System Dynamics Approach Research Articles

The evolution of online public opinion on earthquakes: a system dynamics approach

Earthquakes endanger lives and properties and trigger widespread online public opinion. If the government fails to implement emergency measures quickly, the risks associated with this online public opinion may escalate and present significant challenges in managing it. To solve this issue, this paper investigates the M5.7 earthquake in Yibin, China, as a case study, drawing data from China’s Sina Weibo. It also develops an evolutionary model of earthquake online public opinion using the life cycle theory of public opinion dissemination, public opinion crisis management theory, and system dynamics methods. This study chooses a system dynamic model because it can capture overall trends and feedback cycles. This approach provides a comprehensive view of the evolution of the whole system’s behavior and public opinion during the crisis. This model analyzes the evolution of the online public opinion of the earthquake from the perspective of the earthquake, netizens (social media users), media, and government. This study explores the differences between online public opinion by adjusting the size of different variables in the model. This provides an entry point for better control of the development and communication of earthquake online public opinion. The findings indicate that, following the earthquake, the media exerted the most significant influence on public opinion, mainly through media participation, diffusion, and impact, demonstrating a significantly positive correlation with the magnitude of public opinion. Moreover, the simulation revealed that the government is leading in preventing and controlling online public opinion. This study presents the following innovations: (1) It builds a comprehensive online public opinion model for earthquakes, which provides insights for studying online public opinion regarding other sudden natural disasters. (2) It employs system dynamics to simulate earthquake online public opinion, providing countermeasures and recommendations for the government to manage and regulate this discourse effectively.

Dementia Risk and Social Determinants of Health Among Adults Racialized as Black: A Community-Based System Dynamics Perspective.

The aging population in the USA is projected to increase significantly, with a corresponding rise in dementia cases, particularly among racial minorities. This study examines the key drivers of racial disparities in dementia risk among older Black adults in the St. Louis area, a region characterized by entrenched structural racism. Utilizing a Community-Based System Dynamics (CBSD) approach, we engaged cognitively normal Black adults (age ≥ 45) to explore the complex interplay of social and structural determinants of health (S/SDOH) affecting dementia risk. Eight CBSD workshops were conducted, during which participants identified and analyzed various factors influencing dementia risk through group model-building techniques. These workshops revealed multiple reinforcing and balancing feedback loops, highlighting the intricate relationships between trauma, health literacy, social isolation, education, healthcare access, and systemic racism. There were 59 participants with an average age of 64, a majority of women (88%) and college-educated (15.9years) residing in areas with moderately severe deprivation. The resulting Causal Loop Diagrams underscored the impact of poverty, discrimination, and limited access to quality education and healthcare on dementia risk across the lifespan. Participants proposed actionable interventions, including health information campaigns, community mobilization, and improvements in public transportation and healthcare accessibility. This study emphasizes the necessity of addressing S/SDOH to mitigate dementia risk among Black Americans. The findings call for targeted public health initiatives and policy changes to improve socioeconomic conditions and reduce racial disparities in dementia outcomes.

Unearthing Agricultural Land Use Dynamics in Indonesia: Between Food Security and Policy Interventions

Despite its essential and irreplaceable role in food security, the agricultural sector is often perceived as having the potential to be sacrificed in the name of economic growth, leading to significant land cover transformation into built-up areas and plantations. This complexity prompts critical questions regarding the efficacy of agricultural policy interventions, including augmenting production capacity, diversifying commodities, and expanding agricultural land in light of Indonesia Emas 2045, which embodies Indonesia’s ambitious multidimensional developmental transformation. This study employed a system dynamic approach with region-based closed feedback, focusing on four primary commodities: rice, cassava, corn, and palm oil. It concludes that food vulnerability will become increasingly severe by 2045, particularly in Java Island, which, despite being the sole area with a food surplus, is also experiencing escalating development pressures. In other regions, this pressure transforms transitory land from areas growing rice to areas growing other agricultural commodities, and subsequently to built-up land. The policy intervention scenario, albeit less impactful in reflecting substantial land cover changes nationally, can facilitate swift commodity production. This study emphasizes the necessity of intensifying rather than expanding agricultural land.

