Technology Push, Demand Pull And The Shaping Of Technological Paradigms - Patterns In The Development Of Computing Technology

An assumption generally subscribed in evolutionary economics is thatnew technological paradigms arise from advances is science anddevelopments in technological knowledge. Demand only influences theselection among competing paradigms, and the course the paradigm afterits inception. In this paper we argue that this view needs to beadapted. We demonstrate that in the history of computing technology inthe 20th century a distinction can be made between periods in whicheither demand or knowledge development was the dominant enabler ofinnovation. In the demand enabled periods new technological (sub-)paradigms in computing technology have emerged as well.enablers of innovation;history of computing;technological paradigms

Why we measure period fertility

Four reasons for measuring period fertility are distinguished: to explain fertility time trends, to anticipate future fertility, to construct theoretical models and to communicate with non-specialist audiences. The paper argues that not all measures are suitable for each purpose, and that tempo adjustment may be appropriate for some objectives but not others. In particular, it is argued that genuine timing effects do not bias or distort measures of period fertility as dependent variable. Several different concepts of bias or distortion are identified in relation to period fertility measures. Synthetic cohort indicators are a source of confusion since they conflate measurement and forecasting. Anticipating future fertility is more akin to forecasting than to measurement. Greater clarity about concepts and measures in the fertility arena could be achieved by a stronger emphasis on validation. Period incidence and occurrence-exposure rates have a straightforward interpretation. More complex period fertility measures are meaningful only if a direct or indirect criterion can be specified against which to evaluate them. Their performance against that criterion is what establishes them as valid or useful

Scientific Research about Climate Change Mitigation in Transport : A critical review

Peer reviewedPostprin

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

The Triple Helix Perspective of Innovation Systems

Alongside the neo-institutional model of networked relations among universities, industries, and governments, the Triple Helix can be provided with a neo-evolutionary interpretation as three selection environments operating upon one another: markets, organizations, and technological opportunities. How are technological innovation systems different from national ones? The three selection environments fulfill social functions: wealth creation, organization control, and organized knowledge production. The main carriers of this system-industry, government, and academia-provide the variation both recursively and by interacting among them under the pressure of competition. Empirical case studies enable us to understand how these evolutionary mechanisms can be expected to operate in historical instance. The model is needed for distinguishing, for example, between trajectories and regimes

Functions of innovation systems as a framework to understand sustainable technological change: empirical evidence for earlier claims

Understanding the emergence of innovation systems is recently put central in research analysing the process of technological change. Especially the key-activities that are important for the build up of an innovation system receive much attention. These are labeled ‘functions of innovation systems’. In most cases the authors apply this framework without questioning its validity. This paper builds on five empirical studies, related to renewable energy technologies, to test whether the functions of innovation systems framework is a valid framework to analyse processes of technological change. We test the claim that a specific set of functions is suitable. We also test whether the claim made in previous publications that the interactions between system functions accelerate innovation system emergence and growth is valid. Both claims are confirmed.

Nanotechnology Publications and Patents: A Review of Social Science Studies and Search Strategies

This paper provides a comprehensive review of more than 120 social science studies in nanoscience and technology, all of which analyze publication and patent data. We conduct a comparative analysis of bibliometric search strategies that these studies use to harvest publication and patent data related to nanoscience and technology. We implement these strategies on 2006 publication data and find that Mogoutov and Kahane (2007), with their evolutionary lexical query search strategy, extract the highest number of records from the Web of Science. The strategies of Glanzel et al. (2003), Noyons et al. (2003), Porter et al. (2008) and Mogoutov and Kahane (2007) produce very similar ranking tables of the top ten nanotechnology subject areas and the top ten most prolific countries and institutions.nanotechnology, research and development, productivity, publications, patents, bibliometric analysis, search strategy