Nothing to gain, plenty to lose

Empirical research examining the proposed privatisation of the NSW electricity transmission and distribution businesses has found the asset recycling strategy is likely to negatively impact the state’s credit rating over the medium to long term. The analysis also examined the relative efficiency of public and private networks, finding that once the physical span of each network was considered — essentially the number of kilometres covered — publicly owned electricity networks currently operate more efficiently that privately owned assets in other states. Written by Market Economics managing director Stephen Koukoulas, who has more than 25 years experience as an economist in government and banking, the report found there was no logical case for privatisation, with many arguments based on questionable assumptions and generalisations. The report also highlights that the electricity transmission and distribution businesses currently provide a relatively stable and low-risk cash flow to the budget. The report made a number of key findings, including: • Privatising NSW’s transmission and distribution assets is likely to drive up prices due to higher overheads in comparable privatised businesses; • The physical span of different networks is the single largest factor behind variations in both operational and capital expenditure; • NSW’s publicly owned networks outperforms privately owned peers on operating expenses; • Publicly owned networks appear more willing to engage in long-term planning when undertaking capital expenditure; and • Privatising NSW’s electricity network assets offers little short term budgetary gain and could well be detrimental over the medium to long term

A network diffusion ranking family that includes the methods of Markov, Massey, and Colley

We present a one parameter family of ratings and rankings that includes the Markov method, as well as the methods of Colley and Massey as particular cases. The rankings are based on a natural network diffusion process that unites the methodologies above in a common framework and brings strong intuition to how and why they differ. We also explore the behavior of the ranking family using both real and simulated data

Network-Based Criterion for the Success of Cooperation in an Evolutionary Prisoner\u27s Dilemma

We consider an evolutionary prisoner\u27s dilemma on a random network. We introduce a simple quantitative network-based parameter and show that it effectively predicts the success of cooperation in simulations on the network. The criterion is shown to be accurate on a variety of networks with degree distributions ranging from regular to Poisson to scale free. The parameter allows for comparisons of random networks regardless of their underlying topology. Finally, we draw analogies between the criterion for the success of cooperation introduced here and existing criteria in other contexts

Reply To Comment on \u27Cooperation in an Evolutionary Prisoner\u27s Dilemma on Networks with Degree-Degree Correlations\u27

We respond to the comment of Zhu et al. [Phys. Rev. E 82, 038101 (2010)] and show that the results in question are not misleading

Evolution of Cooperation through the Heterogeneity of Random Networks

We use the standardized variance (nu_{st}) of the degree distribution of a random network as an analytic measure of its heterogeneity. We show that nu_{st} accurately predicts, quantitatively, the success of cooperators in an evolutionary prisoner\u27s dilemma. Moreover, we show how the generating functional expression for nu_{st} suggests an intrinsic interpretation for the heterogeneity of the network that helps explain local mechanisms through which cooperators thrive in heterogeneous populations. Finally, we give a simple relationship between nu_{st} , the cooperation level, and the epidemic threshold of a random network that reveals an appealing connection between epidemic disease models and the evolutionary prisoner\u27s dilemma

Identifying group contributions in NBA lineups with spectral analysis

We address the question of how to quantify the contributions of groups of players to team success. Our approach is based on spectral analysis, a technique from algebraic signal processing, which has several appealing features. First, our analysis decomposes the team success signal into components that are naturally understood as the contributions of player groups of a given size: individuals, pairs, triples, fours, and full five-player lineups. Secondly, the decomposition is orthogonal so that contributions of a player group can be thought of as pure: Contributions attributed to a group of three, for example, have been separated from the lower-order contributions of constituent pairs and individuals. We present detailed a spectral analysis using NBA play-by-play data and show how this can be a practical tool in understanding lineup composition and utilization

Cooperation in an Evolutionary Prisoner\u27s Dilemma on Networks with Degree-Degree Correlations

We study the effects of degree-degree correlations on the success of cooperation in an evolutionary prisoner\u27s dilemma played on a random network. When degree-degree correlations are not present, the standardized variance of the network\u27s degree distribution has been shown to be an accurate analytical measure of network heterogeneity that can be used to predict the success of cooperation. In this paper, we use a local-mechanism interpretation of standardized variance to give a generalization to graphs with degree-degree correlations. Two distinct mechanisms are shown to influence cooperation levels on these types of networks. The first is an intrinsic measurement of base-line heterogeneity coming from the network\u27s degree distribution. The second is the increase in heterogeneity coming from the degree-degree correlations present in the network. A strong linear relationship is found between these two parameters and the average cooperation level in an evolutionary prisoner\u27s dilemma on a network