Economic evaluation of nine intersectoral strategies to improve youth mental health and alleviate financial burden in Colombia using system dynamics modelling

ObjectivesLimited evidence exists for strategic actions in improving the youth population-level mental health, particularly in low- and middle-income countries. This study investigates the health and economic impacts of inter-sectoral mental health programs to inform the optimal suite of interventions for young people in Bogotá, Colombia, where many are at risk of mental disorders and lack access to appropriate health services. MethodsThis study examined nine interventions compared to business-as-usual over a projected 10-year time horizon using a system dynamics model developed through a participatory process involving diverse local stakeholders. Sensitivity analyses were performed to account for uncertainties from a healthcare payer perspective. ResultsAnti-bullying, community-based rehabilitation, and online mental health programs were dominant. GP mental health training and gatekeeper suicide prevention training could significantly improve mental health outcomes at an incremental cost-effectiveness ratio (ICER) below the threshold of 4,890 USD/QALY. However, simultaneously implementing all nine strategies could produce a synergistic effect, yielding the greatest benefit relative to business-as-usual with an incremental net monetary benefit of USD 465 million and an ICER of USD 586 per QALY gained. ConclusionsCombined intersectoral mental health strategies were synergistic and resulted in optimum health outcomes relative to business-as-usual. System dynamics approach offers additional attributes to economic evaluation in mental health settings by considering capacity constraints, unintended consequences, and interactions between multiple interventions. It is a valuable approach for decision-makers to effectively and efficiently prioritise and allocate scarce resources in complex public health systems.

Towards a System Dynamics Model on Risk Factors of Knee Osteoarthritis: A Study Protocol for the DYNAMIKOS Model

(1) Background: Osteoarthritis (OA) is a serious chronic disease mostly affecting the knee joint. Despite the many efforts for developing strategies to predict and control Knee Osteoarthritis (KOA), the disease is on the rise. This paper describes the process for the creation of a simulation model, the Dynamic Knee Osteoarthritis Simulation (DYNAMIKOS) model, that captures the complex interrelationships of the risk factors for the development of KOA; (2) Methods: The DYNAMIKOS model will be based on the System Dynamics approach. The first step will be to develop a Causal Loop Diagram (CLD) model for the risk factors involved incorporating a series of Group Modeling Building (GMB) workshops with experts and stakeholders. Using data from a representative sample of KOA patients, the statistical approaches Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling (SEM) will be carried out. (3) Results: This study will develop a simulation System Dynamics model for the risk factors of KOA based on the results of CLD and SEM; (4) Conclusions: The proposed DYNAMIKOS model could be used for effectively analyzing the complex interrelationships among the multiple factors that constitute the spread of KOA. In this way, plausible prevention strategies could be implemented for effectively managing and leading the potential eradication of KOA.

Capacity investment portfolio optimization considering EV charging flexibility: A heuristic algorithm-informed system dynamics approach

Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.

Debt-deflation theory and middle-income trap: system dynamics approach

The article analyzes the theoretical and practical aspects of the theory of the relationship between national debt and deflationary processes using the example of the impact of the economic recession in China on the decline in global economic growth, and determines the systemic effects of the national economy falling into the middle income debt trap and exiting it. With the help of simulation models, which are based on the methodology of system dynamics, the interrelationship of economic processes of global and national economies is presented. It was found that deflation leads to an increase in the price of the existing external and internal debt and inhibits economic growth and economic development of the national economy and is one of the forms of the country’s middle income trap. The use of the scenario approach made it possible to identify the basic options for the development of the world economy under the influence of negative economic phenomena at the national level and to develop appropriate recommendations for middle-income countries to achieve the goals of macroeconomic and financial stabilization with the appropriate access to the trajectory of new economic development.The study found that the factors of education, social and innovative development are of decisive importance in the country’s achievement of the goal of leaving the middle income trap. The economies of such countries as China are unable to independently overcome this trap without further involvement in global integration and trade processes, and at the same time they themselves form deflationary processes at the global level. In addition, the policy of economic and political semi-isolation leads to the destabilization of the existing world economic and financial systems.

