Fourth Industrial Revolution:Technological Drivers,Impacts and Coping Methods

The world is marching into a new development period when the digital technology,physical technology,and biological technology have achieved an unprecedented development respectively in their own fields,and at the same time their applications are converging greatly.These are the three major technological drivers for the Fourth Industrial Revolution.This paper discusses the specific technology niches of each kind technological driver behind the Fourth Industrial Revolution,and then evaluates impacts of the Fourth Industrial Revolution on global industrial,economic,and social development.At last this paper proposes possible measures and policies for both firms and governments to cope with the changes brought by the Fourth Industrial Revolution.

An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.

How much do social connections matter in fundraising outcomes?

This study examines the role of social connections and network centrality in attracting funders to crowdfunding campaigns.We classify social connections as either external(e.g.,Facebook)or internal(e.g.,investing in online platforms through resource exchange).Drawing from the 108,463 crowdfunding campaigns on the online platform Kickstarter from April 21,2009,to July 24,2019,we apply external linkages and online followers to estimate the effect of external social connections.We construct a digraph network for the internal social connections and use PageRank,HITS,and centrality to obtain the weights of the nodes.Next,we compare the performance change of several prediction algorithms by feeding social connection-related variables.This study has several findings.First,for external social connections,having more online followers improves the funding success rate of a campaign.Second,for internal social connections,only authority and degree in centrality positively affect the number of funders and the campaign’s financing progress among the weights of the nodes.Third,using social connection variables improves the prediction algorithms for funding outcomes.Fourth,external social connections exert greater funding outcomes than internal social connections.Fourth,entrepreneurs should extend their external social connections to their internal social connections,and network centrality expedites project financing.Fifth,the effect of social connections on fundraising outcomes varies among the campaign categories.Fundraisers who are online influencers should leverage their online social connections,notably for the project categories that matter.

State control, access to capital and firm performance

We study the effect of state control on capital allocation and investment in China, where the government screens prospective stock issuers. We find that state firms are more likely to obtain government approval to conduct seasoned equity offerings than non-state firms. Further, non-state firms exhibit greater sensitivities of subsequent investment and stock performance to regulatory decisions on stock issuances than state firms. Our work suggests that state control of capital access distorts resource allocation and impedes the growth of non-state firms. We also provide robust evidence that financial constraints cause underinvestment.

Dynamic Pricing with Stochastic Reference Price Effect

We study a dynamic pricing problem of a firm facing stochastic reference price effect.Randomness is incorporated in the formation of reference prices to capture either consumers’heterogeneity or exogenous factors that affect consumers’memory processes.We apply the stochastic optimal control theory to the problem and derive an explicit expression for the optimal pricing strategy.The explicit expression allows us to obtain the distribution of the steady-state reference price.We compare the expected steadystate reference price to the steady-state reference price in a model with deterministic reference price effect,and we find that the former one is always higher.Our numerical study shows that the two steady-state reference prices can have opposite sensitivity to the problem parameters and the relative difference between the two can be very significant.