Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling

Systems Modeling of the Water-Energy-Food-Ecosystems Nexus: Insights from a Region Facing Structural Water Scarcity in Southern Spain

The complex relationship between water, energy, food, and ecological systems, known as the WEFE nexus, has emerged as a major topic in the debate about sustainable economic development and resource management. This subject is of special interest in Mediterranean coastal areas as rapid economic expansion driven by population growth, higher influx of tourists, and intensification of agriculture is leading to structural water scarcity conditions. However, addressing the diverse range of issues associated with the nexus is a difficult task due to the existence of intricate interconnections, interdependencies, and nonlinearities within and across its various components. Accordingly, this case study applies a combination of participatory systems modeling and network analysis tools to yield insights into the complexity of this nexus in Axarquia, a region with features that make it an example of water-stressed jurisdictions in the Mediterranean. Overall, our results provide a strong foundation to understand the dynamics that govern this nexus in regions where the availability of freshwater resources is a significant concern. Furthermore, they lay the groundwork for the development of models and scenarios to simulate the impact of various policies and interventions on the overall system.

Advancing the understanding and treatment of post-traumatic stress disorder with computational modelling

The existing theories of post-traumatic stress disorder (PTSD) have inspired large volumes of research and have contributed substantially to our current knowledge base. However, most of the theories are of a qualitative and verbal nature, and may be difficult to evaluate and compare with each other. In this paper, we propose that one way forward is to use computational modelling to formulate more precise theories of PTSD that can be evaluated by (1) assessing whether the model can explain fundamental phenomena related to PTSD, and (2) comparing simulated outcomes with real data. Computational modelling can force us to describe processes more precisely and achieve stronger theories that are viable for testing. Establishing the theoretical groundwork before undertaking empirical studies can help us to avoid doing research with low probability of valid results, and counteract the replicability crisis in psychology. In conclusion, computational modelling is a promising avenue for advancing the understanding and treatment of PTSD.

Mapping complex public health problems with causal loop diagrams

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Why we struggle to make progress in obesity prevention and how we might overcome policy inertia: Lessons from the complexity and political sciences

Despite evidence for the effectiveness of policies that target obesogenic environments, their adoption remains deficient. Using methods and concepts from complexity and political science (Stock-and-Flow analysis and Punctuated Equilibrium Theory) and a qualitative literature review, we developed system maps to identify feedback loops that hinder policymaking on mitigating obesogenic environments and feedback loops that could trigger and sustain policy change. We found numerous self-reinforcing feedback loops that buttress the assumption that obesity is an individual problem, strengthening the biomedical and commercial weight-loss sectors' claim to "ownership" over solutions. That is, improvements in therapies for individuals with obesity reinforces policymakers' reluctance to target obesogenic environments. Random events that focus attention on obesity (e.g., celebrities dismissing soda) could disrupt this cycle, when actors from outside the medical and weight-loss sector (e.g., anti-weight stigma activists) successfully reframe obesity as a societal problem, which requires robust and politically relevant engagement with affected communities prior to such events taking place. Sustained prioritization of policies targeting obesogenic environments requires shared problem ownership of affected communities and nonhealth government sectors, by emphasizing cobenefits of policies that target obesogenic environments (e.g., ultraprocessed food taxation for raising revenue) and solutions that are meaningful for affected communities.

Urban densification in the Netherlands and its impact on mental health: An expert-based causal loop diagram

Urban densification is a key strategy to accommodate rapid urban population growth, but emerging evidence suggests serious risks of urban densification for individuals' mental health. To better understand the complex pathways from urban densification to mental health, we integrated interdisciplinary expert knowledge in a causal loop diagram via group model building techniques. Six subsystems were identified: five subsystems describing mechanisms on how changes in the urban system caused by urban densification may impact mental health, and one showing how changes in mental health may alter urban densification. The new insights can help to develop resilient, healthier cities for all.