Modelling of the Ukrainian labor market: system dynamics approach

The purpose of the article is to develop a System Dynamics model of the functioning and development of the Ukrainian labor market, which can subsequently be used to simulate various government decisions and policies. The Stella Architect software is used to construct the model, allowing to provide insights into the current state of the labor market and forecasts future trends, helping identify potential improvements.The explanatory System Dynamics Model effectively demonstrates the complex interrelationships that influence the dynamics of the Ukrainian labor market. The identified balancing loops highlight key factors such as retirement, business closures, production levels, and the formation of new businesses, all of which shape labor supply and demand. Each loop illustrates how changes in one economic aspect can impact others, underscoring the interconnectedness of labor dynamics, economic growth, and business development. Furthermore, the developed causal loop diagrams emphasize the cyclical nature of these interactions, where increases in labor supply can result in higher wages and GDP, leading to further investment and business creation. This model offers valuable insights into the feedback mechanisms that may stabilize or destabilize the labor market, particularly in response to external shocks or policy interventions. In the future, the developed model can be used to test various policies for major elements of the model and to simulate its behavior.

Utilizing Dairy Waste Processing for Organic Agricultural Production: A Sustainable Approach to Producing Organic Goods

Resources are limited. Thus, farmers worldwide are trying to use their waste to best extract value that can promote environmentally friendly farming. Recycling dairy waste for organic cultivation seems to be one of the alternative approaches to pursuing environmentally friendly agriculture. This study investigates whether or not dairy waste treatment could improve organic crop production and promote a circular economy. This endeavor examines the credible benefits, obstacles, and consequences of incorporating dairy waste into organic farming practices. Using System Dynamics and case study approaches, including field trials, agricultural evaluation of data, and discussions with stakeholders, the research strategy advocates investigating how dairy waste-derived fertilizers influence soil fertility, crop productivity, and product quality. Interaction with stakeholders helps evaluate the viewpoints of growers, dairy producers, legislators, and consumers regarding adopting dairy waste treatment for organic agricultural growth. This study demonstrates how dairy waste processing can serve as a significant source of biofertilizer for organic farming. The beneficial impact of organic fertilizer derived from farm waste improves the nutritional value of organic crops, crop yield, and soil health. The crop production information collected in the study demonstrates that dairy waste-derived fertilizers are nutrient-dense and could substitute for manufactured/chemical fertilizers economically and environmentally. This study emphasizes the need for creative ideas to improve agricultural sustainability. The present study advances the understanding of sustainable agriculture and offers practical advice to those looking to use greener methods of operation.

Dynamics of prescribing and accessing medications for opioid use disorder: a community-based systems analysis

Background Although medications for opioid use disorder (MOUD) are effective for treating opioid use disorder (OUD), persistent barriers still prevent patients from accessing this life-saving care. Policies to increase MOUD access have produced suboptimal results. This study presents a qualitative system dynamics model that elucidates the complexities of accessing and staying in MOUD treatment. Methods We utilized a community-based system dynamics approach to modeling the MOUD treatment system. We engaged a cohort of system experts/stakeholders, including individuals who had received MOUD, treatment providers, and policymakers, in interviews and group model building to develop and refine a simulation model. We then created a qualitative causal loop diagram based on insights gained while developing the simulation model and a review of interview transcripts. Results The causal loop diagram captures four key factors affecting treatment initiation, retention, and leaving: (1) fraught interactions between patients and healthcare providers; (2) stigma-driven regulation of MOUD creating a culture of fear and defensive medicine; (3) a punitive culture in clinics and opioid treatment programs offering MOUD; and (4) the internalization of the abstinence narrative contributing to premature termination of treatment. Conclusions Our analysis highlights how interdependent and non-linear feedback processes diminish or counteract the effectiveness and sustainability of MOUD policy interventions. Due to system memory and cultural resistance to change, even rolling back reactionary policies may do little to curb established behavioral patterns. In addition, conflicting and competing strategies among various actors within the system contribute to goal misalignment and a lack of standardization of care.

From challenges to opportunities: navigating the human response to automated agents in the workplace

Workers are increasingly embracing Artificial Intelligence (AI) to optimise various aspects of their operations in the workplace. While AI offers new opportunities, it also presents unintended challenges that they must carefully navigate. This paper aims to develop a deeper understanding of workers’ experiences with interactions with automated agents (AA) in the workplace and provide actionable recommendations for organisational leaders to achieve positive outcomes. We propose and test a simulation model that quantifies and predicts workers’ experiences with AA, shedding light on the interplay of diverse variables, such as workload, effort and trust. Our findings suggest that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates. Additionally, we find that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation. This interdisciplinary research blends a systems dynamics approach with management theories and psychological concepts, aiming to bridge existing gaps and foster the sustainable and effective implementation of AA in the workplace. Ultimately, our research endeavour contributes to advancing the field of human-AI interaction in the workplace.

