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# System Dynamics Models: MCQs IV

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### Transcript of "System Dynamics Models: MCQs IV"

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 15 MCQs Part IV ‘Cessante causa cessat eﬀectus.’ Sallustius Which of the following statements are right and which are wrong? 1. The result of a sensitivity analysis includes information on the response of the model to a large number of small changes to uncertain parameters. 2. The only real goal of sensitivity analysis is to ﬁnd parameters with high leverage. 3. The most valid SD model is the one which produces better point predictions. 4. The choice of integration method and step size always needs to be checked. 5. Any SD model that accurately replicates historical data is always valid. 6. A system that is not sensitive to perturbations by exogenous variables, is in equilibrium. 7. A model that is not sensitive to small parameter changes is better than a model that is sensitive to small parameter changes. 8. SD models are almost always numerically sensitive to parameter changes, but that is not surprising and is not of much interest to system dynamicists. 9. One should also test the sensitivity of SD models to changes in equations of soft variables, table functions, structures and boundaries. 10. If a variable cannot be inﬂuenced by the decision-maker (that is, if it is not a policy lever), then behavior mode sensitivity to changes in that variable is desirable. 11. Sensitivity analysis could be used to study whether changes in parameters and structures lead to changes in modes of behavior and/or (relative) performance of policies. 12. SD validation is really all about checking whether SD models provide the right output behaviors for the right reasons. 13. Direct structure and structure-oriented behavior tests are used to ﬁnd erroneous behavior. 14. If your software shows no errors regarding units, this means your dimensional analysis is completed and the model is good. 15. SD model testing should be designed to prove models are right, which makes falsiﬁcation possible and, hence, adds to the credibility of models and modeler. 190
3. 3. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Multiple Choice Question 1 Consider the model about the exodus of a social housing district due to the social cohesion (exercise 14.14 on page 182) displayed above. Simulation of the SD model leads –even for very diﬀerent values of the parameters in orange (see 100 very diﬀerent runs in the graph)– to inverse S-shaped behavior of the original population. Which of the following aggregated CLDs is the best diagram to communicate the essential link between structure and behavior of this model? (a) (b) (c) (d) | | | | | | | | | | | | | | | | | | | | | | |STOP | 191 | | | | | | | | | | | | | | |\$| | |
4. 4. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt Multiple Choice Question 2 Given are two technology transition models. Suppose that all parameters and initial values > 0. Which of the following behaviors ﬁt the variable ‘sustainable technology’ of models (a) & (b)? (a) (b) a. (1) ﬁts model (a); (2) ﬁts model (b). b. (2) ﬁts model (a); (1) ﬁts model (b). c. (3) ﬁts model (a); (4) ﬁts model (b). d. (4) ﬁts model (a); (3) ﬁts model (b). Multiple Choice Question 3 Consider the symbolic representation of a submodel of a larger blueﬁn tuna ﬁsheries model. Which of the following equations could describe its behavior over time? a. dT dt = ( r + g − eS − T O /l ) ∗ T b. dT = ( r + g − eS − T O /l ) ∗ T c. T = ( r + g − eS − T O /l ) ∗ T ∗ t d. −dT dt = − ( r ∗ g + eS − T O /l ) ∗ T | | | | | | | | | | | | | | | | | | | | | | |STOP | 192 | | | | | | | | | | | | | | |\$| | |
5. 5. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Multiple Choice Question 4 (a) tons of tuna (b) tuna ships The trajectories in the graphs are obtained by testing the sensitivity of the blueﬁn tuna model with the existing policy to changes in parameters and uncertain functions. What could be concluded? a. The model is both numerically sensitive and policy sensitive b. The model is behavior mode sensitive, but not numerically sensitive c. The model is numerically sensitive, but not behavior mode sensitive d. The model is numerically sensitive, but not policy sensitive Multiple Choice Question 5 Suppose that you are hired by the RIVM (the Dutch environmental agency) to model the epidemic outbreak of a new ﬂu strand (actually just discovered). The RIVM estimates it will take about 6 to 9 months to develop and mass-produce a new vaccine. The simulation runs of the infected fraction give an indication of the sensitivity of the model to small changes in the variable average contact rate. Which of the following statements is not correct? a. The rather diﬀerent behaviors of the infected fraction for slightly diﬀerent contact rates indicate that the contact rate may be exploited as a policy until a vaccine has been developed. | | | | | | | | | | | | | | | | | | | | | | |STOP | 193 | | | | | | | | | | | | | | |\$| | |
6. 6. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt b. The model is numerically and policy sensitive to changes in the average contact rate. c. The model is not behavior mode sensitive to changes in the average contact rate because there are always two ﬂu peaks and the epidemic is always over after 21 months. d. None of the statements above is correct. Multiple Choice Question 6 Consider the simulation model below concerning a manufacturing company you may have made. Each ﬁnal product assembled in the assembly line requires 2 components from the component line. Simulation of this model generates the behavior displayed above. Which of the statements below concerning the model and the corresponding behavior is correct? a. Although the structure and speciﬁcation of the model are wrong, the behavior generated with it are plausible and realistic. b. This behavior is impossible. It must be caused by a numeric integration error, i.e. an inadequate combination of integration method and time step. c. The model was modeled in a non-protective manner in view of learning from it and designing policies to prevent undesirable (and impossible) dynamics from happening. d. None of the statements above is correct | | | | | | | | | | | | | | | | | | | | | | |STOP | 194 | | | | | | | | | | | | | | |\$| | |
7. 7. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Multiple Choice Question 7 Small variations in two parameters –the ‘immigration time’ (the time necessary to attract new immigrants) and the ‘construction time’ (the time necessary to develop new real estate)– in a SD simulation model concerning the possible real estate bust in Dubai gives the graphs above. The (relatively small) shock in month 10 corresponds to the announcement of the suspension of payments by Dubai World. Both the ‘immigration time’ and the ‘construction time’ are to some extent policy variables. The number of ‘immigrants’ for immigration times of 1, 2, and 3 months are displayed in the left hand side graph. The number of ‘immigrants’ for construction times of 1, 2, 3, 4, and 5 months is displayed in the right hand side graph (note the detailed graph in the frame). The immigration time could be inﬂuenced by policy makers. What could be concluded from this sensitivity analysis? a. For the changes above, the number of ‘immigrants’ in the model is numerically sensitive, but not behaviorally nor policy sensitive. b. For the changes above, the number of ‘immigrants’ in the model is numerically and behav- iorally sensitive, but not policy sensitive. c. For the changes above, the number of ‘immigrants’ in the model is numerically and behav- iorally and policy sensitive. d. None of the previous answers is correct. Multiple Choice Question 8 Which of the following statements about validation is wrong? a. System Dynamics validation is about checking whether a System Dynamics model provides the right output behavior for the right reasons. b. Direct structure tests are used to ﬁnd erroneous structures, not erroneous behaviors. And structure-oriented behavior tests are used to ﬁnd erroneous behaviors, not structures. c. Sensitivity analysis could be used to study whether small changes in parameters and struc- tures lead to changes in modes of behavior and/or policies. d. A System Dynamics model that replicates historical data is not necessarily valid. | | | | | | | | | | | | | | | | | | | | | | |STOP | 195 | | | | | | | | | | | | | | |\$| | |
8. 8. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt Multiple Choice Question 9 The graph displayed above shows the behavior of a model about the sudden illegalization of the supply of prostitution (Demand for prostitution (green), supply of prostitution (red), and the price (blue)). Which of the following statements regarding the formulation of this policy is correct? The stock of prostitutes can be emptied overnight in this model with: a. a pulse function b. a pulse train function c. a step function d. a reset function Multiple Choice Question 10 Consider the ﬂu model comprising a seasonal form of immunity (the green part in the ﬁgure dis- played above). The ‘normal immune population fraction’ ﬂuctuates over the course of the year. Suppose that you need to write an equation for the ﬂow variable ‘susceptible to immune popu- lation ﬂow’. Which of the following formulations is most appropriate for this SD simulation model? a. IF THEN ELSE ( normal immune population−immune population susceptible to immune population time delay < 0, MIN(normal immune population−immune population susceptible to immune population time delay , susceptible population susceptible to immune population time delay ), MIN(normal immune population−immune population susceptible to immune population time delay , immune population susceptible to immune population time delay ) ) b. MAX ( MIN(normal immune population−immune population susceptible to immune population time delay , susceptible population susceptible to immune population time delay ), −immune population susceptible to immune population time delay ) | | | | | | | | | | | | | | | | | | | | | | |STOP | 196 | | | | | | | | | | | | | | |\$| | |
9. 9. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV c. MIN ( MIN(normal immune population−immune population susceptible to immune population time delay , susceptible population susceptible to immune population time delay ), −immune population susceptible to immune population time delay ) d. MIN ( MAX(normal immune population−immune population susceptible to immune population time delay , susceptible population susceptible to immune population time delay ), −immune population susceptible to immune population time delay ) Multiple Choice Question 11 The runs displayed below were generated with the SD simulation model next to it: what is wrong? a. The parameter values: such wrong behaviors are often caused by wrong parameter values. b. The model equations: the ﬂows should have been speciﬁed as non-negative ﬂows. c. The combination of integration method and time step: this is a numeric integration error. d. Nothing is wrong. If that is the model that generated this output, then it simply is the right model behavior. Multiple Choice Question 12 Consider the (slightly aggregated and simpliﬁed) stock-ﬂow diagram concerning the cholera epi- demic in Zimbabwe (2008-2009) which you may have modeled and simulated (ex.14.9 on p171). | | | | | | | | | | | | | | | | | | | | | | |STOP | 197 | | | | | | | | | | | | | | |\$| | |
10. 10. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt How many feedback loops do you need in an extremely aggregated CLD in order to respect the structure and the dynamics of the simulation model above? a. 1 feedback loop b. 2 feedback loops c. 7 feedback loops d. None of the previous answers Multiple Choice Question 13 What is the minimum number of independent feedback loops in the following simulation model on overﬁshing of blueﬁn tuna? a. 1 feedback loop b. 5 feedback loops c. 6 feedback loops d. 7 feedback loops Multiple Choice Question 14 Suppose you are a professor at a faculty that depends on a ﬁxed monthly teaching fee and a ﬁxed amount of money earned per publication and has a policy to hire as many professors as possible as long as the (roll-over) cash reserves remain positive and stable. Your dean asks you to make a SD simulation model about the dynamics of a faculty to explore plausible futures of the system. | | | | | | | | | | | | | | | | | | | | | | |STOP | 198 | | | | | | | | | | | | | | |\$| | |
11. 11. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Your model, of which the incomplete CLD is displayed on the left, generates growth, overshoot, damped oscillations and, after a while, convergence to an equilibrium, both in terms of the number of professors (blue) and the cash reserves (red). The ambitious dean ﬁnds the limits to growth unacceptable and asks you to identify policy levers that will change the developmental path of the faculty in line with his expansionary desires. Based on this CLD (and possibly your experience with a simulation model like this), what would you advise the dean? a. Reduce the average salary per professor! b. Increase the average salary per professor! c. Create the conditions for professors to publish more. d. In the long term, nothing will help: this system will always converge to the same equilibrium. Multiple Choice Question 15 What is wrong with the SD model about the police ﬁght against burglaries (HB) displayed above? a. One of the loop does not have at least one stock variable, hence, there are simultaneous equations. b. The police response seems to be reactive instead of proactive. c. The simulation model is not endogenous enough: the seasonality of the percentage of houses with opportunities for burglary should have been modeled endogenously. d. Nothing seems to be wrong with this simulation model nor with the situation it represents. Multiple Choice Question 16 A quick sensitivity analysis on a model about de/radicalization generates only two types of be- havior (see left ﬁgure below): either radicalization or deradicalization. A ‘brute force’ uncertainty analysis conﬁrms this conclusion: this model only generates these two modes of behavior even with 10000 runs and enormous uncertainty bands. Further analysis shows that a particular set of | | | | | | | | | | | | | | | | | | | | | | |STOP | 199 | | | | | | | | | | | | | | |\$| | |
12. 12. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt counter-intuitive policies with regard to radicalization has –in contrast to other sets of policies– a robust inﬂuence on the modes of behavior: this appropriate set of proactive policies should allow to nip undesirable radicalization in the bud, that is to say, it does in the model. What do you conclude with regards to the sensitivity? (a) deradicalization (red) versus radicalization (blue) 1940 1960 1980 2000 2020 2040 2060 2080 2100 0.0 0.2 0.4 0.6 0.8 1.0 (b) radical action level (uncertainty analysis with 10000 runs) a. Real-world de/radicalization is behaviorally –not policy– sensitive for these policies. b. Real-world de/radicalization is behaviorally and policy sensitive for these policies. c. This de/radicalization model is behaviorally –not policy– sensitive for these policies. d. This de/radicalization model is behaviorally and policy sensitive for these policies. Multiple Choice Question 17 Which of the following graphs cannot be generated with the model on the right. (Note that equations and parameters are unknown and that it is not known whether X or Y are displayed in these graphs.) a. Only i b. Only ii c. Only iii d. Either none or all (a) i (b) ii (c) iii | | | | | | | | | | | | | | | | | | | | | | |STOP | 200 | | | | | | | | | | | | | | |\$| | |
13. 13. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Multiple Choice Question 18 (d) Debt (e) Production Plants The graphs above display 20 simulations of the amount of debt over time and the number of production plants over time with the Debt Crisis model for variations of their initial conditions (initial production plants between 0 and 200, initial debt between 0 and 2000, latin hypercube sampling). Which of the following state space diagrams developed with the structure and models developed by Hartmut Bossel (2007a, Z115) corresponds to the above behaviors over time graphs? (a) (b) (c) (d) Multiple Choice Question 19 The unﬁnished and overly detailed CLD concerning the potential long term eﬀects of cholera outbreaks displayed below is hardly communicable. Which of the aggregated CLDs corresponds best with the underlying simulation model and allows to communicate about potential short term as well as long term eﬀects? | | | | | | | | | | | | | | | | | | | | | | |STOP | 201 | | | | | | | | | | | | | | |\$| | |
14. 14. STEP: MCQs Part IV c⃝ 2013 by Erik Pruyt (a) (b) (c) (d) | | | | | | | | | | | | | | | | | | | | | | |STOP | 202 | | | | | | | | | | | | | | |\$| | |
15. 15. c⃝ 2013 by Erik Pruyt STEP: MCQs Part IV Multiple Choice Question 20 Which statement regarding the simulation model about the consequences of the growing demand for lithium from the ICT and electrical vehicles sectors fully displayed above is most correct? a. The submodel in blue is an inherently limited diﬀusion structure. Due to these inherent limits, lithium will never be overexploited by electrical vehicle diﬀusion. b. Although for a constant positive growth rate of lithium demand from the ICT sector, the submodel in green will ﬁrst generate exponential growth of the annual ICT lithium demand, although, in the end, growth of the annual ICT lithium demand is likely to be constrained in this model by the limits to lithium exploitation. c. Loop-wise this model is incomplete: important loops, such as the ones connecting lithium availability to the electriﬁcation of the vehicle ﬂeet and growth of the lithium-based ICT sector, are missing. d. Unexploited lithium reserves and resources should not be modeled as a limited non-renewable stock: the diﬀerent reserves and resources should be modeled in detail. Link to the answers to the 15 right/wrong questions & 20 multiple choice questions in this chapter. Links to web based quizzes: | | | | | | | | | | | | | | | | | | | | | | | | | | |STOP | 203 | | | | | | | | | | | | | | |\$| | |
16. 16. 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: