Technological forecasting & Social Change 72 (2005) 1112 1121 other hand, decision support and policy making require information on the potential consequences of the introduction of new technologies before they are implemented widely,
/Technological forecasting & Social Change 72 (2005) 1112 1121 1113 Most funding organisations or contract awarders require valid, scientifically sound, knowledge-based, often quantitative,
/Technological forecasting & Social Change 72 (2005) 1112 1121 1114 ordinated by the Europa ische Akademie zur Erforschung von Folgen wissenschaftlich-technischer Entwicklungen Bad
/Technological forecasting & Social Change 72 (2005) 1112 1121 1115 considered as the most important bridge between basic research and marketable products and processes.
/Technological forecasting & Social Change 72 (2005) 1112 1121 1116 The term droadmapt is used widely, starting from graphical representations of technology development paths and their application environments up to detailed and ambitious
/Technological forecasting & Social Change 72 (2005) 1112 1121 1117 be as specific and reliable as necessary to be the basis for a valid and sound technology assessment
/Technological forecasting & Social Change 72 (2005) 1112 1121 1118 Besides this, a successful implementation of this concept could also help to overcome some of the argumentative asymmetries that can be found in many debates about chances
/Technological forecasting & Social Change 72 (2005) 1112 1121 1119 purposes, and what further benefits of the roadmapping process beyond structuring the field of nanotechnology can be expected. 5. Summary
/Technological forecasting & Social Change 72 (2005) 1112 1121 1120 2 T. Fleischer, A. Grunwald, Technikgestaltung fu r mehr Nachhaltigkeit Anforderungen an die Technikfolgenabscha
/Technological forecasting & Social Change 72 (2005) 1112 1121 1121
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Technological forecasting & Social Change 72 (2005) 1122 1136 at Sandia has its roots in the LDRD program.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1123 IA leads were designed to not only benchmark the visualizations,
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1124 3. 1. Data collection Two different sets of data were compiled from multiple sources
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1125 document matrix.
During our meeting with the CIS area leader, we first K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1126 gathered information
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1127 greatest overlaps with CIS,
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1128 Another significant outcome of the meeting with the IA leader was his desire
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1129 extract the hidden relationships within the landscape visualization
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1130 The first level of analysis identified a macroscale understanding of the overlaps as well as the unique competencies and capabilities that each IA possessed.
N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1131 areas of interest to Sandia since the map indicates that they are well outside our core competency areas.
The area inside the dashed box is explored further in Fig. 7. K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1132 competencies,
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1133 rolling up all of the IAS to an overall Sandia category.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1134 relationships.
K. W. Boyack, N. Rahal/Technological forecasting & Social Change 72 (2005) 1122 1136 1135 References 1 K. Bfrner, C. Chen, K
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Technological forecasting & Social Change 80 (2013) 379 385 Corresponding author at: Center for Strategic Studies andmanagement (CGEE), SCNQD 2, Bl.
Sciencedirect Technological forecasting & Social Change In this context, when analysing the potential of future-oriented technology analysis (FTA) to assist societies, decision-makers and businesses to tackle fundamental, disruptive transformations, in general,
/Technological forecasting & Social Change 80 (2013) 379 385 3. Combining quantitative and qualitative approaches FTA is an umbrella term to denote several decision-preparatory tools (technology foresight,
/Technological forecasting & Social Change 80 (2013) 379 385 are by nature complex and largely impervious to top-down rational planning approaches.
/Technological forecasting & Social Change 80 (2013) 379 385 In more detail, Haegeman et al. 4 depart from the methodological debate that has been a relevant element of the International Seville Conference series on Future-oriented technology analysis (FTA
/Technological forecasting & Social Change 80 (2013) 379 385 more experimental approaches to creating new solutions
/Technological forecasting & Social Change 80 (2013) 379 385 practice and assist in considering transformations that are going to take us closer to anticipating disruptive innovations and events.
