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Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the c...
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... can also calculate each of these metrics for our causal structure diagram examples of the Schelling Segregation and Logistic Population Growth models. Results are shown Table 3. For both examples, all the nodes and edges in the causal structure graphs are involved in feedback loops, leading to feedback densities of 1. Causal complexity for the Schelling Segregation model is 8 (twice its cyclomatic complexity), and causal complexity for the Logistic Population Growth model is 6 (again, twice its cyclomatic complexity). ...Citations
... However, systems dynamics models (consisting of systems of algebraic and ordinary differential equations) and discrete-event simulation models of causal systems typically contain more detailed information about the timing of changes than BN or DBN models, and, therefore, cannot be fully represented by them. Dependencies among time series variables can still be represented by directed graphs (not necessarily acyclic) (Naugle et al. 2023), and many of the ideas that follow that are explained in the context of BNs can be generalized to other causal models in which the values or probability distributions of some variables are determined by the values of others. ...
Many recent articles in public health risk assessment have stated that causal conclusions drawn from observational data must rely on inherently untestable assumptions. They claim that such assumptions ultimately can only be evaluated by informed human judgments. We call this the subjective approach to causal interpretation of observational results. Its theoretical and conceptual foundation is a potential outcomes model of causation in which counterfactual outcomes cannot be observed. It risks depriving decision-makers and the public of the key benefits of traditional objective science, which invites scrutiny and independent verification through testable causal models and interventional hypotheses. We introduce an alternative objective approach to causal analysis of exposure-response relationships in observational data. This is designed to be more objective in the specific sense that it is independently verifiable (or refutable) and data-driven, requiring no inherently untestable assumptions. This approach uses empirically testable interventional causal models, specifically causal Bayesian networks (CBNs), instead of untestable potential outcomes models. It enables empirical validation of causal claims through Invariant Causal Prediction (ICP) tests across multiple studies. We explain how to use CBNs and individual conditional expectation (ICE) plots to quantify the effects on health risks of changing exposures while taking into account realistic complexities such as imperfectly controlled confounding, missing data, and measurement error. By ensuring that all causal assumptions are explicit and empirically testable, our framework may help to improve the reliability and transparency of causal inferences in health risk assessments.
... Large variation or limited accuracy resulted in a non-calibrated model. Simulation models can depict the intricate interactions and feedbacks that drive system behaviour by using feedback loops to simulate the cyclical flows of influence between various system components [63] . These feedback loops also allow models to depict complex interactions, dynamic effects propagation, self-regulation, and learning [64] . ...
... Other studies use semiquantitative cognitive mapping techniques that capture network structure richness and web-like causality (Gray et al., 2019). These studies compare and contrast different network measures applied to mental maps (Haque et al., 2023;Naugle et al., 2021). ...
... When using traditional DEA models (Section 4), a computed score may be biased if the contextual factors are not considered. This is relevant when measuring one's ST skills, because there are socio-demographic and educational background factors that could affect one's levels of ST (Naugle et al., 2021). ...
Systems thinking (ST) includes a set of critical skills and approaches for addressing today's complex societal problems. Therefore, it has been introduced into the curricula of many educational programmes around the world. Despite all the attention to ST, there is less consensus when it comes to evaluating and assessing ST skills. Particularly, a quantitative assessment approach that captures ST's multi‐dimensionality is crucial when evaluating the degree to which one has learned and utilizes ST. This paper proposes a systematic approach to create such a multi‐dimensional Index of ST from textual data. Initially, we provide an overview of the theoretical background as it pertains to different measurement approaches of ST skills. Then we provide a conceptual framework based on ST skill measures and transform this framework into a quantifiable model. Finally, using student data, we provide an illustration of an integrated index of ST skills. We compute this index by using a mixed methods approach, including robust principal component analysis, data envelopment analysis and two‐staged bootstrapping approach. The results show that (i) our model serves as a systematic multi‐dimensional ST approach by including multiple measures of ST skills and (ii) international students and self‐reported math skills are found as significant predictors of one's level of ST in the graduate student dataset ( N = 30), however no significant factors are found in the first‐year engineering student dataset ( N = 144).
... By fine-tuning the model parameters in a calibration process, the best-fitting model can be found in the continuously growing test data set, assuming that the model structure is optimal for the observed environment. New techniques come into the focus to find the best fitting model structure for the analyzed problem [97]. • Iterative, data-driven models: A sequence of model-based decisions provides the potential to interact with the predicted and the observed outcomes, and repeatedly refine the used models [98], • Survival analysis: covers techniques to simulate multi-period decision series on the basis of single period outcomes and hence to find optimal strategies on longer time horizon [99]. ...
Detecting chemical, biological, radiological and nuclear (CBRN) incidents is a high priority task and has been a topic of intensive research for decades. Ongoing technological, data processing, and automation developments are opening up new potentials in CBRN protection, which has become a complex, interdisciplinary field of science. According to it, chemists, physicists, meteorologists, military experts, programmers, and data scientists are all involved in the research. The key to effectively enhancing CBRN defence capabilities is continuous and targeted development along a well-structured concept. Our study highlights the importance of predictive analytics by providing an overview of the main components of modern CBRN defence technologies, including a summary of the conceptual requirements for CBRN reconnaissance and decision support steps, and by presenting the role and recent opportunities of information management in these processes.
... The literature uses different terms to refer to these elicited mental models such as cognitive maps (Levy et al., 2018), fuzzy cognitive maps (Aminpour et al., 2020;Özesmi and Özesmi, 2004;Yoon and Jetter, 2016), concept maps (Brandstädter et al., 2012;Khajeloo and Siegel, 2022;Sommer and Lücken, 2010;Yin et al., 2005), causal maps (Plate, 2010), or causal structure diagrams (Naugle et al., 2021). The differences between the terms commonly arise from the nature of the elicited maps, the method of elicitation, and the specific field of literature. ...
Cognitive maps, or mental maps, are externalized portrayals of mental models—people's mental representations of reality and their presumptions about how the world works. They are often used as the intermediary step toward uncovering individuals' presumptions of the outside world. Yet, the next step is often vague: once one's understanding of the real world is mapped, how can we systematically evaluate the maps and compare and contrast them? In this note, we review several common approaches to analyzing cognitive maps, some rooted in network theories, and apply them to a dataset of 30 graduate students who analyzed a complex socioenvironmental problem. Our analysis shows that these methods provide inconsistent results and often fall short of capturing variations in mental models. The analysis points to a lack of effective methods for examining such maps and helps articulate a major research problem for systems‐thinking scholars.
For the fundamental problem of enumeration of all simple cycles of a given directed graph with n vertices and m edges, it is known that the delay time between successive outputs of two simple cycles is O(n+m) where m∈O(n2). This paper shows that for a directed graph under a uniform probability distribution for the out-degrees of the vertices in the graph, all simple cycles of the graph can be listed with delay time O(nlnn) in expectation.
The climate crisis poses a major threat to sustainability, highlighting the need for climate education that develops students' systems thinking skills regarding dynamic environmental issues. For this reason, this project, funded by the United Nations Development Programme (UNDP), was developed by a research university, a non-governmental organization, and a middle school partnership. As part of the project, this study investigated a ten-week after-school climate education program for 8th-grade students that aimed to improve their understanding of the climate system through simulation and policymaking. Semi-structured interviews were analyzed to assess the program's model alignment with students' explanations. The most frequently mentioned category was the economy, followed by the carbon cycle; population and the greenhouse effect were the least common. The study offers valuable insights into using a systems approach in climate education to comprehend climate complexity and dynamics by considering the importance of economy and population in understanding human-caused climate change.
Chapter 7 is about feedback, circular causality, and system dynamics modeling. Circular causality due to positive or negative feedback is a major cause of complexity. When the causality between variables is not linear but circular, we cannot separate causes and effects, which makes it difficult to predict what will happen in a system. Ignoring feedback can lead to unintended consequences, of which several examples are given. This chapter also provides a broad discussion of causality in the social sciences, which is more difficult than the law-like causality in the natural sciences. System dynamics modeling is an excellent way to analyze the effects of feedback and circular causality. After the thorough discussion of causality, the chapter introduces the basics of system dynamics. As economic applications, the Bass diffusion model based on the SIR model from epidemiology, the beer distribution game, a model of a renewable common-pool resource, and a model of societal collapse are presented.
