The elements that appear in a TDS depiction change from application to application. For instance, Shi, Porter,
Technological forecasting & Social Change 74, no. 4: 413 32. Huang, L.,Z. C. Peng, Y. Guo,
Technological forecasting & Social Change 72, no. 9: 1094 112. O'Regan, B, . and M. Gratzel. 1991.
Technological forecasting & Social Change, doi: 10.1016/j. techfore. 2011.06.004. Robinson, D. K. R, . and T. Propp. 2008.
Technological forecasting & Social Change 75, no. 4: 517 38. Roper, A t.,S w. Cunningham, A l. Porter, T. W. Mason, F. A. Rossini,
Technological forecasting & Social Change 75, no. 4: 457 61. Technology Futures analysis Methods Working group (Alan L. Porter, Brad Ashton, Guenter Clar, Joseph F. Coates, Kerstin Cuhls, Scottw.
Technological forecasting and Social Change 71, no. 1: 287 303. Van Raan, A f. J.,ed. 1988.
Foresight has changed its role according to these changes: it aims to provide an overview of future impacts on our society in broader contexts.
Their main role was to identify key or emerging technologies, looking into the development of science and technology and the expected changes in society.
considering changes on a global or national scale. Based on the discussion, four global or national challenges were set as the goals of science, technology and innovation.
With the discussion above and the dramatic changes occurring inside and outside Japan as a backdrop,
References Cachia, R.,Compano, R. and Da Costa, O. 2007),‘Grasping the potential of online social networks for foresight'',Technological forecasting and Social Change, Vol. 74 No. 8
an efficient,‘round-less',almost real time Delphi method'',Technological forecasting and Social Change, Vol. 73, pp. 321-33.
and how the necessary changes can be captured and measured in a management framework. They also discuss the contributions foresight can make to the management system at different stages of development (cf.
Strategic dialogues need to be flexible enough to cope with this kind of change Organizational learning In such models,
and their action in anticipation of potential research policy changes triggered by the dialogue (such as new funding programs) This can be a problem
Cagnin, C. and Loveridge, D. 2011),‘A business framework for building anticipatory capacity to manage disruptive and transformative change and lead business networks towards sustainable development,
Foresight and‘‘grand challenges''within research and innovation policies Martin Rhisiart Abstract Purpose The paper seeks to discuss how foresight is used to understand the implications of global changes for research and innovation policies.
which combined analysis of global changes with a participatory process involving national stakeholders. The exercise was designed to assess the implications of global changes for research.
A relatively novel aspect which evolved during the course of the exercise, was the focus on translating future-oriented knowledge (from drivers and trends) into grand challenges for the national research and innovation system.
5. increasing pace of change; and 6. energy security. Figure 1 shows the distribution of drivers and trends on an impact versus likelihood matrix.
growth Energy security Renewable energy Renewable energy Peak oil Global trade falters Converging technologies Increasing pace of change Increasing pace of change Open innovation
and innovation communities to consider the impacts of changes in conditions, resources and other factors over different time horizons.
Experiences in Britain, Australia and New zealand'',Technological forecasting and Social Change, Vol. 60 No. 1, pp. 37-54.
a contextualist analysis and discussion'',Technological forecasting and Social Change, Vol. 74 No. 8, pp. 1374-93.
Curran, C. S. and Leker, J. 2011),‘Patent indicators for monitoring convergence examples from NFF and ICT'',Technological forecasting and Social Change, Vol. 78, pp. 256-73.
resurrection and new paradigms'',Technological forecasting and Social Change, Vol. 60 No. 1, pp. 85-94. Hax, A. and Majluf, N. 1996), The Strategy Concept and Process:
implications for technological forecasting'',Technological forecasting and Social Change, Vol. 60 No. 3, pp. 237-45. Markides, C. and Williamson, P. 1994),‘Related diversificaton, core competences and corporate performance'',Strategic management Journal, Vol. 15, pp. 149-57.
a re-evaluation of research and theory'',Technological forecasting and Social Change, Vol. 39, pp. 235-51.
Shih, M. J.,Liu, D. R. and Hsu, M. L. 2010),‘Discovering competitive intelligence by mining changes in patent trends'',Expert Systems with Applications, Vol. 37
Turoff, M. 1970),‘The design of a policy Delphi'',Technological forecasting and Social Change, Vol. 2, pp. 149-71.
