Welfare Implications in Intermediary Networks

2021 ◽  
Author(s):  
Thành Nguyen ◽  
Karthik Kannan
Keyword(s):  
Mergers And Acquisitions ◽  
Economic Analysis ◽  
Business Model ◽  
Network Structure ◽  
Network Measurement ◽  
Online Retailer ◽  
Supreme Courts ◽  
Ride Sharing ◽  
Underlying Network ◽  
Welfare Implications

Competitive pressures have forced many traditional companies to evolve into a platform-based business model. Trade commissions and even supreme courts recognize the need for economic analysis as the nature of competition changes in the market. There have been many mergers and acquisitions across platform-based businesses. In the ride-sharing sector, Lyft and Didi Chuxing were initially in a partnership to thwart Uber but Uber merged its operation with Didi Chuxing eventually. Amazon and Walmart competed fiercely to buy the Indian online retailer Flipkart, which Walmart eventually won. Traditional antitrust models studying the implications of mergers do not consider the underlying network structure of these intermediary markets. This is the main focus of our model and analysis. We provide a network measurement to evaluate the effect of mergers on welfare. Our analysis shows that because of the underlying networks mergers can sometimes improve welfare.

scholarly journals Free Lunch and Ride Sharing: A New Business Model for Winter Sport Resorts

2016 ◽  
Author(s):  
Riccardo Bonazzi ◽  
Michaël Poli ◽  
Patrick Kuonen
Keyword(s):  
Business Model ◽  
Free Lunch ◽  
Winter Sport ◽  
Ride Sharing ◽  
New Business

scholarly journals Organic Business Modeling and the Organism-Ecosystem Unit Duality

2019 ◽  
Vol 9 (2) ◽  
pp. 21-43
Author(s):  
Paul Jordan Washburn
Keyword(s):  
Economic Analysis ◽  
Business Model ◽  
Marginal Utility ◽  
Optimal Growth ◽  
Ripple Effect ◽  
Human Beings ◽  
Healthy Human ◽  
Business Modeling ◽  
Annual Consumption ◽  
The U.S

The health of a corporation relies most heavily upon healthy human beings' value-based productivity for optimal growth and evolution. A duality between personhoods and their respective systems' weighted impacts are in question, as the U.S. Healthcare industries weighted impact affects all other U.S.-GDP subsectors. The author performed an analysis of 21 main U.S.-GDP subsectors based on unclassified 1960-2014 U.S. Bureau of Economic Analysis reports. The author derived a [Consumption:Value] ratio-based equation, demonstrating results in [0.0,2.0] and U.S. dollar scales. The U.S.-GDP-Healthcare subsector increased its average annual consumption by $122,232,000,000 and was part of the U.S.-GDP's 71.4% demonstrating a reduced value ratio between 1960-1969 and 2005-2014. The author describe a weighted duality of personhoods classification, a potential ripple effect violation, and presents a new description of a pathologic, malignant organic business model due to a negatively balanced [Consumption:Value] alteration. These findings highlight reduced marginal utility and value of the U.S.-Healthcare subsector.

scholarly journals Learning Diverse Bayesian Networks

2019 ◽  
Vol 33 ◽  
pp. 7793-7800
Author(s):  
Cong Chen ◽  
Changhe Yuan
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Search Algorithm ◽  
Noisy Data ◽  
Local Neighborhood ◽  
Causal Ordering ◽  
True Model ◽  
Underlying Network ◽  
Novel Method

Much effort has been directed at developing algorithms for learning optimal Bayesian network structures from data. When given limited or noisy data, however, the optimal Bayesian network often fails to capture the true underlying network structure. One can potentially address the problem by finding multiple most likely Bayesian networks (K-Best) in the hope that one of them recovers the true model. However, it is often the case that some of the best models come from the same peak(s) and are very similar to each other; so they tend to fail together. Moreover, many of these models are not even optimal respective to any causal ordering, thus unlikely to be useful. This paper proposes a novel method for finding a set of diverse top Bayesian networks, called modes, such that each network is guaranteed to be optimal in a local neighborhood. Such mode networks are expected to provide a much better coverage of the true model. Based on a globallocal theorem showing that a mode Bayesian network must be optimal in all local scopes, we introduce an A* search algorithm to efficiently find top M Bayesian networks which are highly probable and naturally diverse. Empirical evaluations show that our top mode models have much better diversity as well as accuracy in discovering true underlying models than those found by K-Best.

scholarly journals Modeling Network Evolution by Colored Petri Nets

2016 ◽  
Vol 15 (1) ◽  
pp. 41-61
Author(s):  
Suwimon VONGSINGTHONG ◽  
Sirapat BOONKRONG ◽  
Herwig UNGER
Keyword(s):  
Petri Nets ◽  
Network Structure ◽  
Information Diffusion ◽  
User Behavior ◽  
Network Evolution ◽  
Clustering Coefficient ◽  
Colored Petri Nets ◽  
Traffic Patterns ◽  
Underlying Network ◽  
Flow Of Information

Discovering how information was distributed was essential for tracking, optimizing, and controlling networks. In this paper, a premier approach to analyze the reciprocity of user behavior, content, network structure, and interaction rules to the interplay between information diffusion and network evolution was proposed. Parameterization and insight diffusion patterns were characterized based on the community structure of the underlying network using diffusion related behavior data, collected by a developed questionnaire. The user roles in creating the flow of information were stochastically modeled and simulated by Colored Petri Nets, where the growth and evolution of the network structure was substantiated through the formation of the clustering coefficient, the average path length, and the degree distribution. This analytical model could be used for various tasks, including predicting future user activities, monitoring traffic patterns of networks, and forecasting the distribution of content.

scholarly journals Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Liqun Gao ◽  
Yujia Liu ◽  
Hongwu Zhuang ◽  
Haiyang Wang ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Public Opinion ◽  
Prediction Model ◽  
Network Structure ◽  
Early Warning ◽  
Feature Fusion ◽  
Learning Model ◽  
Feature Model ◽  
Underlying Network ◽  
Deep Learning Model

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

scholarly journals Analysis of External Environmental Factors Which Affect the Development of Ride-sharing Platform Based on ISM model, Examples of DiDi

2018 ◽  
Vol 1 (2) ◽  
pp. 37-39
Author(s):  
Lin Li
Keyword(s):  
Environmental Factors ◽  
Business Model ◽  
Sharing Economy ◽  
Third Party ◽  
Security Issues ◽  
Goods And Services ◽  
Ride Sharing ◽  
New Business ◽  
Point To Point ◽  
Life Consumption

The concept of “sharing economy” was first proposed jointly by American Marcos Felson and Joan Spence. They described a new way of life consumption with “collaborative consumption”. The main feature of sharing economy is individuals achieve point-to-point direct transactions of goods and services through third-party platforms [1]. However, the objective conditions at that time made it difficult to put into practice. With the development of network technology, it is possible to integrate offline idle goods or personal services and provide them to users at a lower price, and become a viable new business model. As a Ride-sharing platform, Uber has become the leading enterprise in the sharing economy, its successful experience is the learning target of other sharing economic platforms, and the business model is also representative in sharing economic industry. However, Uber naively believes that the leading business model and business methods in the US market can be seamlessly extended to other countries and regions, without paying attention to localization for the users, in China and even Southeast Asia, Uber suffered a huge defeat and was replaced by DiDi and Grab. As the largest ride-sharing platform in China, DiDi was pushed to the turmoil in the second half of 2018 due to security issues, two women were raped and killed by DiDi driver while riding, and the call to shut down DiDi was endless in China. In China, Ride-sharing Platform, from Uber to DiDi, from DiDi's strong development to the current endless call to shut down, what kind of key external environmental factors affect the development of the ride-sharing platform? This paper attempts to clarify the external environmental factors that affect the development of shared travel platforms, and use the ISM model to clarify their levels and relevance.

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