Network Effects Model Research Articles

A Kind of Statistics Data Processing with Indicators in an Autocorrelation Network

Efforts to develop data processing or some variable evaluation of the components autocorrelation network or interpersonal relationship network have been hampered by many obstacles. One important reason is the lag of autocorrelation network model (ANM) development. Autocorrelation network models are used to deal with the data with autocorrelation network data. However, some data are correlated with its lag station, so its necessary to introduce the time series in the ANM. The network autocorrelation model with lag and auto-correlated indicators or variables is put forward based on the expansion of the existing social network effect model. The estimation method of the network autocorrelation model is illustrated; the time series stationarity, the consistency and effectiveness of the estimation method are discussed. Also the application of the model is discussed, especially the practical significance on the forecasting and controlling.

An agent-based model of network effects on tax compliance and evasion

Agent-based models are flexible analytical tools suitable for exploring and understanding complex systems such as tax compliance and evasion. The agent-based model created in this research builds upon two other agent-based models of tax evasion, the Korobow, Johnson, and Axtell (2007) and Hokamp and Pickhardt (2010) models. The model utilizes their rules for taxpayer behavior and apprehension of tax evaders in order to test the effects of network topologies in the propagation of evasive behavior. Findings include that network structures have a significant impact on the dynamics of tax compliance, demonstrating that taxpayers are more likely to declare all their income in networks with higher levels of centrality across the agents, especially when faced with large penalties proportional to their incomes. These results suggest that network structures should be chosen selectively when modeling tax compliance, as different topologies yield different results. Additionally, this research analyzed the special case of a power law distribution and found that targeting highly interconnected individuals resulted in a lower mean gross tax rate than targeting disconnected individuals, due to the penalties inflating the mean gross tax rate in the latter case.

Oil market structure, network effects and the choice of currency for oil invoicing

Crude oil is a homogeneous good traded on specialised exchanges and quoted and invoiced predominantly in US dollars. Despite the strong case for the use of the US dollar as a vehicle currency in the oil trade, we provide an alternative view. We develop a simple network effects model to identify the conditions under which either a complete switch in the oil invoicing currency or parallel invoicing in different currencies is possible and economically sensible. We calibrate the model using low actual values for the transaction costs of using euro and/or US dollars, as well as a proxy for information costs, which decline with the increase in the use of the new currency. The results show that there will be a switch to parallel invoicing in both currencies when two conditions are met: first, oil exporters expect that a certain minimum number of other oil exporters will also start using the new currency; and second, the information costs associated with quoting oil contracts in two currencies are low.

Network Effects in Biology R&D

Network effects play a central role in industries such as software and electronics. So far, however, their role in shaping biotechnology innovation has largely been overlooked. Since network effects can make markets vulnerable to monopoly, it is important to know whether and to what extent they operate in biology. We address this issue by a multimethod study of academic and commercial stem cell research. We present statistics showing that researchers focus on a tiny fraction of available lines; that network effects provide the best explanation of this phenomenon; and that these effects are primarily driven by reduced user costs due to learning spillovers. We conclude with a model of network effects in biology research and development. Surprisingly, some winner-take-all outcomes are transient, so that antitrust intervention may not be necessary.

Reconceptualizing and expanding the positive feedback network effects model: A case study

During the 1980s, economists developed the concept of positive network effects and switching costs as the key dynamics of de facto standards competition. They concluded that a positive feedback loop usually ''tips'' a market to the standard with the most users and complementary assets. While recent scholars have acknowledged that not all markets tip, and suggested some possible limitations on this phenomena, these limitations have not been integrated systematically into a model for competition in these industries. Using examples drawn from the standards contests of the past 20 years, this case study develops an integrated model for competition in these industries.

Local Network Effects and Complex Network Structure

This paper presents a model of local network effects in which agents connected in a social network each value the adoption of a product by a heterogeneous subset of other agents in their neighborhood, and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in monotone strategies, can be strictly Pareto-ranked based on a scalar neighbor-adoption probability value, and that the greatest such equilibrium is uniquely coalition-proof. Each Bayes-Nash equilibrium has a corresponding fulfilled-expectations equilibrium under which agents form local adoption expectations. Examples illustrate cases in which the social network is an instance of a Poisson random graph, when it is a complete graph, a standard model of network effects, and when it is a generalized random graph. A generating function describing the structure of networks of adopting agents is characterized as a function of the Bayes-Nash equilibrium they play, and empirical implications of this characterization are discussed.