/Technological forecasting & Social Change 80 (2013) 379 385
Quantitative and qualitative approaches in Future-oriented technology analysis (FTA: From combination to integration? Karel Haegeman a,, Elisabetta Marinelli b, Fabiana Scapolo c, Andrea Ricci d, Alexander Sokolov e a European commission, JRC-IPTS, Edificio Expo WTC, C/Inca
and sounds) and a methodology as qualitative when not relying on statistical/Technological forecasting & Social Change 80 (2013) 386 397 The views expressed are purely those of the authors
Sciencedirect Technological forecasting & Social Change mathematical tools. A participatory method, regardless of the qualitative or quantitative data it uses, is one in
/Technological forecasting & Social Change 80 (2013) 386 397 qualitative) as an imaginative projection of current knowledge in which formal methods and techniques play a subsidiary role (p. 753.
/Technological forecasting & Social Change 80 (2013) 386 397 identification of emerging clusters analysing citations and keywords for a particular technology field,
/Technological forecasting & Social Change 80 (2013) 386 397 Other tools and disciplines that can serve as interface to facilitate the use of qualitative and quantitative approaches and data Social network analysis:
/Technological forecasting & Social Change 80 (2013) 386 397 are brought not always together in the analysis 62 and qualitative and quantitative tasks are carried out by different teams,
/Technological forecasting & Social Change 80 (2013) 386 397 Finally, in this debate, there is a tendency to equate qualitative with participatory.
/Technological forecasting & Social Change 80 (2013) 386 397 sciences, Cameron 71 developed the Five Ps Framework, 13 which provides a mixed-methods starter kit,
/Technological forecasting & Social Change 80 (2013) 386 397 reasonable representation of the systems being analysed, and that the intrinsic uncertainties associated with such representation are documented at best. 5. 2. 2. Lack of trust One aspect of trust is that it derives from perceived credibility,
/Technological forecasting & Social Change 80 (2013) 386 397 identification of the features that may help the organisers of FTA projects in the selection of the most appropriate set of tools (characterising
/Technological forecasting & Social Change 80 (2013) 386 397 32 D. Rossetti di Valdalbero, The Power of Science economic research and European decision-making:
/Technological forecasting & Social Change 80 (2013) 386 397 Fabiana Scapolo holds a Phd on foresight methodologies and practices from the Manchester University (UK).
/Technological forecasting & Social Change 80 (2013) 386 397
Technology life cycle analysis method based on patent documents Lidan Gao a b,, Alan L. Porter c, Jing Wang d, Shu Fang a, Xian Zhang a, Tingting Ma e, Wenping Wang e, Lu Huang e
Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at: Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041, PR China.
0040-1625/$ see front matter 2012 Elsevier Inc. All rights reserved. http://dx. doi. org/10.1016/j. techfore. 2012.10.003 Contents lists available at Sciverse Sciencedirect Technological forecasting
/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,
/Technological forecasting & Social Change 80 (2013) 398 407 in DII by application year for the Application indicator and count the number of patents in DII by priority year for the Priority indicator
/Technological forecasting & Social Change 80 (2013) 398 407 2. 2. TLC stages of CRT and TFT-LCD It is better to choose a training technology with four TLC stages.
/Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.
/Technological forecasting & Social Change 80 (2013) 398 407 A1 i; j ð Þ A1 i;
/Technological forecasting & Social Change 80 (2013) 398 407 For each test point bk, we compute the distance between bk
/Technological forecasting & Social Change 80 (2013) 398 407 definitive projections. Indeed, explicit analyses of what factors and forces are apt to alter projected developmental trends are worthwhile note Ted Gordon's Trend Impact analysis (TIA) especially 34.
