The extensive application of algorithm recommendation technology not only meets the information needs of network users but also leads to the emergence of “information cocoons”. On the basis of summarizing three generating mechanisms, namely, the theory of technological innovation, the theory of interest-driven, and the theory of emotional identity, this paper constructs a game model of bilateral evolution between information platforms and network users and simulates the influence path of key factors on the evolution of both parties’ main strategies. The research shows that algorithm recommendation technology is the root of “information cocoons” in the algorithm era. As the algorithm technology matures day by day, the cost of using algorithm recommendations on information platforms and the loss cost of accepting algorithm recommendations by network users are constantly decreasing, which causes the information platforms and network users’ strategy choice for algorithm recommendation to evolve from {give up and conflict} to {use and accept}, and finally leads to the long-term existence of “information cocoons”.
Abstract. A good auction mechanism is able to release the information who owns to the other side who lacks of. In order to ensure a positive flow of information and to improve the active participation of suppliers, negotiation mechanism is introduced, and suppliers can participate in the design of auction mechanism. Several rounds of English auction is applied to determining the final winner. Taking into account that the buyer has a preference on the property, we use AHP to calculate final weights, and use multi-attribute utility function to calculate the greatest utility as the winner.
IntroductionA reverse auction is a type of auction in which the roles of buyers and sellers are reversed. In a regular auction, buyers compete to obtain a good or service, and the price typically increases over time. In a reverse auction, the buyer advertises need for an item or service, sellers compete to obtain business, and prices typically decrease over time. Because it is widely used in purchase of goods in large quantity, besides price, there are several attributes should be considered, such as supplier's credit, quality and date of delivery. Scholars both at home and abroad have studied on multi-attribute reverse auction: Che firstly studied design competition in government procurement by developing a model of two-dimensional auctions, where firms bid on both price and quality, and bids are evaluated by a scoring rule designed by a buyer[1]; Bichler conducted experiments on several sorts of auction, he found the utility scores achieved in multi-attribute auctions were significantly higher than those of single-attribute auctions [2]. Wu Gang thought setting weights should be based on the importance of attributes, and proposed a new approach called hierarchical interactive collaborative group decision-making [3]. Xie Anshi proposed a decision making methods for mulit-attribute online auction based on fuzzy rough set [4]. This paper carries Bichler's model of multi-attribute weighted summation, introduces consultation mechanism, uses root method in AHP to determine the ultimate weights, and bids several times to make competition sufficient, at last sums the normalized attribute values with weights. Highest score wins.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.