or knowledge databases such as Wikipedia) to construct a model of society capable of simulating what the future holds for us.
The analytic methods used in the predictive-policing model do not identify specific individuals. Rather, they surface particular times
Up until now, the application of ICT to Law has enabled the development of new models for understanding
In effect, the application of this particular ICT-based FTA instrument to Law will enable the development of innovative models for researchers,
together with the use of scenarios, models and simulations to anticipate the set of possible implications that a new proposed law may produce, bear important similarities with the combination of ex ante with ex post impact assessments.
and measurement systems optimized for the Industrial Age models of production. According to the author, foresight needs a paradigm shift in the Knowledge society,
overcoming the epistemic models of FTA that inherently assume a world that evolves as an extrapolation of the past,
D. S. 2011),‘Using the international futures (IFS) model for scenario analysis: combining quantitative and qualitative methods,
3 Other examples are immersive decision theatres (offering a virtual environment facility to visualise output of predictive and scenario-based models with the aim to support decision-making (Edsall and Larson
'and showing how innovation leads to unpredictability that cannot be removed by more accurate data or incremental improvements in existing predictive models.
'and shows how innovation leads to unpredictability that cannot be removed by more accurate data or incremental improvements in existing predictive models.
It then introduces Bergson's model of creative evolution, showing that it leads to ontological expansion,
In contrast to Darwinian models of evolution where selection weeds out unsustainable developments, in the Bergsonian model, living processes*Email:
and predictive models are theoretically incompatible. Policy-relevant future-oriented analysis, therefore, needs to emphasise processes that support insight, intuition,
If we only had accurate data and models, we could have good predictions. In this view, our data and models are only approximations,
and epistemic progress can occur through incremental improvement. Although there may be cognitive and economic limitations, in this view,
The prototypical narrative of the traditionalwestern model of innovation can be found from the first chapter of Genesis. The 1769 version of King James Bible tells us how cattle
This model of creativity underlies much of innovation research still at present. It assumes that as new entities are brought to life,
Cattle, in this model, can clearly be separated from beasts. All the creation can be categorised at the moment of creation.
In practice, such a model assumes a creator who has a blueprint of the different types of animals
and ethnographic point of view, this model is clearly a problematic one. Animals, as well as technologies, are domesticated in a historical process.
In this model, the narrative structure simultaneously generates the key categories of‘creator',‘object of creation'
as a side effect, creates a specific model of reality and ontology. Although the linear causality of this model is rejected now often
and the role of users is emphasised, the underlying static and preexisting ontology is still frequently taken for granted.
In contrast to this biblical ontological model, below we adopt a model of constant creation that relies on a different ontology.
In this model, innovation occurs when social practice changes. The history of innovations and technical change shows that‘heroic innovators'are located often in the downstream.
In contrast to the traditional heroic‘upstream'innovation model, downstream models emphasise the active role of current and future users.
For example, in the multifocal model of Tuomi (2002), new technical functionalities and propensities are thrown in effect from the‘upstream'to a‘downstream'field of interacting social practices,
how can any model capture its key ontological dimensions? Henri Bergson explored this question in great depth over a century ago.
In contrast to the Darwinian model of evolution, where living beings are essentially stochastic samples and passive subjects for environmentally driven selection,
In the Bergsonian model of evolution, the process of life creates newforms and newpossibilities for action.
he did this to add a historical and irreversible element in the prevailing equilibrium models in economic theory.
One possibility is to take the Bergsonian model of evolution seriously and define technical change as a specifically human form of élan vital.
One possibility is to use Leont'ev's (1978) hierarchical model of human activity, which decompoose socially motivated and specialised activity into goal-oriented acts and further into concrete observable operations that implement the acts. 4 In this hierarchical structure,
In this model, Darwinistic selection may weed out those developmental forms that are incompatible with survival and reproduction.
Darwinistic models, however, are inadequate for explaining the process of evolution, as evolutionary change is strongly underdetermined by selection (Varela, Thompson, and Rosch 1991,195).
According to Rosen (1985), anticipatory systems are systems that contain predictive models, allowing future to have an impact on the present:
but rather the output of the model through which I predict the consequences of direct interaction with the bear.
therefore, needs to include a model that generates predictions. In some cases, the model can be hardwired'in the biological system.
For humans, anticipation is hardwired less, and we can continuously adjust our expectations and predictive models.
Humans are also able to use scientific models for prediction. Scientific models create linkages between natural and formal systems.
In Rosen's terminology, natural systems include stones, stars, solar systems, organisms, automobiles, factories, cities, and any other entities in theworld where a set of observable qualities can be related.
Natural systems are the substance matter of sciences and what technologies seek to fabricate and control.
In practice, this means that if the formal model is good enough a representation of the natural system,
create hypotheses about the unobservable causal relationships, fast forward the formal model to a future point of time,
This, indeed, is the only way we move from simple correlations to theoretical models. The modelling relation,
is shown in Figure 1. To create a formal model, we have to encode the states of the natural system into corresponding states of the formal system.
and impute causality on it based on predictive models. Causality, in particular, cannot therefore be‘found'from the nature.
It is a reflection of a predictive model created through our cognitive effort. Science makes use of logical
a constructivist view on the importance of active human cognition-creating models of the world, a Darwinian terminology of natural selection,
This is because natural systems are also cognitive constructions, partially based on existing anticipatory models and partially on the available repertoire of cognitive categories.
Predictability requires anticipatory models that, in turn, require a fixed ontology. We construct natural systems and their associated predictive models by abstracting the lived reality.
As Bergson (1988) pointed out abstraction itself relies on memory. This means that both natural systems and their predictive models are necessarily to a large extent retrospective.
We see the world in a way that used to be interesting and relevant for us. In slightly more provocative terms, predictive and formal models live in a phenomenological world that is fundamentally a reflection of the past.
Using Figure 2, we may now reformulate the distinction between epistemic uncertainty and ontological unpredictability. Epistemic uncertainty is located on the right-hand side of the figure.
because the natural system may be mapped into inaccurate predictive models using codings that leak information, and because the observables can be measured with error.
and makes their predictive models obsolete. Implications for foresight and future-oriented analysis What are the practical implications of the above conceptual analysis for foresight and futureorieente analysis?
At that point, our models of the world also change and we become able to start to gather facts and data about the new phenomenon.
'and used Thom's catastrophe theory to illustrate a model where the same values of‘control variables'can be associated with very different outcomes.
formal models rarely provide useful predictions. Innovation expands the ontological space, making previously invisible aspects of the world visible and relevant for modelling.
formal models cannot be made more accurate by collecting more data or measuring the observables more accurately.
instead, we need a different model. This creates a challenge for formal modelling. In practice, many future-oriented models are based on time-series data.
Such data can be collected only if the ontology and its encodings and the measurement instruments that generate the data remain stable.
In general, the data required for formal models are available only in domains where innovation has not been important,
as the data are collected on categories that used to be important in the industrial economies and value production models of the twentieth century.
Similarly, reactive what if models can only provide predictive value if innovation is unimportaant Specifically,
there is little reason to believe that conventional‘impact analysis'models could lead to useful insights if innovation matters.
facts exist only for natural systems that have associated measurement instruments and established encodings and decodings between the natural system and its formal model.
instead of formulating predictive models. In other words, the focus of future-oriented analysis should be learning, problem redefinition, and innovative construction of new empirically relevant categories, not predictive modelling.
'when we assume an industrial age model of factory-based production, industrial era life patterns and health services,
For purely ontological reasoons foresight cannot be based on reactive models. Models inspired by physics, control theory,
or economics are structurally unable to encompass ontological expansion and innovation. They should therefore be used with caution.
A socio-cognitive model of technology evolution. Organization science 4, no. 3: 527 50. Geels, F. W. 2005.
A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment 4, no. 1: 5 17.
such as analytical models, computer simulations or information constructs Meredith chose a number of measures to describe the poles of the horizontal dimension.