/Technological forecasting & Social Change 80 (2013) 398 407 2 H. X. G. Ming, W. F. Lu, C. F. Zhu, Technology challenges
Technol. 43 (1)( 2008) 157 162.4 J. P. Martino, Technological forecasting for Decision making, 3rd Edition Mcgraw-hill, New york, NY, 1993.5 A t. Roper, S w
European Management Forum, Davos, 1981.10 H. Ernst, The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry, Small Bus. Econ. 9 (4)( 1997) 361 381.11 T. H. Lee, N. Nakicenovic, Life cycle of technology
/Technological forecasting & Social Change 80 (2013) 398 407
Evolutionary theory of technological change: State-of-the-art and new approaches Tessaleno C. Devezas Technological forecasting and Innovation theory Working group, University of Beira Interior, Covilha, Portugal Received 13 may 2004;
accepted 6 october 2004 Abstract It is well known the fact that the world of technology is full of biological metaphors,
One of the most powerful technological forecasting tools, the logistic equation, has its origin in the biological realm
Technological forecasting & Social Change 72 (2005) 1137 1152 promising approaches under way. The fourth part with conclusions closes the article,
which would bring a renewed thrust toward new methods in technological forecasting (Fig. 1). The picture suggests that the chaotic phase transition might be behind us
whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.
and Fig. 1. Technological forecasting in perspective presented by Linstone in the 30-year anniversary issue of TF and SC (1999).
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1139 quality control. Peter Corning 5 has pointed out that complexity in nature
the amount of practical work using simulation methods is still a dwarf T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1140 one.
more than an useful metaphor One of the most powerful technological forecasting tools, the logistic equation, has its origin in the biological realm
The mathematical tools that began to be employed in economics (as well as in technological forecasting) starting in the 1970s had been developed by mathematical biologists in the 1920s
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1141 Yet in 1925 the American biologist and demographer Raymond Pearl 8 in his seminal book
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1142 3. 2. To point 2:
if we substitute the words dgenetic underpinningst by building blocks T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1143 (following John Holland's 14 original
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1144 Such a bridge could be offered by a better-developed danthropology of technique,
Social scientists, and particularly economists, have T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1145 never correctly realized that Darwin in his second
beginning with Donald Campbell 23 in the T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1146 1960s (who coined the term Evolutionary Epistemology to characterize Popper
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,
The systems dynamics approach, widely used in technological forecasting since the 1950s, is btop-downq in character
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1148 Although a consistent ETTC still not exists
which method is suited best for purposes of simulating technological evolution and/or for developing useful tools for technological forecasting.
cellular automata is the poorest for more T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1149 sophisticated simulations due to the simplicity of its basic assumptions and limitations that must be imposed in the rules governing interactions between agents.
This scientific meeting could be planned following the format of A t. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1150 recent proposal of this author with George Modelski for a seminar on Globalization as Evolutionary Process 40 to be held in the spring of 2005 in Paris,
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1151 34 J. Goldenberg, B. Libai, Y. Louzoun, D. Mazursky
and Head of the Technological forecasting and Innovation theory Working group (TFIT-WG). T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1152
Adaptive Robust Design under deep uncertainty Caner Hamarat, Jan H. Kwakkel, Erik Pruyt Delft University of Technology policy Analysis Department, PO BOX 5015,2600 GA Delft
Characteristic for these techniques is that they aim at charting the Technological forecasting & Social Change 80 (2013) 408 418 Corresponding author.
0040-1625/$ see front matter 2012 Elsevier Inc. All rights reserved. http://dx. doi. org/10.1016/j. techfore. 2012.10.004 Contents lists available at Sciverse Sciencedirect Technological forecasting
/Technological forecasting & Social Change 80 (2013) 408 418 Fig. 1 shows a framework that operationalizes the high level outline of adaptive policy-making.
In a recent special issue of Technological forecasting and Social Change on adaptivity in decision-making, the guest editors conclude that Adaptive policy-making is a way of dealing with deep uncertainty that falls between too much precaution and acting too late.
/Technological forecasting & Social Change 80 (2013) 408 418 operationalizing the Adaptive Policy-making Framework is structured through workshops 35.
/Technological forecasting & Social Change 80 (2013) 408 418 explicitly considers the opportunities that uncertainties can present.
/Technological forecasting & Social Change 80 (2013) 408 418 In order to explore the problem and the uncertainties of energy transitions,
/Technological forecasting & Social Change 80 (2013) 408 418 fraction of new technologies for the no policy ensemble (in blue) and the basic policy ensemble (in green) as well as the KDES of the end states of all
/Technological forecasting & Social Change 80 (2013) 408 418 this signpost. Using this trigger, the corrective action would be to stop investing in Technology 2
/Technological forecasting & Social Change 80 (2013) 408 418 4. Discussion and implications for Future-oriented technology analysis (FTA) In this paper we proposed an iterative computational approach for designing adaptive policies that are robust
/Technological forecasting & Social Change 80 (2013) 408 418 3 E. Pruyt, J. H. Kwakkel, G. Yucel, C. Hamarat, Energy transitions towards sustainability:
/Technological forecasting & Social Change 80 (2013) 408 418 Caner Hamarat is a Phd researcher at the Faculty of technology, Policy and Management of Delft University of Technology.
/Technological forecasting & Social Change 80 (2013) 408 418
Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty Jan H. Kwakkel, Erik Pruyt Faculty of technology, Policy,
Similarly, if the Technological forecasting & Social Change 80 (2013) 419 431 Corresponding author. Tel.:++31 15 27 88487.
0040-1625/$ see front matter 2012 Elsevier Inc. All rights reserved. http://dx. doi. org/10.1016/j. techfore. 2012.10.005 Contents lists available at Sciverse Sciencedirect Technological forecasting
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 that policy or planning debates can often be served even by the discovery of thresholds, boundaries,
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 This small and simplistic System Dynamics model was developed in about one day in close collaboration with a mineral/metal expert
Start, end, slope Fig. 1. Causal loop diagram of the scarcity model 18.422 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 otherwise. Next, we tried to identify subspaces in the overall uncertainty space that show a high concentration of crises runs using the Patient Rule Induction Method 31 33.
3. Evolution of market price for a 1000 runs. 424 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 3. 2
-1%+4%425 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 could serve as a starting point for slightly modifying the outlined dynamic adaptive plan,
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 can be decommissioned. Generation companies'expansion decisions are driven mainly by profit expectations,
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 irreducible uncertainties inherent in the forces driving toward an unknown future beyond the short term
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 some structural uncertainties were taken into account.
Future research avenues include elaborating on the use of EMA for designing dynamic adaptive policies and the use of EMA for 429 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80
dynamic scenario discovery under deep uncertainty, Technological forecasting and Social Change,(under review. 23 R. U. Ayres, On the practical limits to substitution, Ecol.
)( 2008) 201 214.430 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 Jan Kwakkel is a postdoctoral researcher at Delft
from short-term crises to long-term transitions. 431 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431
and using scenarios and orienting innovation systems and research priorities 6. Technological forecasting & Social Change 80 (2013) 432 443 Corresponding author.
0040-1625/$ see front matter 2012 Elsevier Inc. All rights reserved. http://dx. doi. org/10.1016/j. techfore. 2012.10.006 Contents lists available at Sciverse Sciencedirect Technological forecasting
/Technological forecasting & Social Change 80 (2013) 432 443 2. Material and methods How can we learn about orienting innovation systems from future scenario practice?
/Technological forecasting & Social Change 80 (2013) 432 443 experiments in the policy process, new concepts and sustainable solutions can be found to grand challenges.
/Technological forecasting & Social Change 80 (2013) 432 443 The concept of the multiple-axes method is based on one of the approaches used by Pierre Wack 52.
/Technological forecasting & Social Change 80 (2013) 432 443 our analysis a better understanding of the linkages between scenario design, methods used and related outcomes.
/Technological forecasting & Social Change 80 (2013) 432 443 The images of the future are focused on key internal developments
/Technological forecasting & Social Change 80 (2013) 432 443 5. Discussion Due to the social dynamic characteristic of innovation, new socio-technical subsystems are emerging 24.
/Technological forecasting & Social Change 80 (2013) 432 443 The solutions developed should be socially reflexive
/Technological forecasting & Social Change 80 (2013) 432 443 acknowledge the limits of our analysis: i e. using a policy perspective for doing an ex-post analysis of future scenario practice.
