This practice is often justified as being consistent with Forrester's emphasis on the importance of qualitative data. It is, however, a serious error. Ignoring numerical data and failing to use statistical tools when appropriate increases the chance that the insights derived from a model will be wrong or harmful. Rigorously defining constructs, attempting to measure them, and using the most appropriate methods to estimate their magnitudes are important antidotes to casual empiricism, muddled formulations, and the erroneous insights we often draw from our mental models.
Less widely cited is his caution that: These comments are not to discourage the proper use of the data that are available nor the making of measurements that are shown to be justified … Lord Kelvin's famed quotation, that we do not really understand until we can measure, still stands. ID, p. The quotation from Lord Kelvin the physicist William Thomson to which Jay refers is: a first essential step in the direction of learning about any subject is to find principles of numerical reckoning and methods for practicably measuring some quantity connected with it Thomson, , p.
As an engineer ever focused on practicalities, Forrester then adds an important note: But before we measure, we should name the quantity, select a scale of measurement, and in the interests of efficiency we should have a reason for wanting to know. To develop reliable knowledge, we must bring the best available data to bear for the problem we seek to address.
Following Jay's advice, we must find a way to measure what we need to know. We should not accept the availability of numerical data as given, outside the boundaries of our project or research. Once we recognize the importance of a concept, we can almost always find ways to measure it. Today many apparently soft variables such as customer perceptions of quality, employee morale, investor optimism, and political values are routinely quantified with tools such as surveys, conjoint analysis and content and sentiment analysis.
Of course, all measurements are imperfect.
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Often the greatest benefit of a modeling project is to identify the importance of and then measure important concepts previously ignored or unquantified. To illustrate, consider Homer's , model of the diffusion of new medical technologies. The model has a broad boundary and significant structure far beyond the classic Bass diffusion model Bass, ; Sterman, , ch. In each case Homer sought the widest range of data, using triangulation to help specify the model and estimate parameters and relationships.
For the pacemaker, this meant extensive interviews with clinicians, researchers, and representatives of pacing companies, including many pacing pioneers. Homer even attended two pacemaker implantation procedures, donning the required lead vest to avoid fluoroscope radiation exposure. The pacemaker was and remains one of the most successful medical innovations, expanding from the first implantation in to more than , per year by in the U. Growth was steady and smooth Figure 8.
None of the experts Homer interviewed suggested anything else. Homer, however, believed it was necessary to find out. At that time the early s there was no Internet or electronic databases to automate the search of medical journals. To gather the data, Homer spent days in the Harvard Medical School library stacks, examining, by hand, every issue of the relevant cardiology journals from through The work was painstaking and slow.
Contrary to intuition and what might be expected from the fact that none of the experts Homer interviewed suggested anything else, evaluations did not exhibit smooth growth. Homer found an excellent fit to the data Figure The oscillations in evaluations Homer found are not merely a curiosity but have significant policy implications. During such times, clinicians would be flying blind, perhaps being too aggressive and placing at risk certain new patients who would benefit little; or perhaps being too conservative and failing use pacing for others who could benefit substantially.
Policy analysis with the model led Homer to endorse the use of large clinical registries for new medical technologies as a supplement to randomized clinical trials and evaluative studies. Registries, typically overseen by federal agencies such as the National Institutes of Health, require clinicians to continuously report outcomes for their patients.
Registries generate a steady flow of information and shorten the delay in the evaluation process, thus keeping up with changes in the technology and its application. They are widely used today, having proven helpful in the early identification of harmful side effects and unexpected benefits for certain patient subsets. Instead, his careful empirical work revealed a surprising phenomenon with important implications see also Homer, When Forrester first formulated principles for system dynamics practice, numerical data were scarce and many of the tools described above for qualitative data elicitation and quantified measurement did not exist.
Furthermore, the tools for statistical estimation of parameters were not well developed compared to today and often inappropriate for use in complex dynamic systems. Given the limitations of computing at the time, multiple linear regression was the most widely used econometric tool. However, linear regression and related methods e. These include perfect specification of the model, no feedback between the dependent and independent variables, no correlations among the independent variables, no measurement error, and error terms that are i.
And, of course, classical multiple regression and similar statistical methods only revealed correlations among variables and could not identify the genuine causal relationships modelers sought to capture. Given these limitations, Jay and other early modelers, including economists, sociologists and others, faced a dilemma: they could estimate parameters and relationships in their models using econometric methods and provide quantitative measures of model fit to the data, but at the cost of constraining the structure of their models to the few constructs for which numerical data existed and using estimation methods that imposed dubious assumptions; or they could use qualitative and quantitative data to build models with broad boundaries, important feedbacks, and nonlinearities, at the cost of estimating parameters by judgment and expert opinion when statistical methods were impossible or inappropriate.
Forrester and early system dynamics practitioners opted for the latter. Much controversy in the early years surrounded the different paths modelers trained in different traditions chose when faced with these strong tradeoffs. Today, data have never been more abundant.
Methods to reliably measure previously unquantified concepts, unavailable to Jay, are now essential. Rigorous data collection, both qualitative and quantitative, opens up new opportunities for formal estimation of model parameters, relationships and structure, to assess the ability of models to replicate the historical data, and to characterize the sensitivity of results to uncertainty, providing more reliable and useful insights and policy recommendations. Rahmandad et al. System dynamics modelers have long sought methods to test models and assess their ability to replicate historical data e.
But system dynamics modelers were not the only ones concerned with the limitations of early statistical methods. Engineers, statisticians and econometricians worked to develop new methods to overcome the limitations of traditional methods.
Faster and more capable computing spurred the development of methods to estimate parameters in nonlinear systems, avoid getting stuck on local optima in parameter space, deal with autocorrelation and heteroscedasticity, and especially model misspecification and causal identification see below.
These methods include indirect inference Gourieroux et al. Bootstrapping and subsampling see, e. Methods for sensitivity analysis that were difficult or impossible to use when Forrester first formulated system dynamics can now be run quickly even on large models. These methods include standard multivariate Monte Carlo, Latin hypercube, and others. Methods for formal analysis of model behavior have also been developed so that understanding the origin of the behavior in a complex model is no longer a matter of intuition or trial and error Mojtahedzadeh et al. New methods for policy optimization have also been developed see Rahmandad et al.
System dynamics models seek to capture the causal structure of a system because they are used to explore counterfactuals such as the response of a system to new policies. Distinguishing genuine causal relationships from mere correlations remains a critical issue. However, in many settings, particularly in the human and social systems where system dynamics is often used, RCTs and experiments are often prohibitively expensive, time consuming, unethical or simply impossible. We have only one Earth and cannot compare our warming world to a control world in which fossil fuels were never used; we cannot randomly assign half the citizens of the U.
Nevertheless, there has been dramatic progress in tools and methods to carry out rigorous RCTs in social systems. Many RCTs in social systems today are carried out in difficult circumstances and provide important insights into policies to reduce poverty and improve human welfare see, e. System dynamics has long used experimental methods Sterman, , , and experimental research in dynamic systems is robust e. Many of these studies involve individuals or small groups and have provided important insights into how people understand, perform in and learn from experience in dynamic systems.
Despite progress in methods for and the scope of experimental methods, experimentation remains impossible in many important contexts. Absent rigorous RCTs, researchers often seek to estimate causal structure and relationships via econometric methods. Although we cannot randomly assign murderers to execution versus prison to assess the deterrent effect of capital punishment, we might estimate the effect by comparing states or countries where the death penalty is legal to those where it is not.
Such studies are extremely common, typically using panel data and specifying regression models using fixed effects to control for those differences deemed to be important.
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The hope is that controlling for these differences allows the true impact of capital punishment on the murder rate or any other effect of interest to be estimated. The problem is that one cannot measure or even enumerate all the possible differences among the states that could potentially influence the murder rate. If any factors that affect the murder rate are omitted, the results will be biased, particularly if, as is common, the omitted factors are correlated with those factors that are included omitted variable bias , and especially if a state's decision to use the death penalty was affected by any of these conditions, including the murder rate itself endogeneity bias.
These are more than theoretical concerns. The results challenged the econometric community to take identification seriously: if econometric methods cannot identify causal relationships then the results of such models cannot provide reliable advice to policymakers.
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These methods include natural experiments e. Robust methods for parameter estimation, identification, assessment of goodness of fit, and parametric and structural sensitivity analysis are now available, not only in econometrics but also in engineering, artificial intelligence and machine learning e. Pearl, ; Pearl and Mackenzie, Abdelbari and Shafi, , offer an application in system dynamics.
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Following Jay's example, system dynamics modelers should master the state of the art and use these tools, follow new developments as the tools continue to evolve, and innovate to develop new methods appropriate for the models we build. These detailed data sources provide far greater temporal and spatial resolution, generating insight into empirical issues relevant to important questions.
For example, Rydzak and Monus , this issue provide detailed empirical evidence on networks of collaboration among workers from different departments in industrial facilities and model how these evolved over time to explain why one facility was successful in improving maintenance and reliability while another struggled. In the social realm, the integration of big data sets describing population density, the location of homes, schools, businesses and other buildings, road networks, social media and cell phone activity provide the granular data needed to specify patterns of commuting, communication, travel and social interactions.
Big data also enable important theoretical developments in dynamics. Consider two examples. In contrast, Li et al. In that spirit, it seems necessary now to ask why system dynamics modelers should learn and use the modeling, empirical and analytic methods described above.
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While the best work in the field follows the procedures outlined above, some argue that the methods described above are neither necessary nor helpful. Many descriptions of the system dynamics method fail to note or encourage the use of new methods to enhance the rigor, reliability, relevance and impact of dynamic modeling.
Many continue to hold fast to tools and methods for system dynamics practice dating to the s even though better choices now exist and some old methods and tools are now neither effective nor acceptable for research or practice. Some fail to emphasize, use, or teach basic principles to gather evidence, test hypotheses and build confidence in models. The System Dynamics approach involves: Defining problems dynamically, in terms of graphs over time.
www.maquinarias-reunidas.com/libraries/known/grid.php Striving for an endogenous, behavioral view of the significant dynamics of a system, a focus inward on the characteristics of a system that themselves generate or exacerbate the perceived problem.