraud occurrence on consumer preferences. Studying consumer response to honey fraud is complicated by the unpredictable nature of detection of fraud and consumer exposure to information about fraud, which makes consumer experiments a valuable tool for understanding consumer response to honey fraud. In this binding valuation experiment, we examine consumer willingness to pay for four types of honey before and after exposure to honey fraud information (versus a control group): imported, domestic (US), local (Lincoln, Nebraska), and organic (no origin specified) honey. When consumers do not have information about honey fraud, they do not value US honey any more than imported honey, though they are willing to pay a premium for both organic and local honeys. When consumers read information about honey fraud, their valuation of imported honey decreases significantly—by over 20%. Valuation of the organic honey also decreases, though by a smaller amount (4%). On the other hand, valuation of both US and local honey increases. The combined effect of the decreased valuation of imported honey and increased valuation of US-produced honey yields a significant increase in the premiums that consumers are willing to pay for US and local honey over the imported honey. The results suggest that increased consumer awareness about honey fraud may lead to stronger preferences for honey produced in the US.     

Two tales of platoon intelligence for autonomous mobility control: Enabling deep learning recipes

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.     

面向资源最优分配的深度学习双边拍卖算法

针对拍卖过程中计算效率低和利益分配不合理等问题, 本文提出了一种基于深度学习的迭代双边拍卖算法. 该算法通过买卖双方的初始报价数据训练基于神经网络的资源最优分配模型, 调用训练好的模型对实时报价数据快速响应, 直接求解经纪人最优分配问题(BAP)以实现计算资源分配, 显著地减小了计算代价, 提高了算法的计算效率. 进一步, 针对利益分配不合理等问题, 在迭代双边拍卖框架的支出规则和收入规则中引入调节因子用于调节买卖双方的利益, 解决已有算法在实现社会福利最大化过程中利益分配不合理的问题. 实验结果验证了该算法的有效性和优越性, 在运行时间、社会福利、买家利益、卖家利益和经纪人利益等多项指标均明显优于已有的迭代双边拍卖算法     

拍卖买受人的合同解除权与承租人的优先购买权——对一起拍卖合同纠纷案的分析

在房屋拍卖中经常遇到两个问题:一是如果买受人未能取得标的房屋的所有权,应该向谁主张权利;二是承租人能否行使优先购买权。在拍卖中,用代理、行纪、居间来说明委托人、拍卖人、买受人的关系,既与拍卖实践不符,也无法解释《拍卖法》第40条规定;三者的关系应按《合同法》规定的隐名代理来处理。根据法解释学原理,在拍卖中,承租人的优先购买权应作限缩解释,即承租人不应享有优先购买权。