Evolutionary Game for Mining Pool Selection in Blockchain Networks

In blockchain networks adopting the proof-of-work schemes, the monetary incentive is introduced by the Nakamoto consensus protocol to guide the behaviors of the full nodes (i.e., block miners) in the process of maintaining the consensus about the blockchain state. The block miners have to devote their computation power measured in hash rate in a crypto-puzzle solving competition to win the reward of publishing (a.k.a., mining) new blocks. Due to the exponentially increasing difficulty of the crypto-puzzle, individual block miners tends to join mining pools, i.e., the coalitions of miners, in order to reduce the income variance and earn stable profits. In this paper, we study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies. We identify the hash rate and the block propagation delay as two major factors determining the outcomes of mining competition, and then model the strategy evolution of the individual miners as an evolutionary game. We provide the theoretical analysis of the evolutionary stability for the pool selection dynamics in a case study of two mining pools. The numerical simulations provide the evidence to support our theoretical discoveries as well as demonstrating the stability in the evolution of miners' strategies in a general case.Comment: Submitted to IEEE Wireless Communication Letter

Using Fuzzy Sentiment Computing and Inference Method to Study Consumer Online Reviews

As a new type of word-of-mouth information, online consumer reviews possess critical information regarding consumer‘s concerns and their experience with the product or service. Such information is considered essential to firms‘ business intelligence which can be utilized for the purpose of production recommendation, personalization, and better customer understanding. This paper considers the problem of online reviews sentiment mining based on the theory of consumer psychology and behavior. Given the fuzzy attribute nature of the online reviews, we have established fuzzy group bases of consumer psychology. Four fuzzy bases, including features, sense, mood and evaluation, are established. The consumer attitude elements are reflected by natural language reviews. A fuzzy sentiment computing algorithm of online reviews for consumer sentiment is developed, and a fuzzy rule base is also presented based on consumer decision-making process. Finally it shows by means of an experiment that the proposed approach is very well suited as an analysis tool for the online reviews sentiment mining problem

Product Fuzzy Recommendation of Online Reviews Based on Consumer Psychological Motives

Sentiment analysis of online comments and their application has become a hot topic. Meanwhile the evaluation and emotion method has challenged researchers and practitioners. This paper proposes a fuzzy modeling for the evaluation and emotion of online review texts by means of the theory of consumption motivation type and establishes corresponding fuzzy corpus. A calculation method of comprehensive evaluation and emotion with respect to the consumer‟s preference for product attributes provide reasoning antecedents. Establishment of fuzzy inference rules give results of recommendation to consumers of four different motivations. Experimental results prove the validity of the proposed method

Modelling the Diffusion of Investment Decisions on Modular Social Networks

In the financial market, information and investment behaviors disseminate in investor social networks, and different contagion patterns may cause diverse investment trends. Prior studies have investigated the impact of investor social networks, but few have considered community structure. In this paper, we study the impact of the community structure of investor social networks on the diffusion of internet investment products. A two-stage diffusion model is proposed, and the clustering coefficient and modularity of an investor social network are considered. The results show that both modularity and the clustering coefficient have an impact on the diffusion velocity and scale and that the impact is most evident at the stage of explosive growth. The negative influence of a large modularity can be hardly mitigated by adjusting other factors. Furthermore, a decrease in modularity and an increase in the clustering coefficient can better facilitate diffusion when the temporary investment rate is high and can partly offset the negative impact of information discarding and divestment