At the artificial pole, FTA uses highly abstracted and simplified models such as linear representations tends to yield conclusions with high reliability
For that appreciation to return the current global circulation climate models (GCM's) may need to be seen as simply a module in a much bigger global model.
and all models and surveys are, to an extent, representations of the opinions and beliefs of their designers (4) The commonly believed metaphors of foresight,
The promised future situation contains sequencing of genes, characterisation of proteins, databases, dynamic models and so on.
The role of technological expectations in a mixed model of international diffusion of process innovations:
Macro forces and their likely evolution are described in BASF‘Global economy Scenarios',where econometric models elaborate basic data in both qualitative and quantitative terms,
A model for uncertainty and strategic foresight In the prior sections, we sketched the strategic foresight approaches that emerged from our data through
and what responses it could adopt (e g. postponement of the launch of new models: response uncertainty.
and a model of how companies create enduring continuity needed for sustainable development (Brundtland 1987). This paper suggests a dynamic framework of continual learning to enable businesses to anticipate
In this context, Section 2 outlines that current models responsible for moulding a business's competitive advantage sustainably are weak in the nature of stakeholders'involvement in strategic partnerships.
Figure 1 depicts the business's Activities Model and shows the main value-creating activities that a business needs to sustain it in the long term.
Theactivities Model (Figure 1) is based on the quantum leaps model devised by Shelton (1997), which describes the necessary capabilities needed to transform our organisations
firms should work out their own model that brings new opportunities through dialogue and interaction, being transparent and accountable to stakeholders (Brinch-Pedersen 2003).
The Maturity Model is an attempt to build FTA's contributions to a structure heavily dependent on the flow of ideas, data and information into a business and its network decision-making in its place in society.
The aim of the Maturity Model is to shape a possible business path towards sustainable developmment outlining how the network value activities ought to evolve in time to shape business sustainability.
The Maturity Model suggested in Table 3 (Cagnin 2005) uses the notion of evolution in
The design of the Sustainability Maturity Model is founded on universal principles as well as the maturity of behaviours that can lead to the development of a mature business throughout its network of relationships (Cagnin 2005)
As a reminder, the model seeks to enable a common strategy and/or strategies aligned across the network,
and support decision-making effectively by using models, such as sympoiesis, that emphasise the creative aspect of living systems which,
legal Run the business Implementing the vision of sustainability Business Sustainability Maturity Model Business Path to Sustainability Comparing present performance (as it is) with the business
Project management process maturity (PM) 2 model. Journal of Management in Engineering, 18, no. 3: 150 5. Larsen, A. H. 2003.
Model Model Model Framework Framework Framework SIGMA SIGMA SIGMA LCA LCA LCA NS NS NS ISO 14000 ENAS SA800 CP
Benchmarking was accomplished by comparing the visualizations with the mental models of IA leads experts who have used,
if large differences were found between them and the leaders'mental models of their areas.
and add detail to their mental models. After completion of the benchmarking activity with Sandia-specific visualizations
one of the purposes of generating a Sandia-specific map of IAS was to benchmark the map against the mental models of IA leads.
about his mental model of:(1) the CIS area, and (2) perceived overlaps between CIS and the other four areas,
A graphic describing the CIS IA lead's mental model of the overlap between CIS
and correspond well to the mental model's view of unique space. Significant overlaps between CIS and ES at the bottom of the main CIS cluster
These two areas, ES and MST, show the Fig. 2. CIS IA leader's mental model of CIS overlaps.
which correlates well with the mental model of Fig. 2. EP shows two small areas of overlap with CIS,
This also matches the mental model quite well. One area of particular interest on the map is that found in cluster at the lower middle of the graph.
integrating them into their mental models, and foreseeing how overlaps, collaborations, and new opportunities can benefit the return on investment to their IAS,
In a parallel effort, we plan to investigate different models of impact and join the best of those to our visualizations to answer questions related to return on investment 11.
In order to realise this, we propose a model that separates roadmap knowledge spaces from the roadmap scope.
Our model separates roadmaps with R&d scope and roadmaps with systemic scope. Figure 3 shows an ideal model of roadmap knowledge spaces.
In the figure we have singled out four knowledge spaces that are important in the context of RTOS (see also Table 1). The model combines the four knowledge spaces with three basic temporal scales (past, present, and futures.
Thus, our model presumes that there is a‘scale continuum'on which technological development can be interpreted: at one end of the continuum there is technology as a mere object (a solution),
In our model, the knowledge space that analyses these wider socio-technical constellations is the strategy space.
Our model starts with a presupposition that in the technology and social/actor spaces the exploration of the more radical futures is restricted usually by the overaal need to identify certain actions in the present.
In our ideal model, we have depicted, for example, disruptive futures (phenomena that change the name of the game),
The integrative methodology rested on the model of expansive learning (Engeström 2001. In the process, two practical methods were added to the model of expansive learning.
First, impact evaluation was used to gain a systematic view of the past (see Halonen, Kallio, and Saari 2010.
for example, through the application of life cycle analysis. The second aspect was to open the field towards more efficient use of ICTS in the processes, such as solutions for distance-based monitoring, the use of building information models,
We presented a model of a process-based roadmap with four knowledge spaces, which extends the horizons of roadmappiing We also presented four case examples the Building Service Roadmap, SSB Network, Construction Machinery Roadmap,
TDS models can serve to identify the key institutional actors, spelling out enterprise requirements, and spotlighting leverage points to affect the prospects of successful commercialisation.
3. successful model for healthy-aging society; and 4. secure life. 2. 2 Delphi Delphi is characterized by repeated questions for the collective convergence of opinions,
Cagnin and Loveridge (2011) discuss challenges as well as detailed models and processes. They describe how a business can become more and more receptive to foresight results,
which established models and methods are suitable for such dialogues. Welp et al. 2006) have investigated this area for science-based stakeholder dialogues.
2006) with relevance to strategic dialogues Model/framework Relevance to strategic dialogues Rational actor paradigm (RAP) The RAP assumes that all individuals maximize their personal benefit without communication with,
since they are answerable to the members of the trade body Bayesian learning This model describes the beliefs an individual has about the world
reservations and sensitivities but need to do so without assuming these can necessarily be influenced The process of beliefs being updated that is captured by the model of Bayesian learning applies in several contexts during a strategic dialogue.
Strategic dialogues need to be flexible enough to cope with this kind of change Organizational learning In such models,
5. 3 Strategic dialogue to develop a model for public private partnerships A third example of a successful strategic dialogue was the definition of a novel type of innovation cluster across academia and industry implemented as public private partnerships.
A range of views on numerous aspects of cooperation models was collected from selected stakeholders from academia
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
mobility and higher education Learning as a lifelong behaviour Vocational skills gaps Technological development Converging technologies The increasing pace of technological change Technology platforms Open innovation models Death of intellectual property?
models Cloud computing Peak oil Energy security Ageing populations ICT in education Banking regulation? Table V Challenges identified with their potential research implications Examples of challenges identified Potential research implications Energy Ireland is dependent on external sources of energy supplies at present
and where the traditional political and governance models are being disrupted. How can Ireland maintain its standing
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
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.
in contrast, can establish casual relations (without which models can be misleading), and identify major discontinuities in trends and/or new ones.
Both Hamarat et al. 11 and Kwakkel and Pruit 12 apply an approach to forecasting that uses an ensemble of different models to explore a multiplicity of plausible futures (Exploratory Modelling
they propose a model to calculate the TLC for a technology based on multiple patent-related indicators.
In this context, the proposed model focuses on devising and assessing patent-based TLC indicators using a Nearest Neighbour Classifier,
and future research should take this into account to test the validity of the proposed model.
They analyse whether models can be used at all in decision-making under uncertainty. In this context they claim that Exploratory Modelling
EMA is an iterative model-driven approach for designing dynamic adaptive policies and it deals with uncertainties by using an ensemble of different models to explore a multiplicity of plausible futures (or scenarios.
Policy options across the future world ensemble are calculated and compared in an iterated process until the suggested policy provides satisfying results.
ii) developing a hybrid model for airport performance calculations to underpin an adaptive strategic plan,
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,
Results indicate that a wide variety of hybrid value creation models with novel configurations of innovation actors emerged.
while there is as yet no clear methodological answer to the identification issue there has been some institutionalised responses and new organisational models of FTA,
12 J. H. Kwakkel, E. Pruyt, Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty, Technol.
Tuomi 14 suggests that the ontological unpredictability4 of innovative process cannot be removed by more accurate data or incremental improvements in existing predictive models.
the development of online models accessible to a whole community, or the engagement of a wider group of participants in data analysis (for the latter, see Cooke and Buckley 7). 3 In this regard,
and predictive models when disruptive and downstream innovation become more frequent, based on the argument that we can only retrospectively know what we are talking about, due to the unpredictability of natural, behavioural and social processes that shape innovation. 387 K. Haegeman et al./
who see FTA EXERCISES as attempts to collect knowledge about‘posits'or possible futures, their plausibility and limits, their internal consistency and conformity with models and data, their consistency with expert judgement,
Qualitative data can provide additional evidence to quantitative models by inclusion of new indicators created from quantified expert judgments.
Cunningham and van der Lei 28 use such an approach for models providing support to decision-making on the selection of new technologies and discuss the issue of providing equilibrium between different groups of experts and stakeholders.
Firstly, it is assumed often that models belong exclusively to the quantitative domain and have objective predictive power.
Models are simplified a representation of reality and can be quantitative or qualitative, depending on the type of data they rely on.
The latter, in the case of quantitative models, take the form of numbers with associated probability distributions
and confidence intervals (depending on the model). However, in the case of long-term horizons, such informed estimates have limited only predictive value.
the value of models is not so much in their ability to tell us with a degree of certainty what will happen to society,
Therefore, a model is more valuable as an analytical rather than a predictive tool. This means that both quantitative and qualitative tools and techniques should be judged not so much against the accuracy of their prediction on the future
and ignorance typically issues being dealt with by FTA the value of models is (at least) as much in the process as in the output. 8 Another common misconception associates subjectivity and value judgement to qualitative processes,
and thus tailoring foresight phases to different foresight functions. 15 Typically, quantitative models present higher credibility for shorter time horizons,
An exception is the International Futures Model, which can be used to examine long-term and interacting global development issues 73.393 K. Haegeman et al./
In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators.
The model includes the following steps: first, we focus on devising and assessing patent-based TLC indicators.
and fitting of growth models to project possible future trends 5. Most trend projection is naïve i e.,
and hope that would help decision makers estimate its future development trends. 2. Methodology The model that we build to calculate the TLC for an object technology includes the following steps:
and tries to answer the question on the validity of evolutionary models of technological change. After some introductory thoughts in the first part, it is tried in the second part to summarize in five points some of the still missing pieces to complete the puzzle to developing a firmly based Evolutionary theory of technological change (ETTC.
making six fundamental theoretical considerations that were accounted not yet for in formal models and/or simulations of technological systems stand out.
what has delayed the entrenching of evolutionary economics as a powerful alternative to other current economic models.
and models were advanced. 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
More recently, Devezas and Corredine 12 proposed a generalized diffusion-learning model to explain the succession of long waves in the techno-economic world,
With this short collection of ideas I wish to suggest that a firmly conceptually based danthropology of techniquet is still lacking in the current attempts of model building and formal theorizing of an ETTC.
but there is relatively few work proposing formal models and using simulation methods in this field. Among the reasons for this lack of practical-oriented works we have referred to:
for short) that uses so-called dsoft computingt models of complex adaptive systems (CAS) that encompasses several methods of simulation
Theoretically and methodologically this approach makes possible the construction of models from the level of processes that are immediately and empirically observable, namely the local interactions of single units (agents) governed by local rules.
Percolation models. It is a numerical simulation method for the search of complex technology spaces based on percolation theory,
In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,
As a first step toward a research agenda for future development of TFA I propose the realization of an international seminar in this field (Evolutionary theory of technological change) bringing together specialists in evolutionary model building and digital Darwinism to discuss the existing approaches
model of technological change, Technol. Forecast. Soc. Change 3 (1971) 75 88.10 C. Marchetti, Society as a learning system:
Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.
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