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.
Cost-benefit analyses based on advanced modelling are standard procedures in many planning processes. In the meantime, it can be observed that more qualitative and discursive methods are stipulated by actors in the process or proposed by the project leaders.
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,
From a policy analysis perspective, the transport system, with its components and their interrelations, could be understood as an abstract conceptual model
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.
But wide parts of the‘‘real world''cannot be included in modelling approaches; it is not possible to detect any effects in excluded areas.
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.
is the case that quantitative modelling is used but leads to either controversial results or-from an ex post analysis perspective was proven to regularly provide obviously wrong results.
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.
which the question of clarifying the assumptions of modelling still is a crucial issue: the planning of an underground railway station for the City of Stuttgart,
since the modelling had been based on VOL. 14 NO. 4 2012 jforesight jpage 289 assumptions which they considered as being wrong.
because it was shown not in the results of ex ante modelling approaches. Again, this means that the cause-effects relations between infrastructure supply and traffic demand were obviously not fully understood,
important effects were reproduced not by the modelling. Also in this case, more open methods would have been needed to raise awareness for the uncertainties in the planning.
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
http://optic. toi. no van Asselt, M. B. A. and Rotmans, J. 2002),‘Uncertainty in integrated assessment modelling'',Climate change, Vol. 54, pp. 75
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.
favouring the use of quantitative forecasts, based on sophisticated algorithms and mathematical models (Hall, 1996; de Tera'n, 1996.
The aftermath of quantitative models left the urban planning field with a profound scepticism of any kind of future-oriented analysis.
This conceptual model may vary a lot from one country to other. For example, some steps of the value chain may be mandatory in certain places,
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
However, modelling tools should support the process and not drive it. In fact, the sophistication of many statistical and mathematical models is more apparent than real
when it comes to tackling the demands of contemporary cities. Therefore, a foresight method like the one proposed here should not lose its eminent qualitative nature.
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:
Keywords Law, Future-oriented analysis, Foresight, Scenario planning, Modelling, Strategic planning, Forecasting Paper type Research paper 1. Introduction Future and Law 1 are two words that are rarely found in the same phrase.
a modelling system with the ambitious plan of turning massive amounts of data into knowledge and technological progress.
or knowledge databases such as Wikipedia) to construct a model of society capable of simulating what the future holds for us.
reflected in its proposal to use modelling systems (along with its data mining procedures) to better enable
In effect, the use of modelling systems corresponds to one of the most recent trends in FTA.
Through the use of modelling techniques and simulation platforms like the one described above, the anticipation of the future is increasingly being carried out through the advanced tools that help process, search,
and the future consequences that a particular piece of legislation would address (preferably through the support of scenario planning and/or the use of modelling analysis). In order words,
The analytic methods used in the predictive-policing model do not identify specific individuals. Rather, they surface particular times
This is the case of modelling systems, such as Futurict. VOL. 14 NO. 4 2012 jforesight jpage 343 The application of modelling techniques to the legal domain represents a step further in the use of ICT, Artificial intelligence (AI) and other advanced
computer applications to this particular area. Up until now, the application of ICT to Law has enabled the development of new models for understanding
and working with legal systems (through, for instance, knowledge based systems and intelligent information retrieval. With the development of modelling techniques and instruments such as the one described above,
the impact promises to be even greater. In effect, the application of this particular ICT-based FTA instrument to Law will enable the development of innovative models for researchers,
legislators and legal practitioners to better understand the world in which Law needs to operate.
such as modelling analysis and simulation platforms, brings additional advantages to Law. In effect, the systemic collaboration between different FTA METHODS, namely between quantitative and qualitative methods is becoming increasingly popular
scenario planning can be associated with modelling analysis to allow legislators to test different legal options and regulatory solutions within simulated environments.
I believe that the employment of modelling systems in political discussion and deliberation exercises should also be used in the preparatory phases of legislative procedures.
I propose the idea of attaching modelling systems and simulation platforms to parliamentary activities of lawmaking processes as another example of a FTA technique applied to Law.
I trust that Parliaments would benefit greatly from the use of modelling and simulation techniques aimed at uncovering future societal, economic and environmental trends.
Through the use of modelling instruments, legislators would not only be able to receive relevant information of future societal trends
Modelling techniques would allow legislators and decision makers to test the prospective impacts and consequences of a given change in legislation.
Modelling is, in this sense, a powerful instrument and an important source of information that should be used to improve legislative making processes.
Still within the field of lawmaking, modelling systems could be combined with other FTA METHODS, such as backcasting and future verification procedures.
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.
lawmaking processes would greatly benefit from the use of modelling techniques and other FTA instruments based on ICT procedures.
reflecting on the application of fta tools and methods (such as Delphi surveys, scenario planning, backcasting and modelling techniques) to the legal sphere,
A concrete example of a combination between quantitative and qualitative methods in FTA, namely between scenario and modelling analysis, can be found in the so-called International Futures (IFS.‘‘
global modeling system which acts as a powerful tool for the exploration of the long-term future of closely interacting policy-related issues (including human development, social change and environmental sustainability).
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 use some ideas from cultural historical theory to argue that modelling the directionality of the innovative élan requires analysis of progress at several time scales.
we describe and expand Robert Rosen's analysis of the nature of modelling and the relationships between natural and formal systems.
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.
Downstream innovation and relational monsters The Genesis essentially depicts a linear model of creation where an‘upstream'heroic innovator is the true source of novelty.
In this model, the narrative structure simultaneously generates the key categories of‘creator',‘object of creation'
and‘user'and a directed linear model of impact and causality that makes these categories salient.
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,
as depicted by Rosen (1985,74), 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.
Then we can infer or predict the impact of causality in the natural system by using the rules of inference in 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
I. Tuomi Figure 1. Modelling relation according to Rosen. mathematical models that make predictive statements particularly efficient and allow,
Rosen clarified the modelling relation in considerable theoretical and conceptual RIGOUR. His description, however, leaves somewhat open the question howwe come up with the natural systems in the first place.
a constructivist view on the importance of active human cognition-creating models of the world, a Darwinian terminology of natural selection,
5 we can simply fill in the missing piece of Rosen's depiction of the modelling relation.
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.
Ontological Downloaded by University of Bucharest at 04:52 03 december 2014 Foresight in an unpredictable world 745 Figure 2. Modelling in the context of the phenomenological veil. unpredictability,
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.
In such a situation formal models cannot be made more accurate by collecting more data or measuring the observables more accurately.
Innovation changes the way the natural system itself needs to be constructed. Ontological expansion means that we do need not a better model;
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,
and it will have predictive value only if innovation remains unimportant. For example, data on phone calls or callers could not have been used to predict industry developments
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.
An example here is the problem of formulating‘grand societal challenges'.'Typically, such societal challenges are based on extrapolations of historical trends
'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.
Uncertainty in integrated assessment modelling. Climatic Change 54, no. 1: 75 105. Varela, F. J.,E. Thompson,
A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment 4, no. 1: 5 17.
and uncertainty 759 Direct observation of objective reality People's perceptions of objective Reality Artificial reconstruction of objective Reality Axiomatic Reason/logic/theorems Normative modelling
Descriptive modelling Logical positivist Empiricist Field studies Field experiments Structured interviewing Surveys Prototyping Physical modelling Laboratory experimentation Interpretive Action research Case studies Historical analysis
Delphi Intensive interviewing Expert panels Futures scenarios Conceptual modelling Hermeneutics Critical Theory Introspective reflection Critical systems thinking Rational Existential Natural Artificial Figure 1
or effective way to obtain information about the situation artificial reconstruction of object reality is attempted in almost all modelling
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
improves practices with conceptual modelling; helps to formulate policies and puts them into practice to influence situations in desirable directions.
often by using scenarios and conceptual modelling, that may include prioritising important areas of intervention,
However,‘naïve'the modelling processes used then may have seemed to some people at that time they were brave attempts to draw attention to the existence and nature of global situations.
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.
Underlying convergence in the NBIC frame are matters relating to modelling and simulation which in turn means algorithm construction and computation.
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,
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