The need for systems thinking to advance Alzheimer's disease research

Despite extensive research efforts to mechanistically understand late-onset Alzheimer's disease (LOAD) and other complex mental health disorders, curative treatments remain elusive. We emphasize the multiscale multicausality inherent to LOAD, highlighting the interplay between interconnected pathophysiological processes and risk factors. Systems thinking methods, such as causal loop diagrams and systems dynamic models, offer powerful means to capture and study this complexity. Recent studies developed and validated a causal loop diagram and system dynamics model using multiple longitudinal data sets, enabling the simulation of personalized interventions on various modifiable risk factors in LOAD. The results indicate that targeting factors like sleep disturbance and depressive symptoms could be promising and yield synergistic benefits. Furthermore, personalized interventions showed significant potential, with top-ranked intervention strategies differing significantly across individuals. We argue that systems thinking approaches can open new prospects for multifactorial precision medicine. In future research, systems thinking may also guide structured, model-driven data collection on the multiple interactions in LOAD's complex multicausality, facilitating theory development and possibly resulting in effective prevention and treatment options.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk

Orthostatic hypotension (OH) is an established and common cardiovascular risk factor for falls. An in-depth understanding of the various interacting pathophysiological pathways contributing to OH-related falls is essential to guide improvements in diagnostic and treatment opportunities. We applied systems thinking to multidisciplinary map out causal mechanisms and risk factors. For this, we used group model building (GMB) to develop a causal loop diagram (CLD). The GMB was based on the input of experts from multiple domains related to OH and falls and all proposed mechanisms were supported by scientific literature. Our CLD is a conceptual representation of factors involved in OH-related falls, and their interrelatedness. Network analysis and feedback loops were applied to analyze and interpret the CLD, and quantitatively summarize the function and relative importance of the variables. Our CLD contains 50 variables distributed over three intrinsic domains (cerebral, cardiovascular, and musculoskeletal), and an extrinsic domain (e.g., medications). Between the variables, 181 connections and 65 feedback loops were identified. Decreased cerebral blood flow, low blood pressure, impaired baroreflex activity, and physical inactivity were identified as key factors involved in OH-related falls, based on their high centralities. Our CLD reflects the multifactorial pathophysiology of OH-related falls. It enables us to identify key elements, suggesting their potential for new diagnostic and treatment approaches in fall prevention. The interactive online CLD renders it suitable for both research and educational purposes and this CLD is the first step in the development of a computational model for simulating the effects of risk factors on falls.

Factor structure and psychometric properties of the german version chronic uncertainty scale (CU-20)

BACKGROUND

The experience of uncertainty is ubiquitous and universal across the globe. Many available tools measuring uncertainty are focused on one aspect of uncertainty, e.g., patients with life-threatening illnesses, hence a measure considering (chronic) uncertainty as an integral experience reflect ongoing uncertainties from a socio-cultural perspective is missing. Additionally, current tools do not account for an extended timeframe to measure chronic forms of uncertainty. The objective of this study is to validate a translated German version of the 20 item Chronic Uncertainty Scale (CU-20).

METHODS

The full sample comprised N = 462 participants. Most of the participants were young German citizens and the sex distribution was relatively balanced (60% females; age in average: M = 24.56; SD = 4.78). Using equally split samples, an exploratory factor analysis (EFA) evaluated the CU-20 factor structure, followed by a confirmatory factor analysis (CFA) to test the established factor structure. Measurement invariance between male and female groups was evaluated. Internal consistency of the six-factor model was shown and scale discrimination was shown against chronic stress.

RESULTS

The EFA results showed decent model fit for the five-factor structure, however based on the CFA results, the theoretically established six-factor model fits the data significantly better. Measurement invariance between male and female groups was shown to be clearly scalar invariant. Cronbach's alpha, omega and lambda all support internal consistency and reliability of CU-20.

CONCLUSIONS

The CU-20 is a valid and reliable measure of one's state of chronic uncertainty reflecting the individuals' experiences of macrosocial forms of uncertainty, compared to the existing ones. This scale is especially useful in the context of migration, refugees or during global crises. Further psychometric testing is required in more diverse samples and a deeper look into measurement invariance is recommended.

Understanding the system dynamics of obesity-related behaviours in 10- to 14-year-old adolescents in Amsterdam from a multi-actor perspective

Introduction and Methods

To develop an understanding of the dynamics driving obesity-related behaviours in adolescents, we conducted systems-based analysis on a causal loop diagram (CLD) created from a multi-actor perspective, including academic researchers, adolescents and local stakeholders.

Results

The CLD contained 121 factors and 31 feedback loops. We identified six subsystems with their goals: (1) interaction between adolescents and the food environment, with profit maximisation as goal, (2) interaction between adolescents and the physical activity environment, with utility maximisation of outdoor spaces as goal, (3) interaction between adolescents and the online environment, with profit maximisation from technology use as goal, (4) interaction between adolescents, parenting and the wider socioeconomic environment, with a goal focused on individual parental responsibility, (5) interaction between healthcare professionals and families, with the goal resulting in treating obesity as an isolated problem, and (6) transition from childhood to adolescence, with the goal centring around adolescents' susceptibility to an environment that stimulates obesity-related behaviours.

Discussion

Analysis showed that inclusion of the researchers' and stakeholders' perspectives contributed to an understanding of how the system structure of an environment works. Integration of the adolescents' perspective enriched insights on how adolescents interact with that environment. The analysis further showed that the dynamics driving obesity-related behaviours are geared towards further reinforcing such behaviours.

Association of social participation and psychological resilience with adverse cognitive outcomes among older Chinese adults: A national longitudinal study

BACKGROUND

Limited epidemiological evidence exists regarding the association of social participation and psychological resilience with cognitive health. This study aimed to comprehensively investigate the effects of social participation and psychological resilience on adverse cognitive outcomes among older adults in China.

METHODS

We used two waves (2011 and 2014) of data from the Chinese Longitudinal Survey of Health and Longevity (CLHLS), and 9765 respondents were eligible for the subsequent screening for the present prospective analysis. The Cox proportional hazards model was utilized to examine the association of social participation and psychological resilience with cognitive impairment, cognitive decline and greater cognitive decline. The restricted cubic spline plots were applied to clarify the dose-response relationships between them.

RESULTS

Compared to those with low social participation, participants with high social participation had a lower hazard ratio (HR) of 0.72 (95 % confidence interval [CI]: 0.59-0.89) for cognitive impairment, 0.85 (95 % CI: 0.76-0.94) for cognitive decline and 0.78 (95 % CI: 0.67-0.90) for greater cognitive decline. Participants with high psychological resilience had an HR of 0.77 (95 % CI: 0.63-0.95) for cognitive impairment 0.85 (95 % CI: 0.76-0.94) for cognitive decline and 0.85 (95 % CI: 0.73-0.98) for greater cognitive decline compared with those with low psychological resilience. Similar effects were observed for social participation score and psychological resilience score. The dose-response analysis also showed that the risk of adverse cognitive outcomes decreased gradually with increasing social participation scores and psychological resilience scores. Additionally, the multiplicative interaction between social participation and psychological resilience was not significant.

LIMITATION

All information was collected by self-report, which may lead to biases in the process of information collection.

CONCLUSION

In this study, social participation and psychological resilience were independently associated with a lower risk of adverse cognitive outcomes, and therefore both need to be considered as broader measures to preserve cognitive health among older Chinese adults.

Simulating the multicausality of Alzheimer's disease with system dynamics

INTRODUCTION

In Alzheimer's disease (AD), cognitive decline is driven by various interlinking causal factors. Systems thinking could help elucidate this multicausality and identify opportune intervention targets.

METHODS

We developed a system dynamics model (SDM) of sporadic AD with 33 factors and 148 causal links calibrated with empirical data from two studies. We tested the SDM's validity by ranking intervention outcomes on 15 modifiable risk factors to two sets of 44 and 9 validation statements based on meta-analyses of observational data and randomized controlled trials, respectively.

RESULTS

The SDM answered 77% and 78% of the validation statements correctly. Sleep quality and depressive symptoms yielded the largest effects on cognitive decline with which they were connected through strong reinforcing feedback loops, including via phosphorylated tau burden.

DISCUSSION

SDMs can be constructed and validated to simulate interventions and gain insight into the relative contribution of mechanistic pathways.