Farsighted network formation

Dutta, Bhaskar; Ghosal, Sayantan; Ray, Debraj

doi:10.1016/j.jet.2004.05.001

“…From a more practical point of view, the theoretical literature on endogenous networks does not offer any simple way to deal with strategic network formation process and even the available results (e.g. Jackson and Watts, 2002, Page et al, 2005, and Dutta et al, 2005 are still too general to be used together with an equilibrium model of the labor market.…”

Section: Heterogenous Social Networkmentioning

confidence: 99%

Why are similar workers paid differently? the role of social networks

Fontaine

¹

2008

Journal of Economic Dynamics and Control

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit company supported by Deutsche Post World Net. The center is associated with the University of Bonn and offers a stimulating research environment through its research networks, research support, and visitors and doctoral programs. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. We provide a matching model where identical workers are embedded in ex-ante identical social networks. Job arrival rate is endogenous and wages are bargained. We study the evolution of networks over time and characterize the equilibrium distribution of unemployment rates across networks. We emphasize that wage dispersion arises endogenously as the consequence of the dynamics of networks, firms' strategies and wage bargaining. Moreover, contrary to a generally accepted idea, social networks do not necessary induce stickiness in unemployment dynamics. Our endogenous matching technology shows that the effects of networks on the dynamics mostly hinge on search externalities. Our endogenous framework allows us to quantify these effects.JEL Classification: E24, J64, J68

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“…Among the various works already carried out, we can find examples related to academic and scientific research networks (Bennett & Kidwell, 2001;Wagner & Leydesdorff, 2005), development networks of free software (Crowston et al, 2007), research and development networks in the automotive industry (Rycroft & Kash, 2004), networks of companies to improve sustainability (Espinosa & Porter, 2011), local public administration networks for economic policy development (Lee et al, 2012), networks of farmers (Dutta et al, 2005), networks of fashion industries (Schuh et al, 2008;Scherrer-Rathje et al, 2009), team management (Parker et al, 2015), among others. In all the literature reviewed, self-organizing networks were not addressed in the scope of Six Sigma programs, although there are common characteristics.…”

Section: Self-organizing Networkmentioning

confidence: 99%

“…Among the various types of collaborative networks, self-organizing networks stand out for their no centralized management, the intense participation of those involved, their ability to keep their development without significant external interference and the ability to promote the learning of all those involved in the network through the systematization and availability of the knowledge generated (Kash & Rycoft, 2000;Rycroft & Kash, 2004;Dutta et al, 2005;Wagner & Leydesdorff, 2005;Schuh et al, 2008). Gutiérrez et al (2012) claim that the Six Sigma program of an organization can be considered as a learning network, providing an organizational structure composed of a network of professionals who work together oriented towards projects.…”

Section: Introductionmentioning

confidence: 99%

Programa Seis Sigma Auto-organizado: modelo da situação atual e necessidades de mudanças

Gomes

¹

,

Marques

²

,

Guerrini

³

2017

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Resumo: A formação de redes colaborativas é uma importante forma das organizações estimularem a inovação, reduzirem custos e transferir conhecimentos. Uma forma particular de rede colaborativa, as redes auto-organizadas Palavras-chave: Rede auto-organizada; Seis Sigma; EKD; Modelagem organizacional.Abstract: The formation of collaborative networks is an important way for organizations to stimulate innovation, reduce costs and transfer knowledge. A particular form of collaborative network -the self-organizing network -has shown great capability to promote the learning of those involved, systematize knowledge and lead to innovation. The Six Sigma programs, adopted by several organizations, have as their main objective the reduction in process variability, thus resulting in lower costs and quality improvement. The literature on self-organizing networks has little on the formation of these types of networks and the literature on Six Sigma is focused on statistical methods. Therefor, the aim of this article is to verify the needs for change in a Six Sigma program that enables the development of a self-organizing network. The case studied was carried out with a large organization, manufacturer of capital goods, where the Six Sigma program modeling was performed. From the generated models, as a result, it was have identified a set of changes needed so that the Six Sigma program can create a self-organizing network.

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“…() now denotes the shortest directed path from  to . 19 We let  ()   = f : () = g denote the set of agents with a shortest distance to agent  equal to  and  ()   = f : () = g denote the set of agents with a shortest distance from agent  equal to . Notice that the cardinality of these sets for  = 1 are the in-degree and the out-degree of node , respectively.…”

Section: Directed Networkmentioning

confidence: 99%

“…In order to keep the analysis simple we ignore such links. 19 Keep in mind that now it is most likely that () 6 = (). 20 The idea here is that both agents bene…t for forming the link.…”

Section: Directed Networkmentioning

confidence: 99%

Contagion in Stable Networks

Bougheas

¹

2017

SSRN Journal

1

0

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Contagion in Stable Networks AbstractWe study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics, underground resistance organizations, trade networks) where we find that stable networks are comprised of completely connected disjoint subnetworks. Then, we consider networks with directed links and we find that the completely connected network is stable, although, its exact structure, and thus contagion implications, is sensitive to parameter values for costs and benefits. Lastly, we introduce aggregate externalities (e.g. fire sales for the case of financial networks) and we find that stable networks can be asymmetric, connected but not completely connected, thus capturing the main features of inter-industry and financial networks. JEL-Codes: C720, D850.

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Farsighted network formation

Cited by 190 publications

References 25 publications

Why are similar workers paid differently? the role of social networks

Why are similar workers paid differently? the role of social networks

Programa Seis Sigma Auto-organizado: modelo da situação atual e necessidades de mudanças

Contagion in Stable Networks

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