Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well. CONTENTS

This paper details an empirical pilot study that explores the development of several conceptual measures and models regarding intellectual capital and its impact on business performance. The objective of this pilot study is to explore the development of items and constructs through principal components analysis and partial least squares (PLS). The final retained, subjective measures and optimal structural specification show a valid, reliable, significant and substantive causal link between dimensions of intellectual capital and business performance. These results should help both academics and practitioners more readily understand the components of intellectual capital and provide insight into developing and increasing it within an organization. Suggestions are then made to advance and improve this research programme

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This chapter examines the evolution of open source software and how their evolutionary patterns compare to prior studies of software evolution of proprietary (or closed source) software. Free or open source software (F/OSS) development focuses attention to systems like the GNU/Linux operating system, Apache Web server, and Mozilla Web browser,

This chapter draws attention to the question of determining the conditions when free/open source software development may represent a significant alternative to modern software engineering techniques for developing large-scale software systems. F/OSSD often entails shorter development times that can produce higher quality systems, and incur lower costs than may be realized through developing systems according SE techniques. Understanding why and how this may arise is the focus of this chapter. It presents, analyzes, and compares data collected from different F/OSSD projects, including an in-depth case study, to help develop such an understanding. The goal of this chapter is to determine the circumstances and conditions when F/OSSD represents a viable alternative to SE for the development of complex software systems. In particular, the chapter seeks to contrast differences observed in the arrangement and tooling of their respective software development practices, production resources, technical regimes, and community practices in which they are embedded. This in turn may then help identify how the practice and principles of SE might be improved.

Evolutionary simulation modelling is presented as a methodology involving the application of modelling techniques developed within the artificial sciences to evolutionary problems. Although modelling work employing this methodology has a long and interesting history, it has remained, until recently, a relatively underdeveloped practice, lacking a unifying theoretical framework.

Few will argue that the epi-phenomena of biological systems and socio-economic systems are anything but complex. The purpose of this Feature is to examine critically and contribute to the burgeoning multi-disciplinary literature on markets as complex adaptive systems (CAS). The new sciences of complexity, the principles of self-organisation and emergence along with the methods of evolutionary computation and artificially intelligent agent models have been developed in a multi-disciplinary fashion. The cognoscenti here consider that complex systems whether natural or artificial, physical, biological or socio-economic can be characterised by a unifying set of principles. Further, it is held that these principles mark a paradigm shift from earlier ways of viewing such phenomenon. The articles in this Feature aim to provide detailed insights and examples of both the challenges and the prospects for economics that are offered by the new methods of the complexity sciences. The applicability or not of the optimisation framework of conventional economics depends on the domain of the problem and in particular the modern theories behind non-computability are outlined to explain why adaptive or emergent methods of computation and agent-based

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Abstract This paper proposes a multiactivity generalisation of the Nelson–Winter model, or the NW model, in order to turn the attention of evolutionary minded economists toward specialisation and exchange, the emergence of markets for intermediate goods, the specialisation of R&D, and other issues of multisectoral growth and development. The argument and the solution is presented in four steps. First, there is a discussion of some practical difficulties and core theoretical problems in relation to the standard NW model of Schumpeterian competition. The conclusion is that this model gives an ad hoc solution to the tendency of evolutionary models to produce monopoly and that is has not really confronted the ‘knife-edge ’ problems of the underlying Leontief technology. Thus there is still a need to confront what may be called the diversity paradox and the Leontief technology paradox of evolutionary modelling. Second, the paper develops a condensed version of the NW model that serves to highlight the theoretical problems and as a platform for the proposed generalisation. This version of the NW model, the LNW model, includes only labour and knowledge. With given technologies the LNW model shows standard replicator dynamics while it can also be used as a testbed for exploring different R&D regimes. Third, the bare bones of the multiactivity generalisation of the NW model, the MNW model, is presented. This presentation starts from firms

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