nep-net New Economics Papers
on Network Economics
Issue of 2010‒08‒06
five papers chosen by
Yi-Nung Yang
Chung Yuan Christian University

  1. A Positive Theory of Network Connectivity By David Levinson; Arthur Huang
  2. Network Structure and Activity Spaces By Pavithra Parthasarathi; Hartwig Hochmair; David Levinson
  3. Network Structure and Metropolitan Mobility By Pavithra Parthasarathi; David Levinson
  4. Liquidity costs and tiering in large-value payment systems By Adams, Mark; Galbiati, Marco; Giansante, Simone
  5. An empirical model for strategic network foundation By Nicholas Christakis; James Fowler; Guido Imbens; Karthik Kalyanaraman

  1. By: David Levinson; Arthur Huang (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)
    Abstract: This paper develops a positive theory of network connectivity, seeking to explain the micro-foundations of alternative network topologies as the result of self-interested actors. By building roads, landowners hope to increase their parcelsÕ accessibility and economic value. A simulation model is performed on a grid-like land use layer with a downtown in the center, whose structure resembles the early form of many Midwest- ern and Western (US) cities. The topological attributes for the networks are evaluated. This research posits that road networks experience an evolutionary process where a tree-like structure first emerges around the centered parcel before the network pushes outward to the periphery. In addition, road network topology undergoes clear phase changes as the economic values of parcels vary. The results demonstrate that even without a centralized authority, road networks have the property of self-organization and evolution, and, that in the absence of intervention, the tree-like or web-like nature of networks is a result of the underlying economics.
    Keywords: road network, land parcel, network evolution, network growth, phase change, centrality measures, degree centrality, closeness centrality, betweenness cen- trality, network structure, treeness, circuitness, topology
    JEL: R41 R48 R53
    Date: 2010
  2. By: Pavithra Parthasarathi; Hartwig Hochmair; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)
    Abstract: This research analyzes the influence of network structure on household spatial patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Twin Cities and South Florida to compile measures of network structure. Statistical regression models test the relationship between network structure and travel. The results show that network design does influence travel, after controlling for other non-network based measures. Results from this analysis can be used to understand how changes in network can be used to bring about desired changes in travel behavior.
    Keywords: Transportation Geography, Network Structure, Circuity, Accessibility
    JEL: R41 R48 R53
    Date: 2010
  3. By: Pavithra Parthasarathi; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)
    Abstract: This research develops quantitative measures that capture various aspects of underlying network structure, using aggregate level travel data from fifty metropolitan areas across the U.S. The influence of these measures on system performance is then tested using statistical regression models. The results corroborate that the quantitative measures of network structure affect the system performance. The results from this analysis can be used to develop network design guidelines that can be used to address current transportation problems.
    Keywords: Network structure, mobility, congestion, accessibility, travel behavior, transportation geography
    JEL: R41 R48 R53
    Date: 2010
  4. By: Adams, Mark (Bank of England); Galbiati, Marco (Bank of England); Giansante, Simone (CCFEA, University of Essex)
    Abstract: This paper develops and simulates a model of the emergence of networks in an interbank, RTGS payment system. A number of banks, faced with random streams of payment orders, choose whether to link directly to the payment system, or to use a correspondent bank. Settling payments directly on the system imposes liquidity costs which depend on the maximum liquidity overdraft incurred during the day. On the other hand, using a correspondent entails paying a flat fee, charged by the correspondent to recoup liquidity costs and to extract a profit. We specify a protocol whereby one bank in each period can revisit its choice whether to link directly to the system, or to become clients of other banks, thus generating a dynamic client-correspondent network. We simulate this protocol, observing the emergence of different network structures. The liquidity pricing regime chosen by a central bank is found to affect the tiering process and the network structures it produces. A calibration exercise on data from the UK CHAPS system suggests that the model is able to generate realistic predictions, ie a network topology similar to that observed in reality, driven solely by the underlying pattern of payments and the structure of liquidity costs.
    Keywords: Tiering; liquidity cost; large-value payment system; RTGS; network formation
    JEL: C70 G20
    Date: 2010–07–29
  5. By: Nicholas Christakis; James Fowler; Guido Imbens (Institute for Fiscal Studies and Harvard University); Karthik Kalyanaraman (Institute for Fiscal Studies and UCL)
    Abstract: <p>We develop and analyze a tractable empirical model for strategic network formation that can be estimated with data from a single network at a single point in time. We model the network formation as a sequential process where in each period a single randomly selected pair of agents has the opportunity to form a link. Conditional on such an opportunity, a link will be formed if both agents view the link as beneficial to them. They base their decision on their own characateristics, the characteristics of the potential partner, and on features of the current state of the network, such as whether the the two potential partners already have friends in common. A key assumption is that agents do not take into account possible future changes to the network. This assumption avoids complications with the presence of multiple equilibria, and also greatly simplifies the computational burden of anlyzing these models. We use Bayesian markov-chain-monte-carlo methods to obtain draws from the posterior distribution of interest. We apply our methods to a social network of 669 high school students, with, on average, 4.6 friends. We then use the model to evaluate the effect of an alternative assignment to classes on the topology of the network.</p>
    Date: 2010–06

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