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,
and grand societal challenges, in particular, it is important to understand the very nature of change.
adapt and respond pro-actively to change. FTA has a potentially useful role to play in enabling a better understanding of complex situations and in defining effective policy responses
At the same time, FTA can contribute to building‘change'capacities that allow organisations to become capable of anticipating
/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,
For example, simulation models can explore the repercussions of changes in major (external) parameters, as well as the outcome of policy options and other actions.
/Technological forecasting & Social Change 80 (2013) 379 385 are by nature complex and largely impervious to top-down rational planning approaches.
inductive approach, visual inspiration, assessment of coverage of dimensions of change, and prolonged divergence. Finally, Georghiou and Harper 3 set the scene against which change is considered
and show the landscape that has formed the demand and influenced the practice of FTA to show that alignment of approaches, consideration of users'perspectives and divergence,
/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
It is an oftenobseerve fact that technologies change their course because of (unpredictable) changes in the broader socioeconomic context (fluctuations in demand
changes in regulation, changing/stronger ethical concerns, scarcity of natural resources, environmental issues, etc. as well as due to new combinations of existing and/or emerging technologies.
/Technological forecasting & Social Change 80 (2013) 379 385 more experimental approaches to creating new solutions
i) capture of indications for extrasysttemi change at a micro level instead of extrapolating seemingly dominant macro-trends, ii) mobilisation of tacit knowledge as well as support a creative spirit and an easy exchange of ideas among diverse stakeholders through
iii) rigorous assessment of coverage of dimensions of change to take into account possibly unrecognised/hidden structural changes,
which change is considered in the domain of innovation policy and in investigator-driven research they show the landscape that has formed the demand
/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
& 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
and that emerging changes in the socioeconomic and technological landscapes need to be taken into account.
/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:
linking ecosystem change and human well-being by combining qualitative storyline development and quantitative modelling through several iterations between both parts 44.
/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
Change 76 (2009) 1037 1050.29 N. Agami, M. Saleh, H. El-Shishiny, A fuzzy logic based trend impact analysis method, Technol.
Change 77 (2010) 1051 1060.30 E. Kemp-Benedict, Converting qualitative assessments to quantitative assumptions: Bayes'rule and the pundits wager, Technol.
Change 77 (2010) 167 171.31 B. P. Bryant, R. J. Lempert, Thinking inside the box:
Change 77 (2010) 34 49.395 K. Haegeman et al.//Technological forecasting & Social Change 80 (2013) 386 397 32 D. Rossetti di Valdalbero, The Power of Science economic research and European decision-making:
the case of energy and environment policies, Peter Lang, 2010. ISBN 978-90-5201-586-6 pb. 33 N. Shibata, Y. Kajikawa, Y. Takeda,
Change 78 (2011) 274 282.34 H. M. Järvanpää, S. J. Mäkinen, M. Seppänen, Patent and publishing activity sequence over a technology's life cycle, Technol.
Change 77 (2010) 1037 1050.36 R. Popper, Foresight methodology, in: L. Georghiou, J. C. Harper, M. Keenan,
Change 78 (2011) 256 273.38 P. Lee, H. Su, F. Wu, Quantitative mapping of patented technology the case of electrical conducting polymer composite, Technol.
Change 77 (2010) 466 478.39 K. Haegeman, C. Cagnin, T. Könnölä, D. Collins, Web 2. 0 foresight for innovation policy:
/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.
& Social Change Another important predecessor approach upon which we draw is the identification of Technology Readiness Levels (TRLS).
These papers studied the indicators that would have different performance based on the changes of technology.
/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.
Numbers of inventors suggest very interesting changes in different stages. Fig. 3, which presents the emerging and growth stages, shows that the number of inventors is typically higher than that of all other indicators.
Since the indicators show different trends in different stages, it might be better to combine all 13 indicators to measure the change of technology rather than using one single indicator.
/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
Change 56 (1997) 25 47.15 R. Haupt, M. Kloyer, M. Lange, Patent indicators for the technology life cycle development, Res.
/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;
Technological forecasting & Social Change 72 (2005) 1137 1152 promising approaches under way. The fourth part with conclusions closes the article,
mainly focusing some of the lessons learned from evolutionary theory involved in anticipating changes in evolutionary trajectories,
whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.
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.
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 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,
and their dynamics (behavior over time) is defined via the change of their organization (or dstatet) as described by the system's differential equations.
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1148 Although a consistent ETTC still not exists
David Goldberg 37, for instance, for studying the connection between the two basic processes of innovation, continual improvement and discontinuous change.
& 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.
& 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,
Change 71 (2004) 287 303.2 H. A. Linstone, TF and SC: 1969 1999, Technol. Forecast.
Change 62 (1999) 1 8. 3 Bowonder, et al. Predicting the future: lessons from evolutionary theory, Technol.
Change 62 (1999) 51 62.4 T. L. Brown, Making Truth: The Roles of Metaphors in Science, University of Illinois Press, 2003.5 P. Corning, Nature's Magic:
Change 18 (1980) 257 282.11 T. Modis, Predictions: Society's Telltale Signature Reveals the Past and Forecasts the Future, Simon and Scuster, New york, 1992.12 T. Devezas, J. Corredine, The biological determinants of long-wave behavior in socioeconomic
Change 68 (2001) 1 57.13 H. De vries, Species and Varieties, Their Varieties by Mutations, Kegan Paul, Trench Trubner,
An Evolutionary theory of Economic Change, Beknap of Harvard university Press, Boston, 1982.17 G. Baslalla, The Evolution of Technology, Cambridge university Press, 1988.18 H. Sachsse, Anthropologie der Technik
Change 70 (2003) 819 859.26 S. Kauffman, At home in the Universe, Oxford university Press, New york, 1995.27 D. Strumsky, L. Lobo,
Change 68 (2001) 293 308. T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1151 34 J. Goldenberg, B. Libai, Y. Louzoun, D. Mazursky
, S. Solomon, Inevitably reborn: the reawakening of extinct innovations, Technol. Forecast. Soc. Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.
Dyn. Control 29 (2005) 225 244.36 Z. Michalewicz, D. B. Vogel, How to Solve it:
Change 64 (2000) 7 12.38 J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural selection, MIT Press, Boston, Ma, 1992.39 J. Koza, et al.
Modeling, Simulating and Forecasting Social Change, a Proposal of a Seminar to the Calouste Gulbenkian Foundation,
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.
& Social Change boundaries of what might occur in the future. Although useful, these traditional methods are not free of problems.
/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:
Change 77 (2010) 917 923.8 R. J. Lempert, S. Popper, S. Bankes, Shaping The next One hundred Years:
Change 77 (2010) 355 368.13 S. Popper, J. Griffin, C. Berrebi, T. Light, E. Y. Min, Natural gas and Israel's energy future:
Change 75 (2008) 462 482.16 J. H. Kwakkel, W. E. Walker, V. A w. J. Marchau, Classifying
Change 71 (2004) 287 303.18 A. Volkery, T. Ribeiro, Scenario planning in public policy: understanding use, impacts and the role of institutional context factors, Technol.
Change 76 (2009) 1198 1207.19 C. Cagnin, M. Keenan, Positioning future-oriented technology analysis, in: C. Cagnin, M. Keenan, R. Johnston, F. Scapolo, R. Barré (Eds.
Change 77 (2010) 924 939.24 W. E. Walker, S. A. Rahman, J. Cave, Adaptive policies, policy analysis,
Change 76 (2009) 462 470.29 L. K. Mytelka, K. Smith, Policy learning and innovation theory: an interactive and co-evolving process, Res.
Change 79 (2012) 311 325.36 D. B. Agusdinata Exploratory modeling and analysis: a promising method to deal with deep uncertainty, in:
Change (in press), http://dx. doi. org/10.1016/j. techfore. 2012.09.012.43 B. P. Bryant, R. J. Lempert, Thinking inside the box:
Change 77 (2010) 34 49.44 R. Lempert, M. Collins, Managing the risk of uncertain threshold response:
Change 76 (2009) 1150 1162.46 P. Martens, J. Rotmans, Transitions in a globalising world, Futures 37 (2005) 1133 1144.47 D. Loorbach, N
Change 77 (2010) 1195 1202.48 W. J. Abernathy, K. B. Clark, Innovation: mapping the winds of creative destruction, Res.
/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.
& Social Change mechanisms underlying a phenomenon are known perfectly, one could predict the future development of this phenomenon.
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,
Recent contextual developments constitute a backdrop of change for the Dutch electricity system. Institutional change driven by liberalization, changing economic competitiveness of the dominant fuels, new technologies,
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.
Changes in ownership structure, initiatives like Single European Sky and the Open Skies treaty between the United states and Europe, the introduction of new aircraft such as the Airbus A380 and the Boeing 787,
. 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
minimizing the time required to realize the change. To address the potential overshoot of negative external affects
Name Description Range Demand Change in demand, the curves can be parameterized in various ways Exponential growth, logistic growth,
or logistic growth followed by logistic decline Wide body vs. narrow body aircraft mix Change in aircraft mix,
the curves can be parameterized in various ways Linear or logistic change Population Change in population density,
or logistic growth to a maximum followed by logistic decline ATM technology Change in air traffic management technology,
the curves can be parameterized in various ways Exponential or logistic performance increase Engine technology (noise/emissions) Change in air traffic management technology,
or logistic performance increase Weather Percentage of change in days with severe wind conditions per year.
-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,
or by modifying the stricter slot allocation regime. 3. 3. Identification of plausible transition pathways for the future Dutch electricity generation system Recent contextual developments constitute a backdrop of change
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 can be decommissioned. Generation companies'expansion decisions are driven mainly by profit expectations,
and gas price increase percentage Yearly fractional increase in coal prices 0. 002 0. 03 Demand growth fraction Yearly fractional change in the demand of end users 0 0. 03 Load
slope change fraction Yearly fractional change in the slope of the load duration curve-0. 01 0. 01 Planning horizon of the generation companies Upper bound for the planning horizon of the generation companies.
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.
& Social Change Developing and using future scenarios can: -contribute to society's strategic intelligence by stimulating future-oriented thinking
For instance, developments in science and technology have a strong potential to influence social change. There are, however, many reasons why the practical use of scientific knowledge
/Technological forecasting & Social Change 80 (2013) 432 443 2. Material and methods How can we learn about orienting innovation systems from future scenario practice?
What methodological issues are salient in relation to the identification of emerging trends and change? How commensurable are different modes of modeling and other forms of dynamical representation?
/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.
The Prelude scenarios2 are a good example (see Appendix 1). An important input for the scenario work in this group are the comprehensive descriptions of the external drivers for change highlighting the uncertainty of future developments.
We found that adapting for change is often the general theme in the lessons learnt.
adapting for change is seen logically as the dominant response. In that sense the potential for innovation within the system (i e. inward reflection) is acknowledged less.
changes in the external environment are part of the scenarios. But in contrast with the first group, change is described less by framing very different long-term future worlds.
Breaking up the long-term in more tangible time periods helps understand the necessary steps for embracing change.
/Technological forecasting & Social Change 80 (2013) 432 443 The images of the future are focused on key internal developments
(i e. inward reflection) and often driven by technology or changes in our way of living.
The future plays the role of the time needed to introduce the necessary changes to comply with the envisaged principles.
The concept of change is an implicit part of the scenarios developed in backcasting from principles
and innovation to meet necessary change and uncertainties in the agri-food sector facing resource constrains and environmental limits.
they also involve the interaction of the stakeholders, their ideas, values and capacities for social change.
investigating and utilizing potential future societal changes and developments, see also 62,77. To synthesize this section on results and implications,
/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
especially by decision-makers Conventional Convention Agree on common accepted probabilities of change (rejecting extreme ideas) Strong on acceptance and alignment,
and external change Visionary Preference Envision how society can be designed in a better (e g. more sustainable) way Allows creating authentic alternative visions to guide innovation Weak on clear targets,
surprise and external change Technocratic Expertise and discovery Demonstrate technical feasibility and optimize technological development Allows minimizing inconsistencies
/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.
investigating and utilizing potential future societal changes and developments. This integrated approach, i e. integrating different modes of futures thinking, is needed for orienting innovation along more sustainable pathways enabling transformations of socio-technical systems.
/Technological forecasting & Social Change 80 (2013) 432 443 References 1 C. Harries, Correspondence to what?
Futures 43 (2011) 130 133.47 S. A. van't Klooster, M. B. A. van Asselt, Accommodating or compromising change?
/Technological forecasting & Social Change 80 (2013) 432 443 58 J. P. Gavigan, F. Scapolo, A comparison of national foresight exercises, Foresight 1 (1999) 495
How to Think Clearly in a Time of Change, Pearson, Prentice hall, New york, 2006.69 U. Beck, Ecological Politics in an Age of Risk, Polity Press, Cambridge, 1995.70 L. M. Ricard, K
Futures 38 (2006) 350 366.77 P. De Smedt, Can Negotiating the Future Influence Policy and Social change?
/Technological forecasting & Social Change 80 (2013) 432 443
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011