Competition Policy In Network Industries: An Introduction

We discuss issues of the application of antitrust law and regulatory rules to network industries. In assessing the application of antitrust in network industries, we analyze a number of relevant features of network industries and the way in which antitrust law and regulatory rules can affect them. These relevant features include (among others) network effects, market structure, market share and profits inequality, choice of technical standards, relationship between the number of active firms and social benefits, existence of market power, leveraging of market power in complementary markets, and innovation races. We find that there are often significant differences on the effects of application of antitrust law in network and non-network industries.networks, network effects, public policy, antitrust, telecommunications, technical standards

Competition Policy in Network Industries: An Introduction

The author discusses issues of the application of antitrust law and regulatory rules to network industries. In assessing the application of antitrust in network industries, we analyze a number of relevant features of network industries and the way in which antitrust law and regulatory rules can affect them. These relevant features include (among others) network effects, market structure, market share and profits inequality, choice of technical standards, relationship between the number of active firms and social benefits, existence of market power, leveraging of market power in complementary markets, and innovation races. The author finds that there are often significant differences on the effects of application of antitrust law in network and non-network industries.

Parametric Macromodels of Differential Drivers and Receivers

This paper addresses the modeling of differential drivers and receivers for the analog simulation of high-speed interconnection systems. The proposed models are based on mathematical expressions, whose parameters can be estimated from the transient responses of the modeled devices. The advantages of this macromodeling approach are: improved accuracy with respect to models based on simplified equivalent circuits of devices; improved numerical efficiency with respect to detailed transistor-level models of devices; hiding of the internal structure of devices; straightforward circuit interpretation; or implementations in analog mixed-signal simulators. The proposed methodology is demonstrated on example devices and is applied to the prediction of transient waveforms and eye diagrams of a typical low-voltage differential signaling (LVDS) data link

Electricity transmission: an overview of the current debate

Electricity transmission has emerged as critical for successfully liberalising powermarkets. This paper surveys the issues currently under discussion and provides a framework for the remaining papers in this issue. We conclude that signalling the efficient location of generation investment might require even a competitive LMP system to be complemented with deep connection charges. Although a Europe-wide LMP system is desirable, it appears politically problematic, so an integrated system of market coupling, possibly evolving by voluntary participation, should have high priority. Merchant investors may be able to increase interconnector capacity, although this is not unproblematic and raises new regulatory issues. A key issue that needs further research is how to better incentivize TSOs, especially with respect to cross-border issues

Computational neural learning formalisms for manipulator inverse kinematics

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints