System Dynamics Models: using System Dynamics Models


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System Dynamics Models: using System Dynamics Models

  1. 1. Small System Dynamics Models for Big Issues Triple Jump towards Real-World Dynamic Complexity Erik Pruyt | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |$| | | First time readers: start with the preface | |
  2. 2. Chapter 17 Using System Dynamics Models ‘Models could be used to develop social visions far more internally consistent than those generated by mental models alone. They could point the way to critical, decisive experiments, and actively test social theories at far less cost than the costs of imposing those theories in ignorance and arrogance upon the whole society. They could be used to search for imprecise policies that are robust against uncertainties rather than precise policies that try to optimise something that is not understood. Perhaps most important, they could simply serve as communication devices in which different, partial, mental models of the social system could be expressed and integrated.’ (Meadows and Robinson 1985, p429) 17.1 Model/Modeling Uses There are many uses of SD models/modeling – to name a few: • To surface assumptions and express/elicit/merge mental models (e.g. using Group Model Building); • To communicate mental/formal models; • To analyze & understand the link between structures & behaviors; • To develop internally consistent social visions or scenarios; • To test (social) theories; • To generate/imagine plausible futures and explore uncertainties, risks and opportunities; • To experiment in a ‘virtual laboratory’; • To design policies that improve system behavior; • To test the robustness of policies, i.e. their effectiveness under deep uncertainty; • To train/teach/learn/experience (e.g. using flight simulators or multi-player games). Below we will focus on one model use that is particularly important for policy analysts: to design policies that improve system behavior. 209
  3. 3. JUMP: Using SD Models c⃝ 2013 by Erik Pruyt 17.2 Policy Analysis, Policy Design, and Policy Testing Policy Analysis and Policy Design In this e-book we follow the intuitive/iterative/interactive approach of policy analysis, design and testing. Experienced modelers can often intuitively distill appropriate structural policies from the structure of a model, from playing with the model, and from performing sensitivity analyses. Following structural changes –in decreasing order of effectiveness– were already suggested: 1. adding/breaking/changing (information-based) feedback loops, decision routines, boundaries of systems and responsibilities,. . . 2. adding/breaking/changing (physical) stock-flow structures; 3. strengthening/weakening existing feedback loops and/or flow variables; 4. adding/eliminating delays/smoothing; 5. changing high leverage policy parameters, i.e. parameters that can be controlled by those involved and that have large effects for relatively small changes. The latter can be identified with sensitivity analysis (see chapter 13). Mathematical and control engineering methods may also be useful1 . For example, for a second order system of ordinary differential equation, one could calculate the eigenvalues of the system, i.e. the roots of the characteristic equation, and derive the behavior. If the eigenvalues/roots are real and both are negative, then the behavior is asymptotically stable. If the eigenvalues/roots are real and one or both are positive, then the behavior is unstable. If the eigenvalues/roots are complex and the real part is negative, then the behavior is asymptotically stable, e.g. damped oscillation. If the eigenvalues/roots are complex and the real part is 0, then the behavior is stable, e.g. oscillation with a constant amplitude. If the eigenvalues/roots are complex and the real part is positive, then the behavior is unstable, e.g. oscillation with increasing amplitude. There are also more advanced analytical methods, statistical screening techniques, techniques to identify dominant loops and shifts in dominance, machine learning techniques, and advanced optimization techniques that can be extremely useful for policy design. These techniques are beyond the scope and level of this introductory case book. They will be dealt with in the follow- up book on ESDMA. Policy Testing After identifying high leverage interventions and building the corresponding policies into the model, one can test and compare policies. It is good practice to build in policies such that they can be switched on/off, and to use different run names for different policy runs so that the dynamics generated by these different policies can be compared. First test policies on runs that require improvement. If they are effective, then also test them on other runs. Are they effective across all plausible runs considered or do they deteriorate some of the runs that did not require improvement? If they are effective across all plausible runs, then they are said to be robust and one may consider implementing them as base policies, that is, from the start on and irrespective of the conditions of the system. Else, one may consider implementing them as adaptive policies, i.e. policies that are activated only when required by the evolution of the real system. Adaptive closed- loop policies are more powerful than closed-loop policies implemented as base policies (Hamarat et al. 2013). However, adaptive closed-loop policies are not easily designed, nor included in models, nor implemented in reality. This topic will be dealt with in the follow-up e-book too. 1See also the additional Math appendix. | | | | | | | | | | | | | | | | | | | | | | |STOP | 210 | | | | | | | | | | | | | | |$| | |
  4. 4. c⃝ 2013 by Erik Pruyt JUMP: Using SD Models 17.3 Interpretation Most models contain many assumptions, aggregations, simplifications, uncertainties, roughly estimated parameters and initial values. Hence, a model instantiation (i.e. one combination of assumptions, aggregations, simplifications, uncertainties, roughly estimated parameters and initial values) will always differ from reality, and so will the behavior generated with the model, and the effects of policies. Hence, there is always a need for extensive sensitivity analysis, uncertainty analysis, and testing of policy robustness, i.e. testing whether policies are effective over the entire plausible uncertainty space and whether they work especially when they really need to work. SD, in combination with regret analysis, is very useful for testing whether policies and recommendations are robust. Model outcomes also need to be interpreted. In SD, outcomes are never interpreted as point or trajectory predictions – they are mostly interpreted as plausible modes of behaviors. It is extremely important to keep that in mind when communicating model behaviors or recommendations based on SD modeling. And ‘never say the model says’ (Barabba 1994). Remember: models are but tools for thought. SD modeling thus requires a lot of reflection beyond the model, the behavior patterns, and the capacity to improve behavior patterns. Since SD is a structural systems approach biased towards structural and consensual solutions, reflection beyond the model and the SD method is needed, especially towards nonstructural and non-consensual solutions. Reflection beyond the model is also necessary for other reasons: e.g. in spite of the fact that a model may show some side effects, it will only really do so if the corresponding subsystems, elements and causal effects are included in the model. Although setting broad boundaries and simulating over long time horizons helps to look at side effects and intertemporal effects, one should nevertheless reflect consciously about side effects and ethical implications, whether they are included in the model or not. Additional (non-mandatory) chapter: VII. Model Use Ap. Math for SD | | | | | | | | | | | | | | | | | | | | | | |STOP | 211 | | | | | | | | | | | | | | |$| | |
  5. 5. Flexible E-Book for Blended Learning with Online Materials Although this e-book is first and foremost an electronic case book, it is much more than just a set of case descriptions: it is the backbone of an online blended-learning approach. It consists of 6 concise theory chapters, short theory videos, 6 chapters with about 90 modeling exercises and cases, many demo and feedback videos, feedback sheets for each case, 5 overall chapters with feedback, 5 chapters with multiple choice questions (with graphs or figures), hundreds of online multiple choice questions, links to on-site lectures, past exams, models, online simulators, 126 slots for new exercises and cases, and additional materials for lecturers (slides, exams, new cases). The fully hyperlinked e-version allows students (or anybody else for that matter) to learn –in a relatively short time– how to build SD models of dynamically complex issues, simulate and analyze them, and use them to design adaptive policies and test their robustness. ISBN paperback version: ISBN e-book version: