Reproducibility of research is critical for the healthy growth and accumulation of reliable knowledge, and simulation-based research is no exception. However, studies show many simulation-based studies in the social sciences are not reproducible. Better standards for documenting simulation models and reporting results are needed to enhance the reproducibility of simulation-based research in the social sciences. We provide an initial set of Reporting Guidelines for Simulation-based Research (RGSR) in the social sciences, with a focus on common scenarios in system dynamics research. We discuss these guidelines separately for reporting models, reporting simulation experiments, and reporting optimization results. The guidelines are further divided into minimum and preferred requirements, distinguishing between factors that are indispensable for reproduction of research and those that enhance transparency. We also provide a few guidelines for improved visualization of research to reduce the costs of reproduction. Suggestions for enhancing the adoption of these guidelines are discussed at the end.
Extending the line of research on stock-flow performance we examined the impact of personality characteristics on task performance. It was assumed that the need for cognition, the need for closure and the preference for intuition and deliberation would relate to individual variations in task performance differentiated into the dimensions heuristic reasoning, task effectiveness (number of correct answers) and task efficiency (time needed to perform the task). It was found that the need for closure did not relate to any of the task performance dimensions, while the preference for deliberation related positively with task effectiveness, and the need for cognition positively with task effectiveness and negatively with heuristic reasoning. Although all three constructs possess a rather explicit temporal dimension, the examined needs and preferences appeared not te be correlated with the time needed to perform the stock-flow task. Further research is needed to substantiate the findings of the current study and to elaborate on the precise relation between needs and preferences and stock-flow information processing as well as to refine the concept of task effectiveness.
This paper presents a system dynamics based macroeconomic model of the Pakistan. The model comprises of population, human development, production, international trade and system of national accounts, and public finance modules. Conscious efforts have been made to achieve the best possible blend of standard long-run theories and country-specific features to model underlying system structure of human centered development in Pakistan by focusing on long-term dynamics. The tracking performance of the model is evaluated. Empirical investigation of a number of topical macroeconomic issues utilizing model simulations have shown the model to be useful which would be extended to address spatial dimension of socioeconomic planning issues of Pakistan. The model helps to better conceptualize the underlying system structure to bring in a broad-based improvement in the human condition without forgoing economic growth. It highlights the need to mobilize cost effective resource generation and suggests that priority be given to allocation of public finance to human development and not the economic services and infrastructure confirming that human development and economic growth are interdependent and intertwined in feedback processes which are mutually reinforcing and that human development is not only an end in itself but is a means to achieve higher productivity as well. This challenges the very basis of continued disregard of human development by public finance managers of Pakistan.
This paper presents a bright future for quantitative System Dynamics Modeling. This future relates to all major issues and grand challenges which all happen to be dynamically complex and deeply uncertain. Combining System Dynamics Modeling and Exploratory Modeling and Analysis allows one to generate, explore and deeply analyze tens of thousands of plausible scenarios related to such deeply uncertain dynamically complex issues, and to design and test adaptive policies over all plausible scenarios. By doing so, the art of System Dynamics becomes the computational science of System Dynamics. This innovative approach is explained in this paper starting from the core of System Dynamics modeling, and is illustrated with three real world applications (pandemic shocks, resource scarcity, and energy transitions). However, more is needed than the brightest analysts and the best analyses for decision makers to decide and take action when facing uncertain complex issues: that is what experiential System Dynamics gaming is needed for. Only when heart and mind are aligned will knowledge and understanding become effective. The use of experiential System Dynamics gaming for conquering the heart of decision makers is illustrated with real world examples too.
In an ever more complex and uncertain world, integrated risk-capability analysis methodologies that allow dealing with increasing degrees of complexity and deep uncertainty are needed more than ever before. Today, some governments and organizations use scenario approaches, risk assessment methods, and capability-based planning, but few have truly integrated risk-capability approaches, and almost none use integrated risk-capability approaches appropriate for deeply uncertain complex risks. However, many important risks are particularly dynamically complex and deeply uncertain. This paper presents and illustrates a novel integrated risk-capability analysis approach for deeply uncertain dynamically complex risks, and discusses near future developments. As such, it illustrates a multi-method consisting of Exploratory Modeling and Analysis, Exploratory System Dynamics Modeling, Scenario Discovery and Selection, and MCDA, and discusses the use of Robustness Optimization for simultaneous all-hazard capability-based planning.
Lyme disease poses an uncertain dynamic threat to many people and public health systems. However, rather different perspectives related to the societal impact of Lyme Disease exist. Thousands of plausible evolutions of lyme disease are generated using different System Dynamics models of Lyme Disease and are studied in this exploratory study with new data analysis techniques in order to assess the risk posed by Lyme disease, in this case to the Dutch population and the Dutch health care system. The risk is scored in the Dutch National Risk Assessment framework adapted to deeply uncertain dynamically complex risks, and mapped in a new type of risk diagram developed for uncertain complex risks in order to compare the risk posed by Lyme disease to many other plausible risks. Scenario discovery techniques are used to identify a small set of representative scenarios that could be used for subsequent capability analysis.
This follow-up paper presents cases and multiple choice questions for teaching and testing System Dynamics modeling. These cases and multiple choice questions were developed and used between January 2012 and April 2012 a large System Dynamics course (250+ BSc and 40+ MSc students per year) at Delft University of Technology in the Netherlands. The cases presented in this paper could be useful for teaching and testing introductory/intermediate System Dynamics courses at universities as well as for self study. For these cases, students need to develop simulation models, answer multiple choice questions related to their models, as well as open questions related to their modeling and model use. Second, the use of multiple choice questions and quizzes for teaching and testing System Dynamics understanding and modeling skills is discussed and illustrated. Finally, changes to the System Dynamics curriculum enabled by further development of the teaching/testing approach of the Introductory System
The scope of this work is to develop an instrument for the economic evaluation of agricultural research in the productive chain of wheat and its impact in the profitability of agricultural cooperatives. A conceptual model was developed using the balanced scorecard and system dynamics methodologies. In the development of the model all processes involved in the productive chain of wheat agricultural research were initially mapped. Furthermore, a BSC strategic map was developed, explaining the objectives and indicators of the cooperative. Finally, using the system thinking approach, a modeling was driven seeking enlargement of the problem systemic vision. The resulting model developed in this work allowed a better understanding of the complex relationships between research and agricultural production, making it easier to analyze the process and the decision of new investments in research on the part of managers and analysts of agricultural cooperatives.
This article discusses about the design of a qualitative model of strategic implementation and control in agro-industrial cooperatives. Based on the concepts of Balanced Scorecard - BSC and System Dynamics - SD, and considering the corporate features of cooperatives as people societies and not capital societies, the article proposes a strategic map, which presents up variables that represent the critic processes in strategic management for these organizations, as well as identifying causal relations hypothesis between the variables. From the concepts of BSC, the map is built with the four traditional perspectives: financial; customers; internal process; growth and learning; and adding two other important perspectives: the social perspective and the member relationship perspective. From the concepts of SD, the map is qualitatively built, predicting the complexity of strategic control, in accordance to the need of conciliation and balance of economic goals between the organization and its members. From the proposed strategic map, the goal is to proceed with the research, defining new indicators of each variable in the map, as well as its adaptation and application towards cooperatives, through the action-research method. The qualitative model can also serves as a conceptual basis for future parameterization and simulation of a quantitative model.
The health care sector is facing a multitude of problems at the same time: rising costs, increase in patients with lifelong diseases, and unsatisfying quality. There is a prominent role for conditions that require a combination of simple (care) and complex (cure) activities. These conditions require different provider expertise; one offering care expertise or more general, preventive monitoring, and the other offering cure expertise, or more specialized, medical monitoring/intervention. In organization design theory the focused factory concept is presented as a way of organizing such processes. However, the application of this concept does not always work well. For decennia, Dutch perinatal care is organized according to the focused factory concept, but recently there has been considerable debate with regard to its performance. Research has shown that the design of the Dutch perinatal care system might not be the right one (Pieters, Van Oirschot, & Akkermans, 2010). In response to its problems, the sector is seeking alternative organization designs. In this paper simulation modeling is used to evaluate these different organization design experiments. From these simulations, we seek to build organization design theory for this type of conditions (Davis, Eisenhardt, & Bingham, 2007; Schwaninger & Grösser, 2008).
Construction projects are complex as they include many activities which influence and interact with each other at different stages. The impact of design phase undiscovered rework on construction phase quality has been hypothesized as influential in project dynamics by many. However few empirical studies have measured this impact. In this paper we develop a simple system dynamics model, estimate it using data from 18 construction projects, and validate the model on a validation set of 15 projects. The model provides good fit for the calibration set and strong predictive power on the validation set. It also allows us to estimate the impact of undiscovered design changes on construction phase quality, which appears to be notable.
In this study, using different versions of a growth management game involving two different complexity factors, we compare performances of heuristic rules with experimental results. We present a method for obtaining a statistical distribution of scores resulting from a given simulated decision heuristic, which can be used to compare against and assess experimental gaming results. The method is based on the idea of generating vast number of scores by stochastically simulating a given decision rule and obtaining the resulting score distribution. We use this method to compare scores from different game versions whose scores are essentially not comparable, and to see how the score distributions change from one game version to another. In simulations, we first use a simple random "decision rule" and then develop a more intelligent hill-climbing heuristic. The results show that when the games involve delay, human subjects do not perform better than the random heuristic a primitive rule composed of a sequence of random decisions. On the other hand, in nonlinear games, subjects outperform the random heuristic and their scores fit better the score distribution of the hill-climbing heuristic. We also demonstrate how the score distribution from random heuristic can be used as a reference performance measure.
The aim of this study is to test statistically the effects of delay, nonlinearity and feedback factors on the complexity of a stock management task. The task requires the player to bring the inventory to a target level and keep it there. Each of the individual complexity factors brings different challenges to the game. Using a slightly modified Latin square experimental design, we test the factors at different strength levels. We use two measures of game complexity: game scores and players subjective difficulty ratings. The results show that, with respect to the simple base game, delay and nonlinearity create worsening in game performances. Also, with increased delay duration, delay order and nonlinearity, subjects' performances deteriorate. However, feedback does not deteriorate the game performance. Furthermore, increased feedback strength even improves scores, due to a technical side effect on the performance measure. All subject groups exhibit learning by repeated trials. Nevertheless, there is also evidence that delay prevents transfer of learning to other game versions. The subjective complexity ratings of the players yield parallel results, the overall correlation of game scores with the subjective difficulty ratings being +0.59.