Network

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Synopsis: Network: Network:

two contributions addressed the broadening of well-established quantitative methods to mesh with qualitative methods. This approach should provide enhancement to models in the future.

In particular the increased modularity of technological solutions that is a key tenet of the network economy is an argument for considering elementary options as more loosely coupled.

a project funded by the DG JRC-IPTS and conducted by the ESTO network in 2002/2003 34.17 For the Nordic Hydrogen Energy Foresight,

and in the emergence and performance of R&d collaboration networks. 482 E. A. Eriksson, K. M. Weber/Technological forecasting & Social Change 75 (2008) 462 482

In addition, extensive network analyses were conducted to support the identification of possible collaboration networks and the development of joint calls for proposals.

Drawing on the results from the Woodwisdom-Net consultation process, we discuss the broader potential of Internet-based decision support tools

however, may foster high expectations concerning shared visionbuilldin and formation of new research and technology development (RTD) networks.

we examine issues in the organization of foresight activities within European coordination tools such as Integrated Projects, Networks of Excellence, ERA NETS, European Technology platforms and Technology initiatives

it can also contribute to the development of complementary value networks based on different technological competencies 3. Broadly seen,

which served to highlight the benefits of creating international RTD networks. Priority setting: The plans to establish an international research program meant that the funding organizations had to define focal research themes for European RTD collaboration.

To some extent, network-building was supported also by listing the registered participants on the website of the consultation process. 7 3. 2. 2. 1. Solicitation of research issues.

may assist in the formation of new collaborative networks. For each research issue, such an analysis conveys which research groups are keen on participating in corresponding project consortia,

Information of this kind be can be exploited to facilitate the formation of newcollaborative networks for instance by encouraging the research groups to respond to calls for proposals in a full awareness of what other groups in other countries have shared interests.

selected foresight activities and selected process elements such as decision support for the bottom up thematic prioritization and the formation of new networks may shift the locus of activities closer to national actors.

Benchmarking Financing R&d Provision of R&d infrastructure Technology transfer and innovation diffusion Legal frameworks (IPRS, standards and regulation) Integrated projects Networks of excellence Methodology:

The role of standardisation is highest for the realisation of highly reliable network systems capable of protecting the privacy

and secrecy of individuals and groups from the intrusion of ill-intentioned network intruders followed by online seal-free (signature-free) document preparation services for various official documents such as contracts which are provided via a network based on security

which are available over networks. Here, selfregullatio via standardisation is obviously not sufficient. In addition recycling of computers can be 4 Based on the results of the First German Delphi study 44, the Fifth Japanese Delphi study 45,

of highly reliable network systems capable of protecting the privacy and secrecy of individuals and groups from the intrusion of ill-intentioned network intruders. 2010 4. 88 4. 17 3. 13 1. 86 4. 28 Realisation of an environment in

which the unlimited utilisation of high-capacity networks (150 Mbps) for around 15€/month or less is possible. 2012 3. 88 3. 73 2. 77 2. 73 3

. 20 Widespread use of online seal-free (signature-free) document preparation services for various official documents such as contracts which are provided via a network based on security technology capable of achieving both privacy

protection and verification. 2010 4. 03 3. 30 3. 83 2. 13 3. 90 Widespread use of systems which facilitate multimedia communication from anywhere in the world using pocketbook-size portable terminals

activities involving network ethics, such as copyright infringement concerning multimedia software use over a network and the violation of privacy. 2012 3. 47 3. 13 3. 70 1. 73 2

. 40 Practical use of systems capable of understanding and automatically checking the content of image data unsuitable for children

which are available over networks 2011 3. 84 3. 66 4. 13 1. 73 3. 00 A service that evaluates the security of the e-commerce system of individual companies

sophistication of e-commerce networks and improved efficiency of business cycle times, resulting in a dramatic reduction of inventory risk for companies. 2014 3. 65 3. 42 1. 86 2. 35 2. 38 All public transport bookings, confirmation of transport services,

whereas the latter is able to push network effects. Based on correlation analysis referring to the 21 items,

The work is linked to a programme of Future oriented technology assessment (FTA ACTIVITIES coordinated within a European nanotechnology research network.

Our tool can be applied in strategic management of research andr&dat the level of science-to-industry networks.

This is especially the case for the recent European Networks of Excellence and Technology platforms have to deal with:

Out project is embedded in a particular network of excellence on nanotechnology called Frontiers. Among the central aims of the Frontiers Network-of-Excellence (Noe) programme1 are a. the coordination of research activities in the research institutes that comprise the Noe (alignment;

and b. the enabling of interactions between industry in creating and sustaining an innovation chain. These aims pose tremendous managerial challenges:

This includes actors outside the network, in the case of nanotechnology, start-ups and SMES which have a lot at stake in entering such risky innovation chains.

Many networks and platforms have dedicated working groups or programmes on foresight, strategic planning and anticipation of societal and ethical hurdles to innovation based on emerging technologies.

which allow the Frontiers network to develop strategies for a number of different issues relevant to particular areas within nanotechnologies for the life sciences.

Our paper centres on one Frontiers FTA project on the stimulation 1 The EC 6th Framework programme Network of Excellence Frontiers is a network of 14 European research institutes

1. developing recommendations for the Frontiers research network; and 2. exploring strategies for specific actor groups (SMES and researchers.

unless start-ups and SMES are part of networks which are able to commission roadmaps for dissemination among their members5,

This broadens the previous notions of path from lock in to the co-evolution of interactions of networks of actors with attempts at mindful deviation. 11 Characteristics of path dependency

when there is alignment. 14 This is in keeping with the'actants'notion as network nodes in Actor-Network theory 49.522 D. K. R. Robinson,

MPM-1) the technical dimension of the MPM was based on desk research as a map to be used for the Frontiers network to aid strategy articulation in research and science-to-industry linkages,

and use them to direct the portfolio of research lines within the network would be attractive (management issue 1 see Section 1). In addition,

and enable the network to constructively stimulate innovation chains stemming from its research choices. Eventually, this allows targeting of research and the negotiation with various relevant innovation chain actors.

1. Developing strategic information for the Frontiers network to include within the framework of MPM-1

Networks of start-ups and SMES related to micro and nanotechnology (cf Minacned) already exist. Thus a form of co-option would be desired the goal to take the step of integration together

3) Start-ups creating network;(4) Heterogeneous clusters. 531 D. K. R. Robinson, T. Propp/Technological forecasting & Social Change 75 (2008) 517 538 move towards a generic platform

and nanotechnology SME networks such as Minacned. 21 Innovation chain 4 is currently occurring at the University of Twente (NL) where a start-up company with a specific sensor is acting as platform integrator.

and builds its network based around this. The IP issue can be generalised to many projected nanotechnology innovations

and builds its network around them with a view to transition to a company after proof of concept. 6. Discussion

For reflexive alignment within research networks or firms it would seem advantageous that a‘strategy support system'(SSS) should be developed as a toolbox to be used without external help.

This generic term denotes a toolbox specifically addressing the needs of organizations and networks of organizations with respect to strategic intelligence, possibilities for alignment,

This network level strategy support system is somewhat abstract from specific technological issues, such as cell-on-a-chip;

growing with each new FTA exercise at this network level. MPM can be of use at the level of research group leaders, portfolio managers,

Improving Distributed intelligence in Complex Innovation systems, Final Report of the Advanced Science and Technology policy Planning Network (ASTPP.

Sci. 17 (2)( 1987) 257 293.44 M. Callon, Techno-economic Networks and Irreversibility, in: John Law (Ed.),A Sociology of Monsters?

Essays on Power, Technology and Domination, Routledge, London, 1991, pp. 132 161.45 M. Callon, The Dynamics of Techno-economic Networks, in:

Expectations, agendas and networks in lab-on-a-chip technologies, Technol. Anal. Strateg. Manag. 18 july September 2006) Number 3 4. 49 M. Callon, J. Law, A. Rip, Mapping the dynamics of science and technology, The Macmillan Press Ltd.

Cheltenham, 2005, pp. 251 281.67 D. K. R. Robinson, A. Rip, V. Mangematin, Technological agglomeration and the emergence of clusters and networks in nanotechnology, Res.

Facilitate networking Form new networks and provoke new ones Creation of new networks and clusters SC:

Increase levels of social capital a Based on the respective lists provided in 14.546 E. Amanatidou,

the creation of action networks; and the ownership of action plans by stakeholders and sponsors.

if the action networks formed were to become self-sustainable, but also acknowledged that this was dependent upon the enthusiasm of the sponsor agency,

networks and actor alignment Given the peculiar nature of the task at hand, namely the search for diverse impacts (from changes in social capital to more informed publics and better networking) that may

whether networks are a new form of governance coordination, lying somewhere between markets and hierarchies 19 21.

All try to analyse the nature of networks. Whether networks are radically different from markets

and hierarchies is still a moot point, however, since markets and hierarchies have had always networking elements associated with them.

however, concerning the factors necessary for networks to be created and sustained. The factors motivating the creation of networks are summarised by Baker 26 as (a) pressures to access know-how

and promote new knowledge and learning;(b) coping with greater Fig. 5. An impact assessment framework for foresight systems capable of enhancing a more participatory‘knowledge society'.

in network operation trust seems to be the most important attribute (Bradach and Eccles, 1991 as quoted in 27).

Network operation has to find a balance between flexibility and coordination efficiency, while also having to tackle problems of legitimacy, accountability and implementation.

As in the private sector, networks involving public administrations are constituted primarily for the purposes of learning, the development of expertise and the exploitation of complementary resources and know-how.

Molina 28 studied networks and alignments in large-scale European projects using the‘socio-technical constituencies'12 approach of organisational behaviour theory.

He claims constituencies differ from communities and networks, because these refer to people or institutions alone,

and also from actor-networks, which put both animate and inanimate‘actors'in the same category.

flexibility and the sustainability of the networks created. The inputs and outputs should exploit the compatibility and complementarity of the available areas of expertise.

network forms of organisation, Research in Organisational Behavior, vol. 12,1990, pp. 295 336.20 L. Blatter, Beyond hierarchies and networks:

the logic and limits of Network Forms of Organisation, Oxford university Press, Oxford, 2003.22 S. Wasserman, K. Faust, Social networks Analysis:

Organisational Alliances, Partnerships and Networks in Management Benchmarking Study, Washington Research Evaluation Network, www. wren. network. net/resources/benchmarking. 27 J. Airaksinen, A. Haveri, Networks and hierarchies in inter-municipal co-operation.

Are networks really light and flexible and hierarchies sticky and rigid? paper presented in the conference of European Group of Public Administration, Lisbon, September 2003.28 A. Molina, Sociotechnical constituencies as processes of alignment:

The rise of a large-scale European information technology initiative, Technology in Society 17 (4)( 1995) 385 412.

products, firms, value chains (production networks),(sub-national) regions, nations, or even larger entities. This problem obviously cannot be solved here. 28 In launching the discussion on the priorities for the new generation of cohesion policy programmes,

and not a particularly active one in various networks, a flexible, dynamic, highly successful university,

not even from the EU Integration of RTDI activities (across national borders) Only a few‘world-class'EU universities can join global networks at the forefront of RTDI activities Widely occurs across the EU and globally;

When preparing ourselves, our institutions and our networks for this phenomenon, our inquisitive intellects and our social networks are both asking some important questions:

who have not as yet widely embraced the predictive power of networks and who seem uncomfortable with the emerging complexity of innovation systems as the key target or client for adaptive policies and new approaches.

10.1016/j. techfore. 2009.10.004 new power of IT and network analytical approaches, but it also directly aims its messages at policy makers responsible for designing more effective strategies for the deployment of public funds for R&d and those responsible for forecasting where and how to do this no small task indeed!

and Chair of the Foresight Synergy Network of Canada. Prior to joining DRDC, he was Director of S&t Foresight for the Office of the National science Advisor to the Prime minister of Canada and Leader of the Office of Technology foresight for the National research council of Canada.

Annele Eerola is a Senior Research scientist of the knowledge center‘Organisations, Networks and Innovation systems'at VTT Technical research Centre of Finland.

The actual economic and institutional arrangements necessary to create flexible and distributed networks may have been captured in the regional development literature 13.

Such networks require special technological and infrastructural capabilities to succeed in this emerging environment. In the following paragraphs some ideas about the organization of technological knowledge is described;

decentralized actors may be stored in a network form. This network, although highly diffuse, may be accessed by all players for personal as well as community betterment.

Hierarchies are one form of technological structure confirmed by theories and practice. A hierarchy is one structure of many that have been used for technological integration 14.

The network data in the raw is not useful for this purpose. A structured representation of technology is needed for multiple reasons:

An unstructured network contains many parameters, which are hard to visualize and interpret. The technology analyst requires structured information,

therefore, are techniques for extracting these networks, and accurately structuring the knowledge so that it can be used for analysis, design and forecasting.

Nonetheless, the purpose of this article is not to advocate flexible networks of innovating firms as a preferred form of innovative activity.

Using the model is a two-part process of simulating a range of possible networks specified by the model,

Extensive technical details of this data structure are available in the literature on complex networks. A literature on hierarchical random graphs,

and their use in managing information about complex networks is emerging 21 24. Clauset 21 for instance, provides a useful survey article on the random hierarchical graph.

These four nodes may be connected in sixtyfoou possible networks, representing various combinations of the component technologies.

Rather than a full enumeration of links, any observed network of these four component technologies can be represented compactly by introducing three parent nodes, each with their own associated probabilities of linkage.

or that node C is connected to D. The resultant hierarchical random graph has replaced six bits of information about network connectivity with three probabilities:

& Social Change 76 (2009) 1138 1149 The hierarchical representation of the data grows more attractive as the network grows larger,

However, we no longer have a single unique specification of the network the hierarchical random graph describes an ensemble of the sixty four possible graphs.

while others remain unlikely. 3. 2. Simulation of new networks using the graph The figure above presents two possible realizations of the hierarchical random graph shown in Fig. 1. These realizations are made by randomly establishing the presence

This network configuration should be expected to be observed 4. 3%of the time, or roughly one in 24 times.

but unlikely realization of the network since most of the high probability connections did not actually occur (Fig. 2). This network occurs 0. 8%of the time,

ranging from perfectly deterministic networks, to highly random networks. According to the specification of the model there may be little or no hierarchical structure,

or a network which is structured richly across multiple layers. Fig. 3, below (adapted from 21),

demonstrates how very different networks may be encoded using the hierarchical random graph format. The left-most graph is a flat network,

representing a random network where every node is likely to connect to every other network. The middle network shows clear clustering

and yet is still a flat network. Members of the three groups in this network are likely to connect to each other,

but are unlikely to connect to members of other groups. The right most network shows organization at multiple scales:

there are clusters, within clusters, within clusters. All three of these networks are assortative: the closer the position on the tree, the more likely two nodes are to connect.

Disassortative networks are also possible: progressing down the tree represents a progressive differentiation of members.

Thus, the hierarchical random graph is a very expressive formalism capable of capturing many possible network relationships. 3. 3. Fitting graphs to data For each of these structures we must also estimate the associated probabilities of network linkages

at each of the parent nodes. The best fit is achieved by fitting probabilities according to the actual proportion of linkages observed in the data.

This is the maximum likelihood estimate of the model parameters given the data 21. With only fifteen possibilities, we can exhaustively search the space of possible network structures.

There are only forty-five probabilities which must be estimated; three for each of the model structures. However the combinatorics will explode as we expand our examples to more realistic networks with multiple child nodes.

An exhaustive search is no longer possible. We therefore need a way to structure the search to spend most of our time on the most likely network structures.

1). In this equation the likelihood of a particular observation is observed dependent on the network (D), the specified series of probabilities in the hierarchical random graph (p),

The sufficient statistics for the observed network can be calculated. Every possible network consistent with the data can be enumerated,

and the likelihood of each network model given the data can be calculated. The analyst can then choose the network

or networks which provide the best fit to the data. Larger networks prevent this exhaustive search process.

Nonetheless, a systematic technique for searching through the space of models is still necessary. A Monte carlo simulation provides a systematic search process which guarantees several desirable properties.

The procedure can start with any proposed network and can be restarted anew at any stage in the process.

The search process requires only a limited look ahead. This is accomplished by successive stages of simulation (to evaluate potential alternatives),

and then model fitting (to determine the most desirable alternatives). The search procedure includes a measure of network relatedness.

A series of stepwise operations can transform one hierarchical graph into any other hierarchical random graph.

Thus, they argue that the behavior of the system is determined exclusively by the structure of the network.

There is therefore a corresponding interest in the morphology of the network and in particular network properties.

A few network properties have received considerable attention in the literature. First is the average degree of nodes,

which is a measure of network connectivity. The second is the clustering coefficient, which is a measure of excess links between closely related nodes.

The final measure is average network diameter which is the minimum number of hops it takes to get between any two nodes in the network.

These comparative network measures are used to compare and contrast networks originating from very different social, physical,

or natural origins. In the following few paragraphs a comparative analysis of the Wikipedia morphology is provided.

This is useful as a point of comparison between this study and others, even if a strict structuralist account of the data is adopted not.

A graphical presentation of a subset of the Wikipedia network near AJAX is given. Network characteristics of this subset of the Wikipedia are provided.

Such results are intended as descriptive statistics which can be interpreted only in light of a more elaborate model of the data.

In short, some descriptive statistics of the network are provided despite the fact that the author does not endorse a structuralist account of the data.

The network grows rapidly in size. The seed is but a single page; this page contains 41 hyperlinks.

This network may be visualized using the Pajek software, with each node representing a single Wikipedia page,

The visualized graph of this network is shown below (Fig. 4). On the left is the network with the 41 pages one hop away from the Wikipedia Ajax (Programming) age.

On the right is the network with the 2973 pages two hops or fewer away from the Ajax (Programming) page.

Many of these nodes in this expanded network are now very remote in content from Internet technologies.

Fig. 4. Expanding network of hyperlinks in Wikipedia. 1143 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 At least three other networks have been studied in the context

These include a metabolic network, a grassland ecology, and a social network of terrorists. Key attributes of the network include average degree k, average clustering coefficient (C),

and average network diameter (d). It is interesting to compare this Wikipedia sample with these other known networks.

See Table 1. The network shares key characteristics with the three previous networks examined. Like the network ecology

this sampled network has a relatively low degree. However, the complete Wikipedia content network probably has a much higher degree perhaps a degree as high as 10.

Like the terrorist network, the nodes are coupled tightly. And like the metabolic network, there is a relatively high average distance between all the nodes in the network.

Perhaps unlike these other networks, the Wikipedia network is truncated clearly from a much larger network.

The Wikipedia network is mixed in character although more disassortative than assortative. The network is therefore similar to the grasslands ecology network

which has previously been shown to be disassortative 21.4.2. Fitting the data The component technologies of Ajax may be represented in hierarchical random graph form.

We apply the Monte carlo simulation procedure of Clauset 21 to fit the 41 pages within one hop of Ajax (Programming) into a hierarchical random graph.

Our goal in this analysis is to use the hierarchical structure to anticipate new changes in this field of information technology.

The Monte carlo simulation converges to a final likelihood of-167.938, after a million runs of the simulation.

The algorithm runs rapidly on a Pentium 1. 73 GHZ processor, with a clock speed of 795 MHZ and 512 MB of RAM:

A visualization of the resultant hierarchical random graph is shown below in Fig. 5. Table 1 Comparative analysis of network characteristics.

Network K c D T. pallidum 4. 8 0. 063 3. 69 Terrorists 4. 9 0. 361 2. 58 Grassland 3

structures which may remain stable even as the network changes or expands to include new nodes.

(or as we identify them in this article the nodes of the network), and change in the linkages between the concepts.

Claim Claimant Data Scientific and technical knowledge consists of a set of interdependent claims Popper 31 Networks of knowledge can be structured readily from science

and technology databases using techniques such as hierarchical random graphs Knowledge claims are heterogenous in character Derrida 32 Networks built upon science

which they struggle to encode within the network of scientific progress Polanyi 35 Changes in technology in this case are manifested in changes in network structure Knowledge is built upon the configuration of knowledge claims,

may contribute to an open innovation paradigm where firms work together as part of an extended technological network 11.

This technological network clearly demonstrated technologies internal and external to the core technology network. The disassortative character of this network means that architectural innovation is much likely to occur from external technologies.

Other technological architectures may be very different: these might be assortative networks which favor the use of technologies which are internal,

and therefore already present within the system. The author suggests that the original conception of architectural change,

The consequences of assortative and disassortative architectural networks may be very different across firms and industries.

The section to follow examines the claim that knowledge resides on networks, as a series of claims or propositions.

or resident in a diffuse way across a network of scientific claims. The role of scientists, engineers,

and innovators is to enhance the coherence of this network. The progress of science is such that claims

Changes to the network can stimulate revolutionary progress. These claims are given in Table 3. Corresponding evidence from this case are displayed in this table,

This perspective then, suggests that knowledge is a network of interlocked claims. Only some of these claims may be anchored in observation,

Since the subjectivist perspective on knowledge is conditional on entirety networks of knowledge claims, tacit knowledge is required

The tacit knowledge of actual practice is encoded only partially in the network of competing claims. The raw data of a hierarchical random graph,

when applied to a network of science and technology information, is likely to be material as well as semiotic in character. 1147 S w. Cunningham/Technological forecasting

which enable exploration of these networks are needed as support tools. The alternative approach would be to expressly encode the configuration within the database of science and technology.

The hierarchical random graph approach structured evidence of an existing technological network recognizing the development of new technological linkages shortly after they actually occurred in the market.

A full analysis of this kind may require a longitudinal analysis of network development. Hierarchical models might be built before and after critical time periods,

In this case it might be possible that the structure of the Ajax (Programming) hyperlink network is relatively stable,

it may be difficult to establish a uniform definition of the technological components of the network.

Repeated trials and a more complete analysis based on an evolving network are desirable items for future research.

The author appreciates helpful discussion from Jan Kwakkel on the epistemology of knowledge networks. References 1 M. C. Roco, Key note address:

/22 A. Clauset, C. Moore, M. E. J. Newman, Hierarchical structure and the prediction of missing links in networks, Nature 453 (2008) 98 101.23 M. Sales

, W. A. Turner, S. Bauin, From translations to problematic networks: an introduction to co-word analysis, Soc.

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