Model

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Synopsis: Model: Model:

content and process. 3. Models and voices This session concentrated on the combination of expert opinion with qualitative techniques.

This approach should provide enhancement to models in the future. The other major discussion focussed on experts,

However, although the dominant model of foresight pursues a more modest level of aspiration than forecasting,

This work may also be supported usefully by (normally simple) simulation models. It is not uncommon that the scenario ideas derived at the workshop have to be modified considerably at this stage,

In back-office work also the same types of simulation models as in Phase 3 can be useful. 476 E. A. Eriksson, K. M. Weber/Technological forecasting & Social Change 75

Models still need to be developed of how to establish AF as continuous learning activities within public and private institutions.

In technical terms, RPM Screening is based on an additive preference model where the overall value of an issue is expressed as the weighted sum of its criterion-specific scores.

Therefore, in the model, it was assumed that each issue, if pursued, would consume an equal amount of funding resources.

Older attempts at planning the future by developing heuristic models (in the sense of futurology) were based on the assumption that the future is predefined as a linear continuation of present trends 12,13.

matching policy instruments and methodologies Innovation surveys Econometric models Control group approaches Cost benefit analysis Expert panels/peer review Field/case studies Network analysis Foresight/Technology assessment

Construction of an econometric model based on theoretically defined relation of regulations and standards and goal variables;

Technical paradigms are‘models 'and‘patterns'for finding solutions to selected technological problems, based on selected principles derived from natural sciences and on selected material technology().

Van den Belt and Rip 36 extended the Nelson Winter Dosi models for the late 19th synthetic dye industry,

This model argues that a path comes into existence behind the backs of all actors concerned

This model seeks to conceptualise path dynamics both at the actor and aggregate level (similar to technical paradigms).

This model has a crucial advantage: by repositioning the notion of path as something that is evolving/emerging in real-time,

and entanglements. 2. 2. The models of path used in this project For this project we draw on the notion of socio-technical path in its two forms:

the agreement being that new models need to be sought. For innovation chain 3 this is indeed a challenge.

Policy 6 (1)( 1977). 36 H. Van den Belt, A. Rip, The Nelson Winter Dosi model and synthetic dye chemistry, in:

Given this, there is need to develop a model capable of describing and understanding how this happens.

A model is needed thus that links all the different variables and reference levels. In this paper, a‘logic model'approach is used to develop an‘objectives hierarchy'describing the relationships between higher level goals,

and identify those elements affected by foresight exercises can be regarded as a generic objectives model for foresight exercises likely to contribute to a more participatory‘knowledge society'.

a logic model approach 18 can be used to build an impact assessment model incorporating the two previous models illustrated in Fig. 3a

and b. This model is depicted in Fig. 5. 8. Impact area specificity: networks and actor alignment Given the peculiar nature of the task at hand, namely the search for diverse impacts (from changes in social capital to more informed publics and better networking) that may

A model is needed thus capable of explaining the interdependencies and interrelatiionship between foresight system elements such as actors, processes, inputs, outputs and impacts,

The model presented in Fig. 3a and b helps to identify the main internal criteria

it is essential to complete the model with findings from other foresight exercises in different countries,

This model can then direct the development of a common impact assessment framework based on the‘logic model'approach.

and a series of interviews with foresight specialists to complete the development of the model describing the dynamics of foresight exercises in different contexts.

Second, a huge diversity can be observed among continents (note the differences among the broad models of higher education e g. in the US, Asia and Europe), across countries on the same continent,

In short, that was the Humboldtian model of universities: assuming a unity of teaching and research, based on the idea of higher education through exposure to,

6. Further proliferation of the already existing diversity of governance and management models, and more pronounced professionalisation of university management.

There is already a wide variety of governance models (different ways and weights of involving stakeholders:

The diversity of governance and management models, therefore, is likely to further proliferate, even inside the group of similar universities,

Finland and Sweden points to the possibility of a‘reformed European socioeconomic model'46,47. b This vision requires an efficient co-ordination of a number of policies,

close co-operation with businesses Some of the‘elite'universities are adapted already well to this model,

The Humboldtian model of universities higher education and basic research as almost inseparable‘Siamese twins'is still a prevailing notion in many professors'and policy-makers'mindsets.

