|
on Network Economics |
| By: | Sodam Baek (College of Engineering, Seoul National University); Kibae Kim (College of Engineering, Seoul National University); Jorn Altmann (College of Engineering, Seoul National University) |
| Abstract: | As IT technology advanced, a new style of innovation emerged, in which a leading innovation company invites end-users to its open software service platform. With respect to this type of innovation, a lot of innovation studies were performed to understand the structure of the interaction among users and the platform provider from the perspective of network science. By concentrating only on the internal mechanisms among agents, the previous studies miss to consider innovation through collective intelligence. A platform provider plays an important role in the innovation. In this research, we investigate the structure of a service network with empirical data gathered from Salesforce.com AppExchange and discuss the role of a platform provider in innovation through collective intelligence. Our results suggest that the platform provider led the innovation in the initial period and, then, third party developers became gradually innovation leaders. Our findings are expected to re-orient the research focus from internal mechanisms to the role of platform providers. |
| Keywords: | Software Service Platform, Platform-as-a-Service, Network Analysis, Salesforce.com, Open Innovation. |
| JEL: | D85 L14 L15 L86 O31 O32 |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:2014112&r=all |
| By: | Oliver Kley; Claudia Kl\"uppelberg; Lukas Reichel |
| Abstract: | We contribute to the understanding of how systemic risk arises in a network of credit-interlinked agents. Motivated by empirical studies we formulate a network model which, despite its simplicity, depicts the nature of interbank markets better than a homogeneous model. The components of a vector Ornstein-Uhlenbeck process living on the vertices of the network describe the financial robustnesses of the agents. For this system, we prove a LLN for growing network size leading to a propagation of chaos result. We state properties, which arise from such a structure, and examine the effect of inhomogeneity on several risk management issues and the possibility of contagion. |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1406.6575&r=all |
| By: | Claudio J. Tessone |
| Abstract: | Many systems exhibit patterns of interaction that are largely sparse and volatile at the same time. Sparsity is a common trait in networks where links are costly, or the nodes involved have some kind of limited capacity. Volatility refers to the fact that edges tend to have very low persistence (compared to the observation period of the network evolution): the patterns of interaction are therefore characterised by a decay time after which the network topology is largely decorrelated with the previous time-step. Here, we introduce a simple model for temporal networks compatible with an arbitrary time-aggregated network, whose volatility can be adjusted. When volatility is too large, the instantaneous network experiences a percolation transition, to a largely disconnected structure. Interestingly, we show a non-trivial relationship between network volatility and the properties of dynamical processes taking place in the nodes of the system. We show that a phase transition towards between non-trivial dynamical states (like synchronisation, or infection propagation) is not-related to the topological transition to percolation, having different critical points. Moreover, we show that long range correlations emerge in the limit of very large network volatility. |
| Keywords: | temporal networks, long-range correlations |
| URL: | http://d.repec.org/n?u=RePEc:stz:wpaper:eth-rc-14-010&r=all |
| By: | Ishanu Chattopadhyay |
| Abstract: | While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence. Designing an efficient causality test, that may be carried out in the absence of restrictive pre-suppositions on the underlying dynamical structure of the data at hand, is non-trivial. Nevertheless, ability to computationally infer statistical prima facie evidence of causal dependence may yield a far more discriminative tool for data analysis compared to the calculation of simple correlations. In the present work, we present a new non-parametric test of Granger causality for quantized or symbolic data streams generated by ergodic stationary sources. In contrast to state-of-art binary tests, our approach makes precise and computes the degree of causal dependence between data streams, without making any restrictive assumptions, linearity or otherwise. Additionally, without any a priori imposition of specific dynamical structure, we infer explicit generative models of causal cross-dependence, which may be then used for prediction. These explicit models are represented as generalized probabilistic automata, referred to crossed automata, and are shown to be sufficient to capture a fairly general class of causal dependence. The proposed algorithms are computationally efficient in the PAC sense; $i.e.$, we find good models of cross-dependence with high probability, with polynomial run-times and sample complexities. The theoretical results are applied to weekly search-frequency data from Google Trends API for a chosen set of socially "charged" keywords. The causality network inferred from this dataset reveals, quite expectedly, the causal importance of certain keywords. It is also illustrated that correlation analysis fails to gather such insight. |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1406.6651&r=all |
| By: | Somayeh Koohborfardhaghighi (Technology Management, Economics, and Policy, College of Engineering, Seoul National University); Jorn Altmann (College of Engineering, Seoul National University) |
| Abstract: | Social networks can be differentiated according to the type of entities (i.e., humans or objects) that are represented within them. These networks can be called human networks and social object networks, respectively. Actors in human networks can act strategically to maximize their own payoffs during interactions with other humans. However, actors in social object network (e.g., SaaS service network) are not able to perceive the environment and act strategically upon that at any time. Only when they join the network, humans position them such that it maximizes their payoff. This paper contends that existing network formation models lack sufficient attention to social object networks (e.g., SaaS service networks). Therefore, we propose a new network formation model, through which we are able to explain how a SaaS service network emerges during the service composition procedure by service developers. The new network formulation model not only considers the usage frequency and reputation but also the similarity of the functionalities of the main SaaS services. It also explains how social objects (e.g., SaaS services) can benefit from establishment of links among each other in the network. |
| Keywords: | Software-as-a-Service Network, Network Formation Model, Social Object Networks. |
| JEL: | C02 C6 C15 D85 L86 O33 |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:2014113&r=all |
| By: | Kartik Anand; Ben Craig; Goetz von Peter |
| Abstract: | The network pattern of financial linkages is important in many areas of banking and finance. Yet bilateral linkages are often unobserved, and maximum entropy serves as the leading method for estimating counterparty exposures. This paper proposes an efficient alternative that combines information-theoretic arguments with economic incentives to produce more realistic interbank networks that preserve important characteristics of the original interbank market. The method loads the most probable links with the largest exposures consistent with the total lending and borrowing of each bank, yielding networks with minimum density. When used in a stress-testing context, the minimum density solution overestimates contagion, whereas maximum entropy underestimates it. Using the two benchmarks side by side defines a useful range that bounds the cost of systemic stress present in the true interbank network when counterparty exposures are unknown. |
| Keywords: | Econometric and statistical methods, Financial Institutions, Financial stability |
| JEL: | C13 C14 C21 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:14-26&r=all |
| By: | Gioia De Melo |
| Abstract: | his paper represents the first application of a novel strategy to estimate peer effects in education in a developing country. It provides evidence on peer effects in standardized tests by exploiting a unique data set on social networks in Uruguayan primary schools. The identification method enables one to solve the reflection problem via instrumental variables that emerge naturally from the network structure. Correlated effects are controlled for via classroom fixed effects. I find significant endogenous effects in reading, math scores (and mixed evidence on science): a one-standard deviation increase in peers' scores increases own scores by about 40 percent of a standard deviation. Simulation exercises show that, in a context of socioeconomic segregation in which students are assigned to public schools according to their neighborhood of residence, peer effects may amplify educational inequalities. |
| Keywords: | Peer effects, education, social networks, inequality |
| JEL: | I21 I24 O1 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:bdm:wpaper:2014-05&r=all |
| By: | Takayuki Mizuno (National Institute of Informatics, Graduate School of Economics, University of Tokyo, The Canon Institute for Global Studies); Wataru Souma (College of Science and Technology, Nihon University); Tsutomu Watanabe (Graduate School of Economics, University of Tokyo, The Canon Institute for Global Studies) |
| Abstract: | In this paper, we investigate the structure and evolution of customer-supplier networks in Japan using a unique dataset that contains information on customer and supplier linkages for more than 500,000 incorporated non-financial firms for the five years from 2008 to 2012. We find, first, that the number of customer links is unequal across firms; the customer link distribution has a power-law tail with an exponent of unity (i.e., it follows Zipf’s law). We interpret this as implying that competition among firms to acquire new customers yields winners with a large number of customers, as well as losers with fewer customers. We also show that the shortest path length for any pair of firms is, on average, 4.3 links. Second, we find that link switching is relatively rare. Our estimates indicate that the survival rate per year for customer links is 92 percent and for supplier links 93 percent. Third and finally, we find that firm growth rates tend to be more highly correlated the closer two firms are to each other in a customer-supplier network (i.e., the smaller is the shortest path length for the two firms). This suggests that a non-negligible portion of fluctuations in firm growth stems from the propagation of microeconomic shocks – shocks affecting only a particular firm – through customer-supplier chains. |
| Keywords: | buyer-supplier networks; supply chains; input-output analysis; power-law distributions; firm dynamics |
| JEL: | L11 L14 C67 |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:upd:utppwp:019&r=all |