理性人的人情味：快手“老铁经济”的内在机制和逻辑

以中国用户规模最大短视频平台之一快手头部主播直播带货推广项目的案例和数据分析为基础,构建解释快手直播电商经济底层逻辑的社会学机制模型.提出快手直播电商经济背后的3种社会学机制:关键影响者逻辑、身份认同逻辑和社会网络逻辑.通过LDA文档主题生成模型将快手主播的直播语义字段进行文档主题生成归类,发现情感因素和理性因素词交互影响受众的购买决策,它背后的社会学作用机制是关键影响者逻辑和身份认同逻辑.2020年快手直播电商半年交易额已达1044亿元,理解快手直播背后的影响机制,能够持续优化推广模型,在互联网流量广告领域具有重要的应用价值.     

Multilayer financial networks and systemic importance: Evidence from China

In this paper, we develop a multilayer network structure and reveal the relationship between network structure and systemic risk. Unlike many previous studies, our model considers both liability and cross-holding of shares between financial institutions simultaneously. We propose a new systemic risk measurement by exploring the dynamic mechanism of financial contagion in the multilayer network. We display the network structure of Chinese financial institutions, including connectivity and diversity, and identify the systemic importance of them. We demonstrate that the multilayer network plays a non-linear role in financial risk spreading. Using the panel regression model and several experiment evidences, we show that the systemic risk can be explained more effectively by the linkage diversity more than the connectivity at both the institutional level and the system level. Our results highlight the importance of considering contagion mechanisms that go beyond a simple single-layer network structure.     

Application of collective knowledge diffusion in a social network environment

ABSTRACT

We present a group dynamics model that shows knowledge integration as a process occurring over time. As each individual in the group contact others, his own knowledge changes, and over time the collective knowledge is obtained. This allows modeling knowledge diffusion in a social network and while the models presented in this paper are not competitive in that area, they approach the problem from previously unconsidered direction. We test the behavior of the model in a multi-agent simulation and we test a simple advertisement campaign in a social network. We provide discussion of elements needed for making model more competitive.     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

Market microstructure in a global B2B network

On‐line marketplaces raise several interesting issues, among them the relevance of location when content is digitized, and the assessment of a supplier's capabilities when buyers worldwide only have electronic contact with sellers. In global B2B on‐line marketplaces, market microstructures, i.e. which firms compete for the same customers, are thus likely to be influenced by how customers value location and firm capabilities in their decisions to do business with different suppliers on‐line. We suggest that both these sets of attributes will continue to matter on‐line—firms possessing similar capabilities, as well as firms that are similar in location by country, time zones or clusters, will compete for business from the same customers. We model the similarity in competitive positions between pairs of firms based on the overlap in their customer networks, using data on actual interactions between supplier and customer banks on an electronic trading system. Using QAP network regression techniques on the 100 largest banks in this industry, we find that similarity in capabilities influences who competes with whom, and that location still matters in a global B2B exchange. Interestingly, location influences who a firm's competitors are, but not where its customers are from. Copyright © 2001 John Wiley & Sons, Ltd.     

基于人工神经网络的地铁施工招标综合评估模型

广州地铁工程是国内数个重要的大型城市基础建设项目之一。目前对于这种复杂的大型工程项目,迫切需要一个成熟可靠的公开招标评估系统。根据广州地铁工程的实际招标评标数据,提出应用人工神经网络(ANN)建立地铁施工招标综合评估模型。BP神经网络具有很强的自学习和自适应能力,可以精确地迫近复杂的非线性动态函数。经过训练网络,基于BP网络的模型成功量化了主观和定性的技术评估指数,得出较满意的评估结果。该模型可以通过增加样本提高精度,从而应用在地铁和其他类似建设项目招标中。     

The European high-speed train network: Predicted effects on accessibility patterns

The principal aim of this paper is to evaluate the impact of the future European high-speed train network on accessibility, by reducing time distance between places and modifying their relative location. We compare the current situation with that foreseen for the year 2010, according to the Outline Plan of the European High-Speed Train Network, in order to analyse which areas will most benefit from construction and improvement of the infrastructure. It is hypothesized that the high-speed train will certainly bring the peripheral regions closer to the central ones, but will also increase imbalances between the main cities and their hinterlands. A weighted average distance indicator is applied for this purpose. This measure identifies the spatial distribution of accessibility in the area of study, emphasizing the infrastructure effects, and locates accessibility changes at the European level. A Geographic Information System (GIS) has been used to carry out this study.     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

A theory of global strategic alliances: The case of the global auto industry

The theory articulated in this paper suggests that the desire to reduce demand and competitive uncertainty are two separate, important motives for alliance formation. Taking this as a starting point, we predict the configuration of horizontal alliances that we might expect to observe within an industry when firms experience these uncertainties to different degrees. An empirical test of this theory using data from the global auto industry yields results consistent with the view (1) that alliances are a device for reducing both the uncertainties that arise from unpredictable demand conditions and those that arise from competitive interdependence, and (2) that variation of demand uncertainty and competitive uncertainty across firms explains differentials in both the intensity and structure of their horizontal alliance activity.