There are many types of quantitative systems models and hence many classifications of them. Here we present a few common terms and classifications that are used in the literature to label the type of methods that we will focus on in this chapter. We concentrate on computer models that aim to provide new insights into future states of fairly complex systems. Examples will be drawn from models that represent complex natural resource management (NRM) systems, where the authors have particular experience.

For studies analysing future states of systems, three rather different terms can be used (van Asselt et al. 2010): forecasting (analysing the likely 'surprise-free' futures, that is futures that are plausible and that logically follow from past and present trends); foresight (analysing different 'possible' futures); and normative future explorations (exploring different 'desired' futures). Forecasting and foresight studies (see Chapter 3, this volume) can also be labelled as, respectively, 'projective' and 'predictive studies'; that is, they try to model the actual, likely or probable evolution of systems, taking the objectives of actors as being more or less implicit. Normative approaches, on the other hand, try to find ('explore') the optimal, desired or alternative solutions to a given problem by keeping the objectives explicit. Predictive (in economic literature also often called positive) studies are generally more policy-oriented: they take system properties, including the human behaviour component, as a given and try to 'predict' the future state(s) of the system in response to alternative policies. Often, explorative or normative future studies are more resource-oriented: they analyse possible futures based on availability and limitations of (natural) resources, while assuming certain objectives of agents and optimum behaviour to realize such objectives.

Today, many models are used for the purpose of so-called integrated assessment and/or in the context of the impact assessment of policies (see Chapter 9, this volume). Here, we refer to integrated assessment as a research process, while we use impact assessment to refer to the political process of assessing the expected impact of new policies or technologies (Adelle et al. 2012). Integrated assessment has been defined as 'an interdisciplinary and participatory research process combining, interpreting and communicating knowledge from diverse scientific disciplines to allow a better understanding of complex phenomena' (Rotmans and van Asselt 1996, p. 327). Integrated assessment and modelling (IAM) has been proposed as a means of enhancing the management of complex systems and to improve integrated assessment (Parson 1995; Harris 2002; Parker et al. 2002). It is based on systems analysis as a way to consider, in a more holistic fashion, the biophysical, economic, social and institutional aspects of a system under study. The term is used for models that consider biophysical and socio-economic aspects and have multi-level capabilities, for instance analysis at regional, farm and field level. The assumption underlying IAM is that computerized tools contribute to better informed ex ante impact assessments of new policies and technologies, as for instance employed by the European Commission since 2003 in the EU's policy formulation process (EC 2005).

Models that aim to contribute to the impact assessment of policies need to have some predictive capacity, that is, they must be able to predict likely systems changes as a result of policy changes, and must therefore allow modelling of the responses of actors. So actor behaviour must somehow be captured in the models. In contrast, more explorative and normative models address system responses or optimum configurations with more 'what-if type questions and scenarios coming to the fore. For example, how would the system change or what would be an optimum system configuration assuming a certain objective (or prioritization of objectives) of actors? The quality of these studies is not measured in terms of the likelihood that the outcomes of the models will actually happen, but rather in showing the ultimate consequences of different priorities or choices. Crucially, they can help to reveal trade-offs between conflicting objectives. The terms predictive and explorative can be further explained and defined

Typical model-based future studies as classified by the degree of future uncertainty and the causality in the model

Figure 5.1 Typical model-based future studies as classified by the degree of future uncertainty and the causality in the model

in a classification that relates future studies to systems models. It employs four classes based on two criteria (Figure 5.1). The first criterion is the level of uncertainty, with respect to assessing future values of system parameters and exogenous factors, for example in relation to land use, population growth, trade and market developments. Usually, the longer the time horizon of a study, the higher the level of uncertainty in these factors. It is here that a scenario approach (see Chapter 3, this volume) might be useful. The effects of making specific estimates for exogenous variables (for example, population growth) may be revealed in scenarios. The whole set of scenarios should represent the extremes of possible values for the uncertain parameters. The second criterion is the level of causality in the model of a given system, used to forecast possible future states. The level of causality is reflected in the type of model that is used for the study. Models may have a strong statistical/descriptive basis or a more mechanistic/explanatory basis with information on causes of certain developments. In more mechanistic models, behaviour or possible behaviour of a system at a higher level is explained completely by characteristics of components at lower hierarchical levels. Regional and farming systems are often too complex to model mechanistically. However, it may well be possible to model certain aspects of the systems, for example the biophysical aspects, and make explicit assumptions about others, for example the socio-economic aspects, in a scenario analysis.

These two criteria classify model-based future studies into four categories (Figure 5.1). Projections are based on a low level of causality in the model employed and in fact are only useful under low levels of uncertainty. If more information on causality and relations behind a projection is available, projections may gradually evolve into predictions. The distinction between projections and predictions is a matter of judgement, but a prediction claims a certain degree of predictability of the described developments, whereas a projection merely transplants current knowledge and information into the future (van Latesteijn 1995). In both, extrapolations of past and current trends are used and system performance is used as an input. Use is often made of actual and historical data of an empirical and statistical nature. Predictive and projective studies are generally done for the short term (less than 10 years). If the level of uncertainty increases, a projection might evolve into a speculation and, if more information is available on how different processes and developments are related, a speculation changes into an exploration of the future (see also Chapter 3, Figure 3.1, this volume). Explorations show options for future developments given explicit assumptions about uncertain developments. They usually concern strategic (occurring over >10 years) issues.

In the terminology used by van Asselt et al. (2010), that is, forecasting, foresight and normative future studies, forecasting comes close to projections, foresights are close to predictions and normative future studies generally belong to the class of explorations. However, van Asselt et al. also use the word 'explore' to describe forecasting and foresight, illustrating the ambiguity evident in both the literature and daily practice when it comes to classifying and describing future studies using computer models.

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