Relativism in Environmental Simulation Modelling

Partly as a result of the epistemological problems described in my previous few posts, Keith Beven has forwarded a modelling philosophy that accepts uncertainty and a more relativist perspective. This realist approach demands greater emphasis on pluralism, use of multiple hypotheses, and probabilistic approaches when formulating and parameterising models. When pressed to comment further on his meaning of relativism, Beven highlights the problems of rigidly objective measures of model performance and of ‘observer dependence’ throughout the modelling process;

“Claims of objectivity will often prove to be an illusion under detailed analysis and for general applications of environmental models to real problems and places. Environmental modelling is, therefore, necessarily relativist.”

Beven suggests the sources of relativistic operator dependencies include;

  1. Operator dependence in setting up one or more conceptual model(s) of the system, including subjective choices about system structure and how it is closed for modelling purposes; the processes and boundary conditions it is necessary to include and the ways in which they are represented.
  2. Operator dependence in the choice of feasible values or prior distributions (where possible) for ‘free’ parameters in the process representations, noting that these should be ‘effective’ values that allow for any implicit scale, nonlinearity and heterogeneity effects.
  3. Operator dependence in the characterization of the input data used to drive the model predictions and the uncertainties of the input data in relation to the available measurements and associated scale and heterogeneity effects.
  4. Operator dependence in deciding how a model should be evaluated, including how predicted variables relate to measurements, characterization of measurement error, the choice of one or more performance measures, and the choice of an evaluation period.
  5. Operator dependence in the choice of scenarios for predictions into the future.

The operator dependencies have been highlighted in the past, but have re-emerged in the thoughts of geographers (Demeritt, Brown, O’Sullivan, Lane et al.), environmental scientists (Oxley and Lemon), social scientists (Agar) and philosophers of science (Collins, Winsberg).

Notably, although with reference to experimental physics rather than environmental simulation modelling, Collins identified the problem of the ‘experimenter’s regress’. This problem states that a successful experiment occurs when experimental apparatus is functioning properly – but in novel experiments the proper function of the apparatus can only be confirmed by the success or failure of the experiment. So in situations at the boundaries of established knowledge and theory, not only are hypotheses contested, but so too are the standards and methods by which those hypotheses are confirmed or refuted. As a result, Collins suggests experimentation becomes a ‘skilful practice’ and that experimenters accept results based not on epistemological or methodological grounds, but on a variety of social (e.g. group consensus) and expert (e.g. perceived utility) factors.

This stance is echoed in many respects by Winsberg’s ‘epistemology of simulation’, which suggests simulation is a ‘motley’ practice and has numerous ingredients of which theoretical knowledge is only one. The approximations, idealisations and transformations used by simulation models to confront analytically intractable problems (often in the face of sparse data), need to be justified internally (within the model construction process) on the basis of existing theory, available data, empirical generalisations, and the modeller’s experience of the system and other attempts made to model it.

Similarly, Brown suggests that in the natural sciences uncertainty is rarely viewed as being due to the interaction of social and physical worlds (though Beven’s environmental modelling philosophy outlined above does) and that modellers of physical environmental processes might learn from the social sciences where the process of gaining knowledge is understood to be important for assessing uncertainty.

However, whilst an extreme rationalist perspective prevents validation and useful analysis of the utility of a model, its output, and the resulting knowledge (because of things like affirming the consequent), so too does an extreme relativist stance which understands model and model builder are inseparable. Rather, as Kleindorfer et al. suggest, modellers need to develop the means to increase the credibility of the model such that “meaningful dialogue on a model’s warrantability” can be conducted. How and why this might be achieved will be discussed in future posts.

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