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The system dynamics (SD) modeling approach champions model transparency and facilitates ease of communication about model elements. Transparency—the state or quality of being easily observed and comprehended—is an important attribute of useful models because it enables users to identify and understand the assumptions, relationships, and data used in the model. Model transparency, therefore, is a central element in good modeling practice. SD models, like other types of models, embody scientific theories about how the world works. Specifically, the SD approach calls for the use of dynamic hypotheses as the basis for developing scientific models. We believe that transparency in our models is akin to openness in our scientific research. One way to achieve transparency in our modeling efforts is through the use of thorough documentation processes. As Sterman states, "perhaps the most important pragmatic issue for modelers is documentation" (Sterman, 2000, p. 865). In general, system dynamicists struggle with how to document their models so that other modelers can examine the elements efficiently and effectively, thus increasing confidence in the model's usefulness. To help modelers increase the transparency of their models through enhanced documentation, scientists at Argonne, building on model documentation work by Oliva (2001), developed a tool that enables modelers to create practical, efficient, HTML-based model documentation and provide customizable model assessments. The tool offers extremely quick generation of model documentation and assessment that can be produced concurrently with model development. Facilitating the generation of documentation and diagnostics of models—documenting and discovering "as you go"—makes it possible, as stated by Sterman, to "uncover errors that otherwise might not be detected until much later, preventing costly rework" (2000, p. 865). The SDM-Doc tool is very simple to use. The user needs only to run the application and select a model to be documented (an .MDL file). Once the model is selected, the results are automatically shown (see http://tools.systemdynamics.org/sdm/Handbook-Model-V.html for an example of the SDM-Doc tool output). The following sections describe the documentation output and its potential use to achieve model transparency. Model assessment results are organized into three categories: model information, warnings, and potential omissions (see Figure 1).1 Ideally, well-formulated models will show zero warnings and zero potential omissions (with the exception of supplementary variables). When instances of model information, warnings, or potential omissions are found, the title of the category is hyperlinked to a list of the variables that meet this criterion and are included at the end of the HTML page. In the SDM-Doc tool, the model assessment results displayed are customizable according to the options shown in Figure 2. The tool allows for the creation of different profiles with different options just by changing the profile name. Different profiles might be used for different purposes such as model development, model publishing, or model sharing. When there are variables in these categories, the category name is hyperlinked to a list of variables, which includes a separate list of such variables that is useful for reporting purposes and for model exploration and testing. The third subsection provides information about the simulation controls: time step, time units, and time horizon of the model. The fourth, and final, subsection provides information about model completeness by showing if the model can be simulated, whether groups were defined by the modeler, and whether a packaged version of the model (i.e. a .VPM file) is available so that the tool can extract information about the graphical representation of the views of the model.2 Overly complex variable formulations: equations that have more input variables than a certain predefined threshold. The default value used is three input variables in each equation, an approach established by following Richardson's rule (2000). The rule states that, in order to maximize the level of transparency of equation formulation, an equation should have a maximum of three variables as input. The threshold used by the tool, however, is customizable by the modeler; the higher the threshold, the lower the number of violations to this rule of equation formulation parsimony. A natural tension arises between the quest for overall models parsimony and equation simplicity. This category, as many others, is hyperlinked to a list of the variables that violate the rule, each with complexity score based on the number of variables used as input for the equation. For example, if the complexity score is 10, it means that the equation to compute the variable reported uses 10 variables as input. Overly complex stock formulations: stock variables that have computations other than addition or subtraction in them. Stocks are accumulations of inflows and outflows; therefore, in principle, the only admissible arithmetic operands are addition and subtraction, as depicted in the following integral equation (from Sterman, 2000, p. 194): In some cases, however, to simplify the detail complexity of the model, modelers may choose to embed other computations in the stock variable, making the stock formulation opaque and difficult to follow. All computations should be moved out of the stock variable and into the corresponding rate variables to increase the transparency of the formulation. After the model assessment results, the tool reports the results for types of variables, groups, modules, and views in summary form, as shown in Figure 3. Depending on what grouping criterion is active, the corresponding hyperlinks become active3 (in Figure 3, the active