Policy 27 (6)( 1998) 569 588.46 K. Aiginger, Copying the US or developing a New European Model policy strategies of successful European countries in the nineties, paper presented at the UN

The essence of Cunningham's model is that the application of hierarchical random graphs of technology characteristics to questions as complex as:

enables a probability model to be constructed that anticipates novel combinations of technologies. Using this model he identified a range of technology changes associated with new standards for accessible internet applications within 100days of their emergence and without prior reference to the individual technology morphologies pathways progression.

Imagining the prospects if this technique can be developed more widely conjures up exciting possibilities for the anticipation of future innovation system developments.

In addition, the paper outlines several important caveat about the use of these models in forecasting new technology:

the model lacks a model of the actor; full validation of the model requires a longitudinal analysis;

missing links may signal poor quality source materials; and content scoring remains a subjective process. 2. Application to distributed design environments Our purpose in exploring this topic is to better consider the information needs of designers.

Therefore, without a generative model of the data, the interpretation of the data may not be robust.

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

In the following section we provide a brief and qualitative account of this model. Extensive technical details of this data structure are available in the literature on complex networks.

while the generative model is connected a graph, specific realizations of the model may be disconnected. Hierarchical random graphs such as the one presented in Fig. 1 are generative models,

meaning we can use the model to infer the likelihood that any given realization will be generated by the model.

Realization 1 is a more likely representation since the most of the high probability links are observed,

and few of the low probability links are observed. This network configuration should be expected to be observed 4. 3%of the time,

According to the specification of the model there may be little or no hierarchical structure, or a network which is structured richly across multiple layers.

ð1þ 3. 4. Model search process The model simulation and fitting process allows a comprehensive search process for small models.

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

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

The search procedure guarantees no degradation in model fit as the search progresses. The Monte carlo simulation invests more computational results in the best available models,

thereby driving the search to the most promising possibilities. Gill 25 provides a relevant and comprehensive account of Monte carlo simulation for data analysis.

which can be interpreted only in light of a more elaborate model of the data. A concluding section of the paper reflects upon the sociology of science,

scripting languages (Activex, Java Applets and Visual basic Script), document models (DOM and XHTML), alternative implementations of Ajax (using JSON and IFRAME),

The Monte carlo simulation procedure evaluated a range of competing solutions to the model. There are many common features shared among the set of best solutions of the algorithm.

Nonetheless, the principal virtue of this hierarchical graph approach is the ability to use this probability model to anticipate novel combinations of technologies.

& Social Change 76 (2009) 1138 1149 As seen in Table 3 the model anticipates a threefold combination of Internet explorer, rich Internet applications and the World wide web consortium (W3c), with a likelihood of 81%.

through use of a model which anticipates architectural evolution. Furthermore, the structured representation of the data may help identify areas where competences may need to be strengthened further or even completely restored.

as provided by machine learning models and delivered by decision support systems, may contribute to an open innovation paradigm where firms work together as part of an extended technological network 11.

and case studies. 6. Interpretations from the philosophy and sociology of science The hierarchical random graph is one possible model of science, technology and innovation data.

whether such a model is consistent with what is postulated about the sociology and epistemology of science.

The goal in doing this survey is neither to validate the use of the model,

The hierarchical random graph model is missing a model of the actor. In other words, it attempts no explanation of the capabilities or interests of the innovator,

Hierarchical models might be built before and after critical time periods, and the resultant predictions compared. Alternatively, a dynamic extension to the hierarchical random graph might be envisaged.

a cyclical model of technological change, Adm. Sci. Q. 35 (1990) 6045 6633.4 A l. Porter, A t. Roper, T. W. Mason, F. A. Rossini, J. Banks, Forecasting and Management of Technology

a conceptual basis for uncertainty management in model-based decision support, Integrated Assessment 4 (1)( 2003) 5 17.8 G. S. Altshuller, Creativity as an Exact Science

Des. 117 (Special B)( 1995) 2 10.15 C. Marchetti, N. Nakicenovic, The Dynamics of Energy systems and the Logistic Substitution Model, International Institute for Applied Systems analysis

infrastructure organizations are confronted with an increasing amount of future uncertainty 3 that calls for a fundamental reconsideration of the former success model, at least in three respects:(

The proposed process thus follows the model of an analytic deliberative decision making process 54. As a result, these procedures will most likely not provide very specific recommendations which can be implemented directly.

Following the model of action research 56, the project team was involved not only in devising and specifying the method

procedure and methods The steps of the RIF procedure are sketched in this section according to the phase model from Miles

and policy from linear to systemic innovation models has challenged the conventional technocratic and technology oriented forecasstin practices and called for new participatory and systemic foresight approaches 3. Also the R&d functions are moving from the basic science

which structures the information in three dimensional model (I-Space): concrete abstract, undiffused diffused and uncodified codified.

o data on the system being analysed and on all the associated substances, o operational model of the system under analysis, o systematic hazard identification procedure and risk estimation techniques,

A generic model of the risk assessment procedure, applicable within the Nordic countries, will initially be framed.

SECI and SLC models give foresight and risk analysis studies a common theoretical ground. Both models organise the knowledge making in three dimensional space generating the knowledge from personal and proprietary to common sense and public,

from tacit to explicit knowledge through sense making and field building, dialogue and interaction. The core idea is to share the knowledge

No matter the size of the model or the computer that runs it, some developments are beyond current discovery

But if the model and the real system are in a chaotic state, the results of a policy may be exquisitely dependent on a number of factors other than the policy itself.

quite different results might be obtained on successive runs of a model (or in two bplaysq of reality) with the same policy,

Second, nonlinear models can be built to simulate real life systems that operate in a stable mode most of the time.

Such models can be used to find conditions that drive the systems they simulate into oscillatory or chaotic states.

Then, using the model, policies can be found that move the system back toward stability.

In the old days validity was tested by building models with data through some date in the past

and then using the model to bforecastq the interval to the present. If there was a match,

the model could be believed and used in forecasting. Now we see that if the system was in a chaotic state,

Nevertheless, such models are useful because they can point the way toward stability, establish reasonable ranges of expected operation, show periodic tendencies,

Fourth, using such models, the analyst can identify the future limits of operation of a system

None of the model-based scenario exercise included surprises 35. In this context another study suggests that standard scenario approaches tend to systematically exclude surprising or paradoxical developments as inconsistent or logical impossible.

Change 75 (4)( 2008) 462 482.13 A. Volkery, T. Ribeiro, T. Henrichs, Y. Hoogeveen, Your vision or my model?

have a particular model of what they expect of the outcome, even though‘implementation'is stressed always as an important element of the programme design.

and Arie Rip have termed pre-engagement through socio-technical scenario building 2. It involves the combination of exploration of dynamics using theoretical models

model 14 16 and sociology of expectations) 9, 17,18. Evolutionary theories of technical changes emphasise that for innovation one should think of variation and selection (and retention of those selections.

evolutionary models of technical change; the innovation chain+,and endogenous futures. This framework which can help in structuring large amounts of heterogeneous data,

what they have to offer. 2. 1. Lacunae in evolutionary models of technical change How do innovations come to be selected from a number of possible options;

This model is a complex mix of perspectives, and is a combination of technology studies, innovation and management studies,

which shape action (this is emphasized in the quasi-evolutionary model mentioned earlier). Van Merkerk and Robinson 9 show examples from the field of lab-on-a-chip technology and how expectations have an effect on selection choices of pathways to follow,

Manag. 9 (2)( 1997) 131 148.15 H. van den Belt, A. Rip, The Nelson Winter Dosi model and synthetic dye chemistry, in:

Traditional alternatives to rational-analytical models of decision processes are political models and anarchical models (e g. the garbage-can and muddling-through models).

To these authors there seems to be a relationship between Martin's definition of foresight

In this sense, the strategy processes of the Energy research programme corresponded with the Mode 2 model of research.

and outlined according to rational-analytical models of decision processes whereas research councils seem to follow other models.

There is no doubt that a more rational-analytical approach is appealing, especially for technical research councils. In reality, however, the processes involve a strong element of power play and politicca negotiation.

Initially, the prevailing technocratic and linear process models of policy making (e g. in terms of formulation implementation evaluatiio phases) were replaced by cycle models,

Already in these cycle models, policy learning is seen as an essential ingredient of political governance.

With such an open and distributed model of policy making in mind it is recognised now increasingly that an opening of political processes is necessary to ensure the robustness and the effectiveness of its outcomes.

Weber 2006) which in line with the networktyyp distributed model of policy-making processes are provided simultaneously rather than in distinct phases:(

As a loose analogy, consider the change from the handmade automobile to the assembly line Model T Ford beginning in 1908.

and plot such a model. We then inspect it, decide a different growth limit should be investigated,

The Model T analogy carries over here too (loosely) the availability of this standard vehicle enables an efficient infrastructure to develop around it.

A l. Porter/Technological forecasting & Social Change 72 (2005) 1070 1081 1072 Innovation indicators are rooted empirical measures in models of how technological innovation proceeds.

much as the Model T revolutionized production processes. References 1 A l. Porter S w. Cunningham, Tech Mining:

The model and modelling techniques in use guided the data gathering of the system analysis part. Autonomous There was still a significant degree of freedom to adapt to the perceived needs during the process and the development of roadmaps and scenarios.

and even if our models are fit for purpose, there are always factors that lie outside of these models that may intervene.

In the case in question we are led typically to think of the‘‘obvious catastrophes''and more or less wild cards mass epidemics, asteroid impacts, supervolcanoes, accelerating climate change, and the like.

Often we may doubt the adequacy of our models, of course, and not only because we believe that they should be able to incorporate at least some of the exogenous factors.

A whole class of economic models, routinely used to inform policy, are based on assumptions about economic affairs tending towards equilibrium that have very little relation with the behaviour of businesses (especially innovating enterprises).

when invoking conventional economic models for short-term analysis and forecasting, at least in less turbulent times.

We can examine their plausibility and limits, their internal consistency and conformity with models and data,

These are the sets of knowledge that are to do with the consequences that our models of situations

the assumptions and models they embody should be explicit and intelligible, open to being assessed critically by participants in,

For selection to be successful, this step results in a model of what is expected to take place (under various contingencies),

The knowledge and mental models of practitioners and stakeholders may have to be brought into play in such cases.

and knowledge One of the most influential contributions to thinking about organisational learning and KM has been the model of the dynamics of shared knowledge creation developed by Nonaka and Takeuchi 19.

This model has been used to characterise a wide range of innovation processes, including FTA ACTIVITIES. The four steps, captured under the label SECI (socialisation, externalisation,

Knowledge acquisition learning how to use models, formulae, equipment, methods etc. Combination: systematising and/or translating formalised concepts into new frameworks, procedures, etc.

some organise this information into lists or more structured frameworks even models; some allow for synthesising information into posits, views of alternative futures,

Eerola's account of the various steps and procedures of the Nordic H2 energy foresight are located in terms of the SECI model in Fig. 2

with discussion about the connections between ideas proving a good basis for exchanging information about implicit models and theories.

and the implicit model of the system under consideration; scenarios may be differentiated in terms of key uncertain drivers, broad archetypes about system performance,

Formal models (for example, diffusion and substitution analyses) can only go so far for example, with more qualitative analysis required to explore possible factors shaping take off points, ceilings, novel applications of the technology that is diffusing, and so on.

But qualitative speculation about how and how far new technologies may be used will also do well to be grounded in terms of available understanding (i e. models) of product cycles and diffusion curves.

(and time) to examine the underpinning assumptions of models (not to mention intellectual familiarity with the conceptual underpinnings of social and economic models).

(also published as Models of Doom, Universe Books, New york, 1973). 45 D. H. Meadows, D. L. Meadows, J. Randers, W. W. Behrens III, The Limits

Traditional regulatory models would impose mandatory rules on a company to ensure that it behaves in a socially responsible manner.

by an analogy relating industrial systems to natural systems, a model for a desirable transition to cleaner production:

The organisation of industry on this principle with the waste products of some branches of industry providing raw material for others means in effect using natural processes as a model,

with the next area being new governance models. Overall, the lists provide a fertile field for some real dramatic change scenarios centred on some key discontinuities.

The rapid progress in information and communication technologies enabled the application of sophisticated transport models.

Whereas the intention of quantifications using numerical models or cost-benefit analyses are often quite clear to decision-makers,

''which arise from a lack of knowledge about the appropriate model or theory that might be relevant for a particular phenomenon,

Remaining with this‘‘web of nodes''-model, a policy intervention in the transport sector directly affects at least one, maybe several of these nodes.

The model illustrates that a policy intervention can lead to widely ranging effects, and some of them may only become visible after the measure was implemented.

which largely can be described by quantitative relations (transport models) or, at the other extreme, provide more punctual knowledge from rather different areas

Transport models show a certain slice or cut out of the web, with some selected nodes and the linkages between them.

It should be noted that the original epistemic function of a model is to reduce complexity

In relation to the typology of levels of knowledge described in chapter 2 it can be concluded that models are focussed mainly on improving knowledge in the field of knowns.

So, models are hardly able to deal with known unknowns or unknown unknowns. Other tools with a different and/or broader focus are needed.

In general, they allow for the further specification of knowns rather than for the detection of any unknowns (see Figure 1). Typical examples are transport models.

In the case of complex models in particular, there might also be surprises regarding the character of the effects;

but only for factors that are considered already in the model. Another example for structurally closed methods are cost benefit analyses (CBA),

Brainstorming Quantitative models Open space Cost-benefit analysis Expert workshops Multi-criteria analysis Focus groups pta methods Explorative (qualitative) scenarios VOL. 14 NO. 4 2012 jforesight jpage

In general, several tools are combined in a scenario process (workshops, CBA, trend analyses, models, Delphi, roadmaps and others.

which are creating the basis for transport planning models (TPMS), might be subject to a high degree in uncertainty.

and the assumption that variables excluded from the model will not be instrumental in modifying travel behaviour over time.

quantitative tools such as models have become much more sophisticated, there are still many examples that uncertainty in relation to such assumptions is acknowledged not sufficiently.

The inability of the models to understand and predict fundamental societal changes was the most often stated reason for the inaccuracies.‘‘

‘‘The change in the labor force due to increased participation of women was one of the commonly quoted examples of the model's inability to properly account for travel behaviour''.

models were used for quantitative assessments. A wide range of stakeholders have been involved as well as, on a smaller scale, the wider public in form of an online survey.

Limitations of models and other quantitative approaches have to be discussed in relation to the data that is included in the process.

only selective knowledge can be gained on social phenomena through quantification due to the fact that the models normally only consider a reduced amount of variables that describe social realities (Grunwald, 2009).

Evaluating Models for Environmental Discourse, Risk governance and Society, Vol. 10, Kluwer Academic, Dordrecht. Risk Commission (2003), Ad hoc Commission on‘‘Revision of Risk analysis Procedures and Structures as well as of Standard Setting in the field of Environmental Health in the Federal republic of germany'',Final Report, Salzgitter Federal Radiological

Distinctly different in the sense that when we make successful models the formal systems needed to describe each distinct aspect are not derivable from each other.

of being conscious and aware about the nature of the mental model, i e. being a description of perception rather than a description of reality (Schwartz, 1991),

which one works and lives in determines how one thinks (the mental model or frame),

which a system is perceived use of different mental models, or reframing. Again, policy makers need to watch for the‘‘emergent''properties that arise as a system organises itself following a policy intervention,

We will therefore use a simple, generic, policy-making model, Table II adapted from Bhimji (2009)- direction,

and schools, a distributed behavioural model 1'',Computer graphics, Vol. 21 No. 4, pp. 25-34.

The aftermath of quantitative models left the urban planning field with a profound scepticism of any kind of future-oriented analysis.

simulation models. Therefore, the third step should be based on quantitative tools and be restricted to urban experts. 4. Determination of spatial implications.

economic models and public policies towards SD. 2. Horizontal axis. Showed the availability of resources needed to achieve the sustainable development goals in the future.

but at the same time, Spanish society as a whole is inclined to support sustainable development models.‘‘‘‘Back to basics''is marked by the failure of the previous development model,

which has led to social tension and frustration. Public and private agents are fully aware of the need for sustainable development due to a lack of response by the economic and technological realm.

In this scenario, Spanish society suffers a deep disenchantment with the socioeconomic model that prevailed at the end of the twentieth century.

This model requires a strong set of management skills in all public organisations to guide participation and coordination actions.

This scenario generates strong environmental and social impacts due to a model based on strong economic growth and intense consumption (see Figure 5). Public policies related to urban development are implemented not effectively because of social and economic pressures.

resources are scarce and social attitudes are very favourable towards the implementation of strict SD models (see Figure 6). Due to difficulties in enacting

In this scenario, the governance model is managed by a strong centralised power base (probably the State) that makes major decisions regarding the pattern of urban development to be implemented by regional and local authorities.

This model will alternate compact buildings with abundant public spaces which will facilitate social relationships

3. 1 Simple mathematical algorithm Economic growth K GDP(%)annual) 24.3 Sustained 2. 1 Strong 3. 7 Negative 24.4 Econometric model

Strong decrease 535 Land-use models Society Population growth. Variation in population over a year, expressed as a percentage of the difference of the number of individuals in the total population at the beginning of that period Urban density.

Figure 8 Spatial implications of Scenario A (2025) VOL. 14 NO. 4 2012 jforesight jpage 329 B Build a new economic model that is environment-friendly

and develop visions of alternative futures for a transition towards a more sustainable model. VOL. 14 NO. 4 2012 jforesight jpage 335 To purchase reprints of this article please e-mail:

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