Key Factor to Drive Success of New Product with Network Effects: Product Quality or Installed Base?

Abstract Which is the key factor to drive the success of new product with network effects: product quality or installed base? The answer of this problem extremely influences what strategy the company will adopt. On the basis of the complex networks method, a local network effect model is introduced, in which the utility of the new product valued by each agent includes intrinsic value depending on product quality and network value depending on the popularity of the product in his personal network. The results show that the new product exists for a threshold intrinsic value, which marks two diffusion patterns: diffusion success provided that the intrinsic value is above the threshold intrinsic value, otherwise diffusion failure. The installed base obtained in the launch period is not the key factor of diffusion success. It functions to moderate the threshold intrinsic value to some extent. Furthermore, the strength of the network effects also affects the threshold intrinsic value. These findings have important implications for firms' developing strategies to diffuse new products with network effects.

Local Network Effects and Complex Network Structure

This paper presents a model of local network effects in which agents connected by a social network each value the adoption of a product by a heterogeneous subset of other agents in their 'neighborhood', and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in monotone strategies, can be strictly Pareto-ranked based on a scalar neighbor-adoption probability value, and that the greatest such equilibrium is uniquely coalition-proof. Each Bayes-Nash equilibrium has a corresponding fulfilled-expectations equilibrium under which agents form local adoption expectations. Examples illustrate cases in which the social network is an instance of a Poisson random graph, when it is a complete graph, a standard model of network effects, and when it is a generalized random graph. A generating function describing the structure of networks of adopting agents is characterized as a function of the Bayes-Nash equilibrium they play, and empirical implications of this characterization are discussed.

Should Buyers Try to Shape IT Markets Through Non-Market (Collective) Action? Antecedents of a Transaction Cost Theory of Network Effects

This paper develops a transaction cost theoretic model of network effects and applies it to assessing the chances of users to influence the range of technological choices available on IT markets through collective action. The theoretical basis of the model is formulated by a number of empirically refutable propositions which overcome a number of conceptual and empirical difficulties encountered by the traditional interpretation of network effects as (positive) network externalities. The main difference between our model and modeling network effects as network externalities is that network effects are seen as caused by the costs of purchasing/marketing new technology, that is, transaction costs, rather than by the benefits of using new technology. A preliminary application of the model suggests that users can significantly improve the chances of replacing an established technology by a new, potentially superior one if they set up an organizational structure that serves as a conduit of information exchange and knowledge sharing. This, however, would call for a rather different type of collective user action than exists today in the form of user groups.

A NETWORK EFFECTS MODEL OF CHARISMA ATTRIBUTIONS.

We propose a network effects model of charisma that specifies the distribution and change of charisma attributions among individuals connected in a social structure. Data from a police organization...

Theory, Measurement, and Specification Issues in Models of Network Effects on Learning

Theory, Measurement, and Specification Issues in Models of Network Effects on Learning

Theory, Measurement, and Specification Issues in Models of Network Effects on Learning: Reply to Carbonaro and to Hallinan and Kubitschek

O } ur article (Morgan and S0rensen 1999, henceforward M&S) is an attempt to spark debate and encourage research on the effects of school social systems on learning. The comments by Carbonaro (1999, henceforward C) and Hallinan and Kubitschek (1999, henceforward H&K) suggest that we have succeeded. We are thankful for C's appraisal, supplementary data analysis, and comparison to his own related research (Carbonaro 1998). We are, however, surprised by H&K's comment. As we will show, H&K do not understand Coleman's paired theories of norms and social capital, which we embrace and extend in our article. Thus, much of H&K's conceptual criticism, although directed at us, applies to Coleman's theories, which H&K's comment aims to defend. In response to the comments of C and of H&K, we offer a defense of our article, starting with two general comments. First, our distinction between norm-enforcing and horizon-expanding schools is an ideal-type distinction, constructed primarily

Estimation of the Network Effects Model in a Large Data Set

Effects of interpersonal context are processes best represented by the network effects model. However, the size of the data matrices involved has until now prevented application of the model to large data sets containing information on individuals. The proposed adaptation of Doreian's maximum likelihood estimator substantially reduces the amount of computer core memory required for estimating the model, so that estimation can be carried out on a typical mainframe computer. An empirical illustration of the network effects model using the Adolescent Society data is presented.