nep-net New Economics Papers
on Network Economics
Issue of 2010‒12‒18
ten papers chosen by
Yi-Nung Yang
Chung Yuan Christian University
  1. The determinants of co-inventor tie formation: proximity and network dynamics By Cassi, Lorenzo; Plunket, Anne
  2. Global Diffusion of the Non-Traditional Banking Model and Alliance Networks: Social Exposure, Learning and Moderating Regulatory Effort By Cuntz, A.N.; Blind, K.
  3. A structural model of segregation in social networks By Angelo Mele
  4. Iterating influence between players in a social network By Michel Grabisch; Agnieszka Rusinowska
  5. Why are some coalitions more successful than others in setting standards? Empirical evidence from the Blu-ray vs. HD-DVD standard war By Zouhaïer M'chirgui; Olivier Chanel; Didier Calcei
  6. Game theory models for exchange Networks: experimental results By E. J. Bienenstock; P. Bonacich
  7. Negotiated Exchange in Social Networks By Michael Lovaglia; John Skvoretz; David Willer; Barry Markovsky
  8. Exclusion and Power: a Test of Four Theories of Power in Exchange Networks By John Skvoretz; David Willer
  9. The Distribution of Power in Exchange Networks: Theory and Experimental Results By K. S. Cook; R. M. Emerson; M. R. Gillmore; T. Yamagishi
  10. Party formation in collective decision-making By Martin J Osborne; Rabee Tourky
  1. By: Cassi, Lorenzo; Plunket, Anne
    Abstract: This paper investigates the determinants of co-inventor tie formation using micro-data on genomic patents from 1990 to 2006 in France. We consider in a single analysis the relational and proximity perspectives that are usually treated separately. In order to do so, we analyse the determinants of network ties that occur within existing components and between two distinct components (i.e. bridging ties). We test the argument that formation of these two different types of ties results from distinct strategies in accessing resources. Doing so, we contrast network and proximity determinants of network formation and we investigate if social network allows economic actors to cross over geographical, technological and organizational boundaries.
    Keywords: Social networks; relational perspective; proximity; co-patenting; network formation
    JEL: R12 Z13 D85 O31
    Date: 2010–11
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:27303&r=net
  2. By: Cuntz, A.N.; Blind, K.
    Abstract: We analyze the impact of (alliance) network exposure on the speed and extent of adoption of the business model as being one explanatory factor for diffusion controlling for actor specific characteristics and embeddedness in the network. In order to explain how existing national regulation moderated this relationship and whether it succeeded in its risk-limiting mission by moderating global adoption patterns and risk-bearing behavior among financial institutions we estimate various history event analysis model i.e. standard Cox and extended frailty models. We find strong support for the role of network exposure rather than social learning, the impact of regulatory effort on patterns of adoption and the role of country clusters for diffusion in the financial sector.
    Keywords: diffusion;networks;alliances;banking;regulation;social learning;exposure
    Date: 2010–12–01
    URL: http://d.repec.org/n?u=RePEc:dgr:eureri:1765021681&r=net
  3. By: Angelo Mele
    Abstract: <p><p>In this paper, I develop and estimate a dynamic model of strategic network formation with heterogeneous agents. While existing models have multiple equilibria, I prove the existence of a unique stationary equilibrium, which characterizes the likelihood of observing a specific network in the data. As a consequence, the structural parameters can be estimated using only one observation of the network at a single point in time. The estimation is challenging because the exact evaluation of the likelihood is computationally infeasible. To circumvent this problem, I propose a Bayesian Markov Chain Monte Carlo algorithm that avoids direct evaluation of the likelihood. This method drastically reduces the computational burden of estimating the posterior distribution and allows inference in high dimensional models.</p> </p><p><p>I present an application to the study of segregation in school friendship networks, using data from Add Health containing the actual social networks of students in a representative sample of US schools. My results suggest that for white students, the value of a same-race friend decreases with the fraction of whites in the school. The opposite is true for African American students.</p> </p><p><p>The model is used to study how different desegregation policies may affect the structure of the network in equilibrium. I find an inverted u-shaped relationship between the fraction of students belonging to a racial group and the expected equilibrium segregation levels. These results suggest that desegregation programs may decrease the degree of interracial interaction within schools.</p></p>
    Date: 2010–11
    URL: http://d.repec.org/n?u=RePEc:ifs:cemmap:32/10&r=net
  4. By: Michel Grabisch (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)
    Abstract: We generalize a yes-no model of influence in a social network with a single step of mutual influence to a framework with iterated influence. Each agent makes an acceptance- rejection decision and has an inclination to say either ‘yes' or ‘no'. Due to influence by others, an agent's decision may be different from his original inclination. Such a transformation from the inclinations to the decisions is represented by an influence function. We analyze the decision process in which the mutual influence does not stop after one step but iterates. Any classical influence function can be coded by a stochastic matrix, and a generalization leads to stochastic influence functions. We apply Markov chains theory to the analysis of stochastic binary influence functions. We deliver a general analysis of the convergence of an influence function and then study the convergence of particular influence functions. This model is compared with the Asavathiratham model of influence. We also investigate models based on aggregation functions. In this context, we give a complete description of terminal classes, and show that the only terminal states are the consensus states if all players are weakly essential.
    Keywords: Social network, influence, stochastic influence function, convergence, terminal class, Markov chains, aggregation functions.
    Date: 2010–11
    URL: http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00543840_v1&r=net
  5. By: Zouhaïer M'chirgui (CREM, LAREQUAD - Euromed Management - Euromed Management); Olivier Chanel (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579); Didier Calcei (Groupe ESC Troyes - ESC Troyes - ESC Troyes)
    Abstract: Standard-setting coalitions are increasingly composed of rival firms from different sectors and are characterized by simultaneous and/or sequential cooperation and competition among their members. This paper examines why firms choose to belong to two standard-setting coalitions instead of one and what determines the success of a standard coalition. We test empirically for network effect, experience effect, and coopetitive effect in the Blu-ray vs. HD-DVD standard war. We find that the higher the similarity of the members in the coalition, the greater the probability of standard coalition success. Furthermore, relatedness leads to a greater probability of joining both competing coalitions, but at a given degree of knowledge difference, an opposite effect exists.
    Keywords: Blu-ray; HD-DVD; coalition; coopetition; standard war
    Date: 2010–12–07
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-00543972_v1&r=net
  6. By: E. J. Bienenstock; P. Bonacich
    Date: 2010–12–10
    URL: http://d.repec.org/n?u=RePEc:cla:levarc:2031&r=net
  7. By: Michael Lovaglia; John Skvoretz; David Willer; Barry Markovsky
    Date: 2010–12–10
    URL: http://d.repec.org/n?u=RePEc:cla:levarc:2200&r=net
  8. By: John Skvoretz; David Willer
    Date: 2010–12–10
    URL: http://d.repec.org/n?u=RePEc:cla:levarc:2010&r=net
  9. By: K. S. Cook; R. M. Emerson; M. R. Gillmore; T. Yamagishi
    Date: 2010–12–10
    URL: http://d.repec.org/n?u=RePEc:cla:levarc:2199&r=net
  10. By: Martin J Osborne; Rabee Tourky
    Date: 2010–12–09
    URL: http://d.repec.org/n?u=RePEc:cla:levarc:506439000000000050&r=net
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