Assessment of regional official development programs on solid waste management via system dynamics approach considering their GHGs emissions

Assessment of regional official development programs on solid waste management via system dynamics approach considering their GHGs emissions

Beyond Illumination: Stakeholder Perspectives on Urban Lighting Master Planning for Chiang Mai Old City, Thailand

Chiang Mai Old City, a cultural heritage site and major tourist destination in Thailand, has significant cultural capital but lacks a well-designed urban lighting system, limiting its nighttime development potential. This issue arises from the absence of an urban lighting master plan, a crucial tool for guiding the city’s growth. The challenge lies in reconciling the diverse perspectives of stakeholders to create a comprehensive lighting master plan that meets shared goals. This research proposes a system dynamics approach to analyze stakeholder complexity. A qualitative, multi-stage method was employed, through in-depth interviews and focus groups with 60 stakeholders from three groups: government, professionals, and end users, to prioritize critical factors. Causal Loop Diagrams (CLDs) were used to illustrate the interrelations among those factors, leading to alternative scenarios for the lighting master plan’s development. The findings demonstrate that safety and security is the top priority, followed by cultural and economic factors. Eleven reinforcing loops and two balancing loops are proposed through CLD visualization. This framework highlights the importance of a participatory process, advocating for a systematic and holistic approach where all stakeholders with diverse perspective collaborate side-by-side in the development of the urban lighting master plan for Chiang Mai Old City.

A Mathematical Model for the Combination of Power Ultrasound and High-Pressure Processing in the Inactivation of Inoculated E. coli in Orange Juice.

The mathematical modeling of a combination of non-thermal technologies for E. coli inactivation is of great interest for describing the dynamic behavior of microorganisms in food, with the goal of process control, optimization, and prediction. This research focused on the design and implementation of a mathematical model to predict the effect of power ultrasound (US), high-pressure processing (HPP), and the combination of both non-thermal technologies on the inactivation kinetics of E. coli (DSM682) inoculated in orange juice. Samples were processed by US, HPP, and a combination of both technologies at varying process parameters, and a mathematical model for microbial inactivation was developed using a System Dynamics approach. The results showed that the combination of these technologies exhibited a synergistic effect, resulting in no detectable colony-forming units per mL of juice. The developed model accurately predicted the inactivation of E. coli following the combination of these technologies (R2 = 0.82) and can be used to predict microbial load reduction or optimize it based on process parameters. Additionally, combining both techniques offers a promising approach for extending the shelf life of fresh juices using non-thermal stabilization technology.

Modelling interrelations between C-ITS impact categories: a system-dynamics approach using causal loop diagrams

The growing number of connected vehicles has led to an increased focus on Vehicle-to-Everything (V2X) communication in the field of transport research. This communication paradigm facilitates cooperation between vehicles and infrastructure to address traffic challenges such as efficiency, sustainability and safety. The development and standardisation of such Cooperative Intelligent Transport Systems (C-ITS) has been pursued in several projects. Beyond technical considerations, assessing the effect of these applications in terms of various impact categories is of paramount importance. However, existing research tends to examine impact categories such as efficiency, sustainability, safety, psychological or socioeconomic impacts separately, often overlooking potential interactions and interdependencies. This approach is inadequate as impacts on one category can have both cascading effects on others and rebound effects. To address this gap, this paper proposes a system dynamics approach using Causal Loop Diagrams (CLD) to illustrate the interconnectedness of impact categories and the potential impacts of C-ITS services. By depicting general relationships, interdependencies and feedback loops between impact category elements, the model accommodates the introduction of single or multiple C-ITS services as separate modules, allowing an analysis of their combined effects on the overall system. To this end, two use cases demonstrate the applicability of the developed CLD and illustrate some of the multiple interrelations between the effects of C-ITS services. The results of this paper support road operators and researchers when setting up the impact assessment of C-ITS services by revealing the dynamic and intertwined nature of different impact categories.

Design and assessment of sustainable spent automobile lithium-ion battery industries in Japan: A system dynamic business model approach

An increasing number of used automobile lithium-ion batteries (LIBs) require appropriate treatment, such as disposal as solid waste, recycling of materials, or repurposing as second-life LIBs, to avoid undesired environmental consequences. However, the economic feasibility of these treatments affects industrial development. We propose a system dynamics business model (SDBM) as a novel approach for forecasting the economic impact of the industry in Japan, which provides comprehensive quantitative and qualitative assessments. We designed three business models (BMs): repurposing, recycling, and circular production. Repurposing BM extends the lifespan of LIBs by encouraging battery owners and collectors to engage in the second-life LIB market. Recycling BM supports recyclers in determining cost-effective recycling technologies. Circular production BM provides a policy analysis to the government. We elucidate the anticipated benefits to stakeholders involved in BMs. If all stakeholders have community interests, a circular economy for automobile LIB recycling can be realized without government subsidies.

An experimental design of the blockchain business model using a soft system dynamics modeling approach

PurposeThe growing discussion on blockchain and business models often falls short of demonstrating and evaluating systems consistently exposed to settings of dynamic complexity. Therefore, in practicing systems thinking, this study aims to provide a depiction of dynamic complexity in blockchain business models and develop policy-based scenarios to enhance blockchain-based systems behavior.Design/methodology/approachThis study integrated the soft system dynamic (SD) methodology approach, which focuses on a situation analysis and SDs in policy design. This single case study chose a firm engaged in the content industry, where the adoption of blockchains is a solution to tackle the industry’s significant challenges. Data were collected using a qualitative approach and then adapted into a simulation model.FindingsThe study pinpointed key parameters significantly affecting the system through a sensitivity analysis. Then, this experimental study found that all improvement initiatives delivered better system performance. At the same time, the study also identified counterintuitive findings, where the interventions using multiple value subsystems had insignificant effects on the system compared to a single advent.Originality/valueThis study illuminates the growing field of blockchain and business models through system modeling and experimentation, using an integrative approach like soft system dynamics methodology. It also identifies and demonstrates value distribution and the dynamic complexity inherent in the blockchain business model.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Development of a water resources utilization model and optimization of the patterns of rice genotypes with system dynamics approach

The system dynamics approach was conducted to optimize genotype patterns in various climatic and water availability scenarios, to address the water productivity improvement of the Sefidrood irrigation network. The studied cropping pattern scenarios were SD1 (the continuation of current trends), SD2 (the reduction in the cultivated area), SD3 (replacing modified crops on an annual basis), and SD4 (increasing the annual cultivated area of Hashemi type rice). Moreover, the impacts of irrigation efficiency improvements and water resources management strategies on the physical and economic water productivity indices were studied. The results indicated that the SD3 scenario has the most positive impact on water productivity with 0.45 kg/m3 increases. However, a questionnaire survey revealed that this scenario that belongs to the increase in the modified genotype area cannot be recommended due to cooking quality and low prices. The Hashemi genotype was the favorite rice among farmers and experts. Also, analyzing the irrigation efficiency improvement scenario showed that this strategy has a limited impact on the physical and economic water productivity. Increasing the capacity of local rainwater storage pools, Ab-bandans, and increasing the drainage water reuse had the highest water productivity in the condition of river water reduction and uncertain water availability.

Impact of resilience policies on cape town's water-food nexus: A system dynamics approach

BackgroundClimate change is increasingly affecting the supply of resources such as water and food. From 2015 to 2018, Cape Town endured its most severe drought on record. Yet, resource management often occurs in isolation, which contrasts with the holistic perspective provided by the nexus concept that recognizes the interdependence of resource sectors. This study employs system dynamics modeling, to examine the City of Cape Town’s (CoCT) water-food trade-offs and interactions using qualitative and quantitative approaches. It assesses various policies proposed by the CoCT, to improve system resilience and to boost future water supplies, examining their efficacy and potential drawbacks. These policies are tested against future scenarios including population growth and climate change predictions of different severities.ResultsThe simulation results show an increase in food demand, which is mainly linked to population growth and a significant decrease in water availability. Without intervention, the CoCT is expected to experience serious water shortages within the 40-year simulation period.ConclusionsThe findings indicate that the CoCT’s strategies will effectively secure adequate water for its expanding population. However, a major concern was found to be the proposed intensification of aquifer exploitation. The model predicts that such an approach could lead to overabstraction of some aquifers, compromising their sustainability.