/Technological forecasting & Social Change 80 (2013) 432 443 References 1 C. Harries, Correspondence to what?
/Technological forecasting & Social Change 80 (2013) 432 443 58 J. P. Gavigan, F. Scapolo, A comparison of national foresight exercises, Foresight 1 (1999) 495
/Technological forecasting & Social Change 80 (2013) 432 443
The role of future-oriented technology analysis in the governance of emerging technologies: The example of nanotechnology Petra Schaper-Rinkel AIT Austrian Institute of technology, Donau-City-Straße 1, A-1220 Vienna, Austria a r t i c l e
and then recognizing that the Technological forecasting & Social Change 80 (2013) 444 452 E-mail address:
. 2012.10.007 Contents lists available at Sciverse Sciencedirect Technological forecasting & Social Change emergence of nanotechnology is adjudicated not just in labs,
whereas in the field of converging technologies broad futuristic discourses took place that were followed not by funding strategies dedicated explicitly to converging technologies. 445 P. Schaper-Rinkel/Technological forecasting
446 P. Schaper-Rinkel/Technological forecasting & Social Change 80 (2013) 444 452 support nanotechnology education, research and development the fastest will thrive in the new millennium 1. These statements illustrate that the report
The funding is provided through the NNI member agencies. 6 The Center for Nanotechnology in Society at Arizona State (CNS-ASU) is funded by the NSF. 447 P. Schaper-Rinkel/Technological forecasting
448 P. Schaper-Rinkel/Technological forecasting & Social Change 80 (2013) 444 452 Nanolux (optics industry, nanotechnology for energy efficient lighting.
449 P. Schaper-Rinkel/Technological forecasting & Social Change 80 (2013) 444 452 In this later stage, heterogeneous stakeholders beyond the actors of the early established nano-policy networks
In the late 1980s and early 1990s, several industrial countries established their first programs in 450 P. Schaper-Rinkel/Technological forecasting & Social Change 80 (2013) 444 452
Making it in Miniature Nanotechnology Report Summery, POST, Parliamentary Office of Science and Technology policy, London, 1996, p. 4. 451 P. Schaper-Rinkel/Technological forecasting & Social Change
and methods and practices of futuring. 452 P. Schaper-Rinkel/Technological forecasting & Social Change 80 (2013) 444 452
Technological forecasting & Social Change 80 (2013) 453 466 Corresponding author. E-mail address: Elna. Schirrmeister@isi. fraunhofer. de (E. Schirrmeister.
0040-1625/$ see front matter 2012 Published by Elsevier Inc. http://dx. doi. org/10.1016/j. techfore. 2012.10.008 Contents lists available at Sciverse Sciencedirect Technological forecasting
Both these 1 www. innovation-futures. org. 454 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 approaches can be termed inductive
but also in the weak signal collection available on the internet. 2 The movie is available at www. innovation-futures. org. 455 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change
web-extracted innovation. 456 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 Fig. 3. Screenshot from the INFU web-based
survey. 457 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 Fig. 4. Visualisation of all INFU visions. 458 E. Schirrmeister,
P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 2. 2. Visual inspiration The INFU amplifications were illustrated in a visual,
P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 without any rootwithin phenomena that can be observed today 25,26.
P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 the scenario building activity is looking for a consensus building process among the participants
Dimensionof change Modified specificationextreme A Modification specificationextreme B Today's dominantspecification Fig. 7. Assessment of coverage of dimensions of change. 461 E. Schirrmeister, P. Warnke/Technological forecasting
and use phases. 462 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 (2) Participation:
Fig. 9. Element from INFU mini panel Participatory Innovation. 463 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466
P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 dominance of the macro-level and the influence of today's perception of consistency were reduced to give room for creative assessment of structural transformation.
465 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 References 1 O. Da Costa, P. Warnke, C
Her research focuses on Foresight methodology and the mutual shaping of technology and society. 466 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466
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