A simulation model is an internally logically-consistent theory of how a system functions. Simulation models are currently recognised by environmental scientists as powerful tools, but the ways in which these tools should be used, the questions they should be used to examine, and the ways in which they can be ‘validated’ are still much debated. Whether a model aims to represent an ‘open’ or ‘closed’ systems has implications for the process of validation.
Issues of validation and model assessment are largely absent in discussions of abstract models that purport to represent the fundamental underlying processes of ‘real world’ phenomena such as wildfire, social preferences and human intelligence. These ‘metaphor models’ do not require empirical validation in the sense that environmental and earth systems modellers use it, as the very formulation of the system of study ensures it is ‘closed’. That is, the system the model examines is logically self-contained and uninfluenced by, nor interactive with, outside statements or phenomena. The modellers do not claim to know much about the real world system which their model is purported to represent, and do not claim their model is the best representation of it. Rather, the modelled system is related to the empirical phenomena via ‘rich analogy’ and investigators aim to elucidate the essential system properties that emerge from the simplest model structure and starting conditions.
In contrast to these virtual, logically closed systems, empirically observed systems in the real world are ‘open’. That is, they are in a state of disequilibrium with flows of mass and energy both into and out of them. Examples in environmental systems are flows of water and sediment into and out of watersheds and flows of energy into (via photosynthesis) and out of (via respiration and movement) ecological systems. Real world systems containing humans and human activity are open not only in terms of conservation of energy and mass, but also in terms of information, meaning and value. Political, economic, social, cultural and scientific flows of information across the boundaries of the system cause changes in the meanings, values and states of the processes, patterns and entities of each of the above social structures and knowledge systems. Thus, system behaviour is open to modification by events and phenomena outside the system of study.
Alongside being ‘open’, these systems are also ‘middle-numbered’. Middle-numbered systems differ from small-numbered systems (controlled situations with few interacting components, e.g. two billiard balls colliding) that can be described and studied well using Cartesian methods, and large-numbered systems (many, many interacting components, e.g. air molecules in a room) that can be described and studied using techniques from statistical physics. Rather, middle-numbered systems have many components, the nature of interactions between which is not homogenous and is often dictated or influenced by the condition of other variables, themselves changing (and potentially distant) in time and space. Such a situation might be termed complex (though many perspectives on complexity exist). Systems at the landscape scale in the real world are complex and middle-numbered. They exist in a unique time and place. In these systems history and location are important and their study is necessarily a <a href="http://dx.doi.org/10.1130/0016-7606(1995)1072.3.CO;2″ target=”_blank” class=”regular”>‘historical science’ that recognises the difficulty of analysing unique events scientifically through formal, laboratory-type testing and the hypothetico-deductive method. Most real-world systems possess these properties, and coupled human-environment systems are a prime example.
Traditionally laboratory science has attempted to isolate real world systems such that they become closed and amenable to the hypothetico-eductive method. The hypothetico-deductive method is based upon logical prediction of phenomena independent of time and place and is therefore useful for generating knowledge about logically, energetically and materially ‘closed’ systems. However, the ‘open’ nature of many real-world, environmental systems (which cannot be taken into the laboratory and instead must be studies in situ) is such that the hypothetico-deductive method is often problematic to implement in order to generate knowledge about environmental systems from simulation models. Any conclusions draw using the hypothetico-deductive method for open systems using a simulation model will implicitly be about the model rather than the open system it represents. Validation has also frequently been used, incorrectly, as synonymous with demonstrating that the model is a truly accurate representation of the real world. By contrast, validation in the discussion presented in this series of blog posts refers to the process by which a model constructed to represent a real-world system has been shown to represent that system well enough to serve that model’s intended purpose. That is, validation is taken to mean the establishment of model legitimacy – usually of arguments and methods.
In the next few posts I’ll examine the rise of (critical) realist philosophies in the environmental sciences and environmental modelling and will explore the philosophy underlying these problems of model validation in more detail.