grouping criterion is: by type). The SDM-Doc tool is capable of sorting the model output by each of these four groupings—type, group, module, and view—as well as by variable name and the combined module/group/name sequence. Also available is an option to sort by level structure, which corresponds to the "natural way" to make sense of a model and matches the order proposed by the original DYNAMO documentor (see Richardson and Pugh, 1981, pp. 214–229, for details).4 In addition, a view summary (i.e., a table of variable usage in model views) is created at the end of the HTML file to visualize the distribution of variables included in model views (see Figure 4 for a partial example). Each equation is displayed showing what module, group, and type it belongs to (see Figure 5) and all its associated information: name, units, range (when defined), formula, description, views in which the variable is used, whether source information has been defined for it, and what other variables in the model use it. In the case of a lookup function, the description includes the graphical representation of the tabular data, as shown in Figure 6. This type of model documentation allows users to efficiently navigate model structure by simply clicking on the variable names on the right-hand side of the equations or by clicking on the links to the variables that use this variable as input ("used by"). In this way, model transparency is achieved because all details of the formulation can be quickly examined, even in the case of very large models. In addition, in the by-view grouping (see Figure 7), when a user is given the opportunity to see a diagram of model structure,5 followed by the equations that belong to that part of the structure, as suggested by Sterman (2000, p. 856), an important cognitive process is enabled, allowing for a thorough inspection of the model using all available sources of information. While the SDM-Doc development at Argonne started as a way to easily navigate model structure (providing hyperlinks to equation components and other features), it has evolved to automate some basic model tests. The tool now also provides a powerful overview of model structure, leading to enhanced model clarity and transparency. Judging models based solely on inputs and outputs is not enough to promote deep understanding of complex systems. The idea behind the development of the tool was to help modelers move from opaque models—"black-box" models—to as close as possible to completely transparent models—"glass-box" models. Although we may never be able to measure model transparency in an absolute way, and although not every modeler and decision maker will necessarily measure, or know how to value, improved model transparency, we believe that enhanced documentation and assessment practices will result in increased transparency in models, and that increased model transparency will improve model quality and usefulness. In addition, as model transparency increases, the speed with which model users can understand the model structure and use the results will increase, and models will become more objective (than their opaque alternatives), allowing comparison with other models and possibly leading to their use for further development and understanding (i.e., as benchmarks and/or building blocks). Increased transparency enables model builders to develop explicit, common definitions of model constructs—leading to quality peer review of models, increased trust and confidence in model results, and an improved ability to communicate the importance of modeling results. Increased model transparency also enhances the clarity of model assumptions, traceability of relationships among model variables, understanding of data used in the model (its sources, boundaries, and uses—parametrization and calibration), understanding of model components at different levels (e.g., equation, view, group), and understanding of the model as a whole. As models become more transparent, model builders and the decision makers who use the models are able to better understand the components of the models, resulting in an increased understanding of the structure of the problems they face and in a lower unbounded (i.e., "blind faith") reliance on models and their output. Finally, increased model transparency also leads to a higher level of modeler accountability and helps remove barriers to understanding and reproducing model results, potentially leading to enhanced insights, model reusability, and adaptations and extensions of models to address related issues. Model transparency, then, is not only about being able to see inside our models as through a glass box, but also about how the inside of that glass box is organized, making what is behind the glass more easily comprehensible. The SDM-Doc reports on model components and complexity (e.g., variables, number of stocks, time units) and tests the accuracy of model documentation (e.g., variable usage, warnings, potential omissions, equation documentation, simulatability). By automating these tests, we believe the tool will help the modeling community improve its internal documentation standards, leading to an improved model development process. By providing the overview description of the model size and complexity, as well as supporting multiple perspectives for understanding the model, we believe the tool provides important support for model development, analysis, and eventual understanding. We hope to be able to continue expanding the model assessment capability as additional tests are identified and vetted by the community, and are delighted to provide the tool as open source and encourage other groups to develop the functionality for other system dynamics modeling platforms.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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