System Dynamics Research Articles

Global study of long term heart rhythm synchronization in groups.

Heart rhythm measurements over time reflect important elements of Autonomic Nervous System dynamics. Synchronization among the heart rhythms of multiple participants has been observed, but this study uses the first global dataset collected that measures synchronization at several locations across the globe continuously for multiple weeks. For 15 days, 104 participants located in California (USA), Lithuania, Saudi Arabia, New Zealand, and England underwent continuous ambulatory heart rhythm monitoring. They were not instructed to perform tasks together. Significant long-term correlations were observed across participants within the same region, for just the groups in Saudi Arabia and New Zealand. This is surprising, given that each participant has an individualized life and distinct heart rate. In a different analysis using population-mean cosinor, only in these two locations was a significant circaseptan (about 7-day) rhythm observed. It appears that weekly rhythms in these groups partially contribute to correlations, in addition to long-term movements. A hypothesis with supporting evidence is proposed to explain this, that participants in these groups were socially closer than in the other groups. It would then appear that heart rhythms synchronize over the long term due to social connectedness, even when they are not physically near each other or performing tasks together.

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.

Pilot Fatigue Coefficient Based on Biomathematical Fatigue Model

The routine assessment of pilot fatigue is paramount to ensuring aviation safety. However, current designs of pilot fatigue factors often lack the comprehensiveness needed to fully account for the dynamic and cumulative nature of fatigue. To bridge this gap, this study introduces a biomathematical fatigue model (BFM) that leverages system dynamics theory, integrating a dynamic feedback mechanism for fatigue information. The novelty of this approach lies in its capability to continuously capture and model fatigue fluctuations driven by varying operational demands. A comparative analysis with international methodologies for evaluating cumulative fatigue on weekly and monthly scales demonstrates that the proposed BFM effectively reproduces variations in pilot fatigue characteristics. Moreover, the pilot fatigue coefficient derived from the model provides a robust differentiation of fatigue profiles across diverse work types, making it particularly suitable for estimating cumulative fatigue over monthly intervals. This BFM-based approach offers valuable insights for the strategic planning of flight schedules and establishes an innovative framework for utilizing BFMs in fatigue management. By employing a scientifically grounded evaluation method rooted in system dynamics and the BFM, this study rigorously assesses cumulative pilot fatigue, confirming the model’s accuracy in replicating fatigue patterns and validating the efficiency and reliability of the derived fatigue coefficient.

Evolutionary Game Theory: A General Review

This article explores the Evolutionary Game Theory (now EGT), encompassing its historical underpinnings, recent advancements, and future potential. Originating in the 1970s through the pioneering work of John Maynard Smith and George R. Price, EGT leverages game-theoretic concepts to elucidate the evolution of strategies within various populations across biological, economic, and social domains. Notably, recent progress has seen the integration of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 into agent-based simulations, thereby enriching the authenticity and intricacy of strategic interactions. Additionally, the study addresses the complexities associated with modeling diverse behaviors and bridging the insights derived from LLMs to practical applications in fields like biology, healthcare, education, and social sciences. Furthermore, it underscores the significance of interdisciplinary collaboration and innovative methodologies in addressing the multifaceted challenges within EGT. Finally, the article contemplates the potential avenues for future research, emphasizing the fusion of EGT with real-world applications and the necessity for comprehensive models that encompass the complexities of evolutionary dynamics in adaptive systems.

Six-scroll chaos within the dynamics of the Thomas chaotic system and application to biomedical data encryption

Abstract In this paper, the influence of non-monotonic nonlinearity within the dynamics of the Thomas model
is studied. The system presents various six-scroll chaos over a wide range of parameter values through indepth analysis using conventional nonlinear analysis tools. The new model also reveals multistability with
an elegant configuration of up to nine periodic states. An electronic analog version of the model is designed
and then simulated using PSPICE software to verify the physical implementation of the model. A novel
biomedical image encryption scheme based on the six-scroll chaos system, Arnold transform and diffusion
operation is proposed and its security is analyzed using statistical testing and key space analysis. The results
demonstrate the effectiveness of the proposed system in providing secure and efficient encryption of digital
images.

Important but Neglected: Job Description of Community Health Workers in the Eastern Cape: A Qualitative Study

Introduction The role of community health workers (CHWs) in improving health system dynamics in developed and developing countries is gaining global attention. Because they connect the community to the health system, knowing their roles is crucial. This study explores CHWs' perceptions on their job description and activities, challenges, and the relevance of their roles. Methods This qualitative descriptive study included 10 interviews and 13 focus groups of CHWs. A semi-structured interview guide was used. Atlas.i was used to perform thematic content analysis. Results CHWs educated and advocated for health issues like HIV, TB, and healthy lifestyles. CHWs experience discrimination, assault susceptibility, lack of training and skill, local dialect constraint, and distrust. CHWs believed their services were needed and valued by their communities. Overall, CHWs were satisfied with their jobs and applauded the program. Conclusion The findings showed that CHWs are aware of their duties and perform them with enthusiasm and commitment regardless of challenges they face, and they are appreciated by the communities they serve. Our findings have implications for enhancing CHW program practise, policies, and future research recommendations, underlining the need of addressing the issues experienced by the clients and communities they served, since this impacts the quality of their work.

Dynamics inventory decisions in perishable supply chain considering sudden change in demand and risk attitude

To elucidate the impact of decision-makers’ risk preferences on the perishable-goods supply chain system during demand fluctuations, this study utilizes prospect theory and system dynamics to develop a simulation model that incorporates decision-makers’ risk attitudes. By solving this model with different parameter settings, the Lyapunov exponent is obtained to determine the system’s stability domain under different scenarios. Retailer losses are evaluated using spoilage conversion rates and fresh product stockouts, shedding light on the relationship between system stability and retailer losses. The research aims to explore the optimal ordering strategy under demand fluctuations. The findings indicate that inappropriate inventory strategies, coupled with decision-makers’ risk attitudes, can lead to system oscillations and substantial losses. Conversely, an appropriate inventory strategy coupled with moderate risk aversion can help mitigate retailer losses.

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Optimizing IoT Networks in Smart Agriculture Using Probabilistic Models and Machine Learning Algorithms

This study aims to enhance the performance of Internet of Things (IoT) networks in smart agriculture by integrating probabilistic models and machine learning algorithms. The research addresses the need for efficient, reliable, and scalable IoT infrastructures to support precision farming and sustainable agricultural practices in increasingly complex and data-intensive farming environments. A multi-faceted approach was employed, combining probabilistic modeling and machine learning techniques. IoT sensor networks were simulated using real-world agricultural data, supplemented by a small-scale field deployment. Bayesian networks and Hidden Markov Models were applied to capture system dynamics, while Random Forest, Support Vector Machines, and Long Short-Term Memory networks were used for pattern recognition and predictive analytics. Network performance was evaluated using metrics including latency, throughput, packet loss rate, energy efficiency, scalability, reliability, and data accuracy. The integrated approach significantly improved IoT network performance in agricultural settings. Key improvements include: 25% reduction in latency, 35% increase in throughput, 60% decrease in packet loss rate, 30% improvement in energy efficiency, and 6.5% increase in data accuracy. Adaptive routing algorithms, informed by machine learning predictions, were particularly effective in managing network load during peak agricultural seasons, reducing latency by 18% during these periods. The study relied primarily on simulations and limited field trials. Comprehensive real-world implementation across diverse agricultural environments and larger scale deployments is necessary to fully validate the findings. The research did not address potential cybersecurity challenges in optimized IoT networks, which remains an important area for future investigation.

Qualitative Aspects of System Dynamics Modeling

AbstractIt is difficult to imagine a system dynamics study without any qualitative aspects. Defining the dynamic problem of interest, representing the causal structure generating the problem's dynamic behavior, and using the model to serve the purpose of the study all involve qualitative processes, elements, and decisions. The 17 papers in this Special Issue illustrate the richness and diversity of qualitative aspects in the field and show how thoroughly qualitative aspects are integrated into every stage of system dynamics practice from problem specification to model formulation and model use. They also present exciting ways people are innovating with qualitative aspects, advancing model use with diverse audiences, and thinking deeply about what policy makers, stakeholders, and clients need to best motivate action. The papers show general agreement about the definitions of “small” qualitative concepts, such as qualitative variables, qualitative data, and qualitative procedures and less agreement about the definitions of “large” qualitative constructs such as qualitative models or qualitative modeling. Overall, this collection lays the groundwork for further reflection about the value and practice of qualitative aspects of system dynamics and suggests some paths forward. © 2024 System Dynamics Society.

Strategic Innovation: Integrating System Dynamics into Management Control

This article explores the concept of strategic innovation, with a focus on its integration with system dynamics in the field of management control. By examining the synergies between these two domains, the article proposes innovative perspectives to enhance decision-making processes and overall business management. The authors highlight the benefits of adopting a systemic approach in management control, emphasizing how it can foster a deeper understanding of complex interactions within organizations. Practical case studies illustrate how this integration can lead to significant innovations, providing managers with valuable tools to navigate in an ever-evolving business environment. Ultimately, the article underscores the strategic importance of merging systems thinking with management control to promote more effective, resilient, and future-oriented business management.

Dinuclear Dysprosium Compounds: The Importance of Rigid Bridges.

We report the synthesis, structures and magnetic behaviour of two isostructural dinuclear Dy3+ complexes where the metal ions of a previously reported monomeric building block are connected by a peroxide (O2 2-) or a pair of fluoride (2×F-) bridges. The nature of the bridge determines the distance between the metal ion dipoles leading to a dipolar coupling in the peroxido bridged compound of only ca. 70 % of that in the bis-fluorido bridged dimer. The sign of the overall coupling between the metals is antiferromagnetic for the peroxido bridged compound and ferromagnetic for the bis-fluorido bridged complex. This in turn influences the magnetisation dynamics. We compare the relaxation characteristics of the dimers with those of the previously reported monomeric building block. The relaxation dynamics for the bis-fluorido system are very fast. On the other hand, comparing the properties of the monomer, the peroxido bridged sample and the corresponding Y-doped sample show that the relaxation properties via a Raman process have very similar parameters. We show that a second dysprosium is important for either tuning or detuning the Single Molecule Magnet (SMM) properties of a system.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Treinta años de la Ley General de Educación colombiana: un planteamiento sobre la autonomía estudiantil

On February 8, 2024, Colombia celebrated the 30th anniversary of the enactment of the General Education Law, which places autonomy as one of its core principles. The gap between what the law stipulates regarding this principle, and the realities experienced in some classrooms, both in basic and secondary education, as well as in undergraduate and even graduate education, appears to be, at the very least, immense. This discussion aims to reflect on the principle-concept of student autonomy from a philosophical and psycho-pedagogical perspective oriented toward identifying progress in the implementation of the General Education Law in Colombia. Through a qualitative methodology, the concept of autonomy is approached from natural language, philosophy, and legislation. Some successful experiences are described where autonomous student development and teaching and learning processes were achieved. Citizen autonomy is considered the foundation for the strengthening and development of successful, or at least functional, societies, and in this sense, it is shown how, from the experiences and dynamics of the Colombian educational system, the path to forming autonomous individuals is still far from being achieved. It is concluded that rethinking the role of the agents linked to the educational context will be essential if the course is to be changed.

Dynamics of non-Markovian systems: Markovian embedding versus effective mass approach

Dynamics of non-Markovian systems: Markovian embedding versus effective mass approach

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.

Review Paper of Grid-Connected Solar PV Systems With Decoupled Control

This paper presents a decoupled control for grid-connected solar photovoltaic (PV) system. The control framework used in this paper aims not to track the changing maximum power point (MPP) for extracting power at MPP, hence blocking the MPP tracking issue against the effect of solar radiation and temperature. Real power is produced by tracking the control voltage with a zero-dynamics-based controller. A cascaded loop controller with inner loop to regulate the control voