For instance, a portion of the EP portfolio dealing with microelectromechanical systems (MEMS) technology has shifted from component integration to applications.
For example, laboratory B has an area of common technical focus with laboratory A through lithography, laboratory C through fuel cells and biological systems,
and laboratory D through biological systems and semiconductors. The identification of these common points directs us to btechnology categoriesq that can be analyzed further to identify the portfolio of technology that characterizes the capabilities of each laboratory.
in the current interlinked innovation meta-system, research and technollog organisations (RTOS) would benefit from developing two systemic capacities:
For example, Smits and Kuhlmann (2004,11) argue that innovation is a systemic activity thatinvolves a variety of actions within the system,
'In addition, Geels (2004,900) uses the termsocio-technical system'to describe the systemic interaction that encompasses production, diffusion,
In this article, we open a view towards the systemic capacities, based on a perspective of an organisation as a complex system that is mobile in space time.
The idea springs from the perspective that organisations are complex systems where transformations arise through emergence,
roadmapping comes quite close to system dynamic modelling techniques, yet roadmapping is still more of a technique for strategic focussing Downloaded by University of Bucharest at 05:05 03 december 2014 826 T. Ahlqvist et al.
than for system simulation. However, combining roadmapping with system dynamic modelling is definitely a potential path for future methodological development.
Process-based strategy roadmapping is methodologically more flexible and exploratory than traditiiona technology roadmapping. The roadmaps are approached not as hermetic plans to achieve definite goals (e g. new products),
and different kinds of digital systems to help optimisation and customer selection. The third aspect was need the to move away from the intense price competition towards integrated service packages that would be oriented based on quality pricing schemes.
Creation of four context scenarios on the adoption of ICT in four Nordic countries Building system-level strategic
and organisations when responding to system-level changes. First activation of the systemic transformation capacities is useful
From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory.
Classical technology forecasting methods were devised to address incrementally advancing technological systems. These methods keyed on technical system parameters, somewhat more than on socioeconnomi system aspects.
That is because they were driven initially by cold war considerations that concentrated on functional gains more than on cost
the technology delivery system(TDS')has demonstrated enduring value by capturing and representing (1) key enterprise (todeliver'an innovation) and (2) contextual factors (impinging on such delivery).
Wenk and Kuehn (1977) advance TDS as a form of socio-technical system conceptual modelling to help identify the pivotal elements involved in innovation.
and available human knowledge of the particular innovation Downloaded by University of Bucharest at 05:05 03 december 2014 846 Y. Guo et al. system, within the context of a more general innovation context (i e. the socioeconomic context in
Stage 3 brings expertise to bear on the system depiction (Stage 1) and empirical results from Stage 2. Step F digests prior results to present those to participating experts
Key reviewarticles helped us to understand the important componeent and players in thisdelivery system'.'Engagement of our collaborating solar cell researchers helped distinguish the more important elements.
GA. He is also Professor Emeritus of Industrial & Systems Engineering, and of Public policy, at Georgia Tech, where he continues as the co-director of the Technology policy and Assessment Center.
UKINNOVATION systems for newand renewable energy technologies: Drivers, barriers and systems failures. Energy Policy 33, no. 16: 2123 38.
Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 859 Guo, Y.,L. Huang,
Interinstitutional networks in technological delivery systems. In Science and technology policy, ed. J. Haberer, 153 75.
*)or (systemic sclerosis) or (diffuse scleroderma) or (Deep space Station Controller) or (Data Storage Systems Center) or (decompressive stress strain curve or (double-sidebandsupprresse carrier) or (Flexible AC Transmission Systems
science and technology to be embedded in society as a socialized system. With the discussion above and the dramatic changes occurring inside and outside Japan as a backdrop,
The area addresses the issue of constructing a new information society system where ICT underpins the basic infrastructure of society,
storage system 06-L Energy saving 07-B Agriculture, forestry, and fisheries resources 07-C Water resources 07-D Environment, recyclable resources, recycling, LCA 07-E Hydrocarbon resources, mineral resources,
the challenge of developing a more resilient societal system was identified, with potential implications around social research on resilience,
middle classes Uncertain results for banking regulation A challenge to liberal democracy models Conflict follows geopolitical shifts Terrorism continues to pose a threat to security A multi-polar governance system Religion
where some appeared to be narrower and more specific for the national RTDI system. This suggests that not all RTDI priorities are linked specifically to grand challenges.
B Living Systems; B Medicine and Health; B Rational Use of Natural resources; B Transportation, Aviation and Space Systems;
B Power Engineering and Energy Saving; B Manufacturing Systems; and B Safety. The thematic areaRational Use of Natural resources''covers the following five technology areas:
1. environmental monitoring and forecasting (atmosphere and hydrosphere; 2. estimating resources and forecasting (lithosphere and biosphere;
Techniques for assessing anthropogenic systems hazardous to environment''.''The methodology of this study included various expert and analytical techniques being engaged to prepare this S&t foresight (analytical research, bilbliometric and patent analysis, interviews with and polling of experts,
B Living Systems; B Industry of Nanosystems; B Transportation and Aerospace Systems; B Rational Use of Nature Resources;
and B Energy efficiency and Energy Saving.Rational Use of Nature Resources''was considered therefore one of the key priorities.
B combined solid minerals extraction and deep processing systems (2010-2015; Table II Characteristics of most developed technology groups in theRational Use of Natural resources''thematic area Technology group Index of R&d levela Cumulative effectb Monitoring and control systems,
and B an atmospheric pollution monitoring system, capable of the early detection of conditions potentially leading to natural or anthropogenic environmental emergencies (2015-2020.
B improvements in the law enforcement system and law enforcement practice; B designing effective economic mechanisms for stimulating enterprises to decrease waste formation;
The IPC, established by the Strasbourg Agreement of 1971, provides for a hierarchical system of language-independent symbols for the classification of patents and utility models according to the different areas of technology to
NO 1 2013 jforesight jpage 57 intellectual property (IP) system that rewards creativity, stimulates innovation
advanced and reliable distribution network and system technology for electricity; distributed power generation systems; next-generation SCADA technology;
heat, electricity, cooling cogeneration/building use 15 (Biotechnology) 1 (Electrical machinery, apparatus, energy) Japan Artificial photosynthesis technology/solar energy conversion efficiency;
micro cogeneration systems/residential use; ceramic micro gas turbines/thermal efficiency; ocean-thermal conversion/electric power generation South korea Cogeneration fuel cell/residential use;
''Development of distributed power technology with large-scale use of alternative energy supply'',Development of low-cost and high-purity hydrogen mass production Technology'development of zero emission power generation system
The content of these topics comprisesCirculating fluidized bed flue gas desulfurization'',Coal gasification-based poly-generation technology'',Energy consumption analysis for construction and building environmental systems and energy saving optimization technology,
Also, the IPC code has some limitations in mapping the content of Delphi topics in describing a system innovation in which technology
Schmoch, U. 2008),Concept of a technology classification for country comparisons'',Final Report to the World Intellectual Property Organization (WIPO), Fraunhofer Institute for Systems and Innovation research, Karlsruhe
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
systems in which FTA is conducted; and policy governance sub-systems in which FTA is embedded (or on the contrary, with
which certain FTA APPROACHES would clash). By putting together these major building blocks, one can better devise
(i) the perceived policy needs/opportunities to be tackled by FTA,(ii) the chosen FTA APPROACH and its methods and (iii) the policy governance sub-system,
since societal challenges and complex interrelated systems require a more holistic and systemic understanding of situations.
and Analysis (EMA) is a methodology for analysing dynamic and complex systems and supporting long-term decision-making under uncertainty through computational experiments.
Hamarat et al. 11 explore the application of EMA combined with a number of tools in a case that focuses on a large systemic transformation or transition of an energy generation system towards a more sustainable functioning.
in three different technical domains and related to three different grand challenges, grounded in a system perspective.
in order to better understand the systemic and structural transformations of complex systems, ii) inclusion of a multiplicity of perspectives, worldviews, mental models or quantitative models,
The underlying claim is that innovation itself needs to be oriented along more sustainable pathways enabling transformations of socio-technical systems.
and enabled a look into paradigm shifts rather than tackling different variants of the established system view.
complex and adaptive nature of the systems we are dealing with today are moving from one technological era to another.
In reality, predicting certain elements of a broader system such as demographic developments is not in contradiction in any way with developing multiple futures.
and qualitative methods, could be traced potentially back to the education system, where students are confronted early with choices between different options (such as the divide between social and natural sciences),
/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,
. H. Wai, Developing a horizon scanning system for early warning, in: 12th International Command and Control Research and Technology Symposium, US Naval War College, Newport R i. USA, June 19 21,2007, 2007, Available at:
Institute of Studies for the Integration of Systems, Rome. He received his engineering degree at Ecole Centrale (Paris). His key qualifications are Sustainability Policy analysis and impact assessment, and foresight studies.
or system technologies 3. The first step for devising a technology strategy is to decide if the technology is worth the investment.
When a complex technical system incorporates a number of emerging technologies, use of TRLS has proven helpful in designing a viable new system.
The key notion is that progress is likely, but precise anticipation of when a given advanced technology will be ready for application is precarious.
We count the number of literature citations and the number of patent citations in DII by priority year. 2. 1. 5. IPC (four-digit) The International Patent Classification (IPC) system,
) system is developed a hierarchical classification system by Derwent. It is similar to the IPC classification system. Whereas the IPC is assigned by the examining patent offices,
NDIA Systems Engineering Conference online at, http://lincoln. gsfc. nasa. gov/trl/Nolte2003. pdf 2003.8 NASA, HRST Technology assessments.
, Study on indicator system for core patent documents evaluation, in: Proceedings of ISSI 2009-The 12th International Conference of the International Society for Scientometrics and Informetrics, Rio de janeiro, Brazil, 2009, pp. 154 164.17 C. Zhang, D. H
Market Manage. 21 (1)( 1992) 23 31.36 E. Hajime, The suitability of technology forecasting/foresight methods for decision systems and strategy:
Alan Porter is a Professor Emeritus of Industrial & Systems Engineering, and of Public policy, at Georgia Tech, where he remains Co-director of the Technology policy and Assessment Center.
Viewed on the most general level, living systems, from cells to societies exhibit common properties, with some attending intrinsic fundamental invariants.
complex systems, including socioeconomic systems. This debate has been in great part centered on the striking similarities between biological evolution and technological/cultural evolution.
and/or simulations of technological systems stand out. D 2005 Elsevier Inc. All rights reserved. Keywords: Technology evolution;
Complex systems; Universal Darwinism 1. Introductory thoughts The main objective of this seminar concerns the exploitation of the powerful new capabilities provided by the Information technology Era to advance Future-oriented technology analysis (TFA), both product and process.
as systems increase in complexity, it becomes necessary to draw upon social experiences to provide the necessary analogies 4. This is the case in cellular and molecular biology,
The more complex and intangible the system the more useful is the resort to metaphors. That is evidently the case of the theory of evolution itself:
which follows the necessity of acknowledging the law-like aspect underlying all growth phenomena in the living (social as well) realm, mainly related to the mechanism of information transmission and increase in system's complexity.
Viewed on the most general level, living systems, from cells to societies, exhibit common properties, with some attending intrinsic fundamental invariants.
complex systems, including socioeconomic systems (see point 3 ahead). Evolutionary arguments in economics, as in biology, originally took purely verbal forms,
"In biological systems an innovation can be achieved without necessarily changing the genetic underpinnings of a feature,
2 How does heritability occur in technological systems? That is, how do technological units (whatever they may be) carry their information forward through time?
some modern approaches from complex systems theory, like self-organization, is an alternative to dbiological analogiest or Darwinism;
complex systems, including socioeconomic systems, also involving a basic philosophical commitment to detailed, cumulative, and causal explanations,
after a lapse of almost a half century after the initial thrust commented on in point 1. Basically he suggested that Darwinism contained a general theory of the evolution of all complex systems,
but the more general process of evolution of complex systems dfor which organic evolution is but one instance.
or human genes transmission it had evolved not continually toward more and more complex technological systems; the human massive capacity for culture (and technology) may be seen as a very strong capacity of adaptation to respond to very quick spatial and temporal variations, observed in the Earth homeland since the Pleistocene;
T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,
which on the whole constitutes the world system, as recently empirically and mathematically demonstrated by Devezas and Modelski 25;
the persistent opposition of mainstream economics to Darwinian concepts as applied to socioeconnomi systems, mainly caused by misinformation and non-acquaintance with the basic assumptions of Universal Darwinism;
There are two possible approaches to simulating technological and/or socioeconomic systems. The systems dynamics approach, widely used in technological forecasting
since the 1950s, is btop-downq in character (so called because it views the system from above, as a whole).
It is applied usually to human feedback systems and their dynamics (behavior over time) is defined via the change of their organization (or dstatet) as described by the system's differential equations.
Such top-down analyses are very suitable for describing the system's regularities and for identifying dominant feedback loops,
or in other words, for forecasting agents'aggregate behavior. The other approach forms the new sub-field of bartificial Lifeq (AL,
for short) that uses so-called dsoft computingt models of complex adaptive systems (CAS) that encompasses several methods of simulation
and it is characterized best as a bbottom-upq approach. Its origin remounts to the 1970s with the emergence of gaming simulation.
The most important system's property unraveled by this method is the existence of scale-free networks,
and regarding its application to technological systems see the work of Sole'et al. 31, also conducted in close collaboration with other researchers at the Santa fe Institute.
In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,
Altogether the application of these methods within the limits imposed by their own characteristics has helped researchers in unraveling some until now hidden properties of technological systems.
whose continuing evolutionary process conduces to increasingly complex systems; cultural evolution (and technological evolution as well) is the continuation of biological evolution by other means;
human technology is a part of a biologically co-evolved massive capacity for culture, managing two inheritance systems, vertical (twofold in scope, genetic and Lamarckian) and horizontal (pure Lamarckian in scope),
and more energetic among a broad innovation-driven and co-evolutionary set of processes, composing the whole of the world system.
Change 3 (1971) 75 88.10 C. Marchetti, Society as a learning system: discovery, invention and innovation cycles revisited, Technol.
Life Sci. 23 (2001) 425 465.25 T. Devezas, G. Modelski, Power law behavior and world system evolution, Technol.
and policy-making 1 5. The termmodel'refers here to a representation of the most crucial aspects of a system of interest for extracting usable information 6. The termdecision-making'is used here for the act or process of making strategies or conscious decisions
uncertainty is prevalent in complex systems and policy-making related to complex issues. Policy failures are often attributable to the omission of uncertainties in policy-making 7. Policies that would be optimal for one particular scenario often fail in most other scenarios.
when dealing with complex systems 8. Adaptive foresight studies would also hugely benefit from enhanced computational assistance 15.
but also relate to functional relations, model hypotheses and aspects, model structures, mental and formal models, worldviews, modeling paradigms, the effects of policies on modeled systems,
which a multiplicity of alternative models could be developed for how (aspects of) systems may work,
but one is not able to rank order the alternative system models, plausible outcomes, and outcome evaluations in terms of likelihood 16.
all alternative system models, plausible scenarios, and evaluations require consideration, without exception, and none should be treated as the single best model representation, true scenario,
and many aspects related to these systems and their future developments are deeply uncertain. Current attempts at steering the transition toward a more sustainable and cleaner configuration are static
In order to realize this, it is suggested that a monitoring system and a pre-specification of responseswhen specific trigger values are reached should complement a basic policy.
In Step I, the existing conditions of an infrastructure system are analyzed and the goals for future development are specified.
beyond which actions should be implemented to ensure that the policy keeps moving the system at a proper speed in the right direction.
and other uncertainties in order to generate a large variety of scenarios that are used in turn to analyze complex uncertain systems,
it could help in developing a monitoring system and its associated actions. It thus appears that EMA could be of use in all adaptive policy-making steps.
Here, the troublesome and promising regions identified with PRIM are used directly for designing adaptive policies and the corresponding monitoring systems.
a System Dynamics 50,51 model developed for exploring the dynamics of energy system transitions 3 is used in this study.
and the transition toward a more sustainable energy generation system is a grand societal challenge. This study shows how EMA
and steering transitions toward more sustainable energy systems. Thus, this study is in line with the purpose of FTA projects that aim at developing long-term, adaptive,
That is, EMA could be used to support an inclusive modeling process from the start, where different beliefs about how a system functions,
has been illustrated through a case about the structural and systemic transformation of energy generation systems toward a more sustainable future.
an exploratory system dynamics approach, in: The 28th International Conference of the System Dynamics Society, System Dynamics Society, Seoul, South korea, 2010.416 C. Hamarat et al./
/Technological forecasting & Social Change 80 (2013) 408 418 3 E. Pruyt, J. H. Kwakkel, G. Yucel, C. Hamarat, Energy transitions towards sustainability:
The 29th International Conference of the System Dynamics Society, WASHINGTON DC, USA, 2011.4 W. Walker, P. Harremoës, J. Rotmans, J. Van der Sluijs, M. Van
the actor-options framework for modelling socio-technical systems, in: Policy analysis, Delft University of Technology, Delft, 2010.6 P. Eykhoff, System Identification:
Parameter and State Estimation, Wiley Interscience, London, 1974.7 W. E. Walker, V. A w. J. Marchau, D. Swanson, Addressing deep uncertainty using adaptive policies:
Des. 31 (2004) 743 758.21 R. d. Neufville, A. Odoni, Airport Systems: Planning, Design, and Management, Mcgraw-hill, New york, 2003.22 E s. Schwartz, L. Trigeorgis, Real Options and Investment under Uncertainty:
, C. Macharis, P. L. Kunsch, A. Chevalier, M. Schwaniger, Combining multicriteria decision aid and system dynamics for the control of socioeconomic processes.
Technology policy and Management, Delft University of Technology, Delft, 2008, p. 285.37 E. Pruyt, J. Kwakkel, A bright future for system dynamics:
The 30th International Conference of the System Dynamics Society, St gallen, Switzerland, 2012.38 S. Bankes, Exploratory modeling for policy analysis, Oper.
Systems thinking and Modeling for a Complex World, Mcgraw-hill, 2000.52 G. Van Rossum, Python Reference manual, CWI, Amsterdam, 1995.53 Ventana Systems Inc.,Vensim Reference manual, Ventana
System Inc.,2011.54 Ventana Systems Inc.,Vensim DSS Reference Supplement, Ventana Systems Inc.,2010.55 M d. Mckay, R. J. Beckman, W
His research interests are exploration and analysis of dynamically complex systems under deep uncertainty. In his Phd research, he focuses on long term decision-making under deep uncertainty using the Exploratory Modeling and Analysis method.
Erik Pruyt is the Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.
Social and Political sciences & Solvay Business school of the Free University of Brussels. His research focuses mainly on the multidimensional dynamics of complex systems,
In the first case, EMA is combined with System Dynamics (SD) to study plausible dynamics for mineral and metal scarcity.
what kinds of surprising dynamics can occur given a variety of uncertainties and a basic understanding of the system.
Future-oriented technology analysis Exploratory Modeling and Analysis Deep uncertainty System dynamics Adaptive policymaking Agent-based modeling 1. Introduction Future-oriented technology analysis (FTA) is understood as an umbrella label for various approaches
Thus, technological systems can be decomposed in the physical components as well as the social components including institutions.
The solar system of planets is a relatively small system the sun and the eight planets and can be observed very well,
or systems in which humans are involved, the situation is different. In these cases, there are many components
and the system can only partly be observed. The use of predictive models for such systems is problematic.
There have been scientists who have realized this. Some claim the forecast is always wrong 4, others say all models are wrong 5,
and yet again others qualify arithmetic for such systems as useless 6. Such comments raise the question
Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems 12,13.
but where this information does not allow specifying a single model that accurately describes system behavior.
EMA is focused not narrowly on optimizing a (complex system to accomplish a particular goal or answer a specific question,
Knowing that a system can exhibit such behavior can change the debate or open up new directions for the design of targeted solutions.
The third case presents an EMA study into transition pathways for the Dutch electricity system.
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,
System Dynamics is used for modeling and simulating dynamically complex issues and analyzing their resulting nonlinear behaviors over time
diverging beliefs and ideas about system functioning, and complex interactions between supply, demand, substitution, and recycling, necessitating a more exploratory approach.
Causal loop diagrams are used often to communicate feedback loop structures included in System Dynamics models.
The model has been implemented as a System Dynamics model using the Vensim software 27.421 J. H. Kwakkel,
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
, based on his mental model of the underlying structure of the mineral/metal system 18. The objective of the joint modeling endeavor was twofold:(
Both objectives were achieved at first by means of traditional System Dynamics modeling and manual exploration of the influence of key assumptions, changing one assumption at a time.
Airports are a crucial element in this system and are major drivers of regional and national economies.
Emissions Emission Dispersion Modeling System (EDMS) the FAA required tool for emission analysis 41. Third party risk Methodology developed by the National Air Traffic Services (NATS) for third-party risk 42,43 the NATS methodology has been extended to apply to multiple runways 49,50.
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
for the Dutch electricity system. Institutional change driven by liberalization, changing economic competitiveness of the dominant fuels, new technologies,
The consequences of each of these alternative developments are assessed using an agent-based model 45 of the Dutch electricity system.
which explicitly focuses on multiple actor groups within the electricity system, most importantly the end users and the generation companies.
The first case showed how EMA can be combined with System Dynamics to investigate the types of behavior that can occur with respect to mineral and metal shortages.
while in particular the first and third case demonstrate how this can be combined with nonlinear dynamic models (System Dynamics and Agent Based Modeling respectively),
which are more appropriate for the types of systems and phenomena FTA applies to. FTA intends to guide policy
diverging or even conflicting understanding of how a system is working, and different sources and types of information and data.
the different ways of understanding a system, and utilizes the plethora of information sources available. Moreover, EMA can also be used for creatively imagining possible futures
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Vensim Reference manual, 2011.28 G. van Rossum, in: Python Reference manual, CWI, 1995.29 Ventana Systems Inc, in:
Vensim DSS Reference Supplement, Ventana Systems, Inc, 2010.30 D. N. Ford, A behavioral approach to feedback loop dominance analysis, Syst.
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Emissions and Dispersion Modeling System User's Manual, Federal Aviation Administration, Office of Environment and Energy, WASHINGTON DC, 2009.42 P. G. Cowell, R. Gerrard, D
Erik Pruyt is Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.
Social and Political sciences & Solvay Business school of the Free University of Brussels. His research focuses mainly on the multidimensional dynamics of complex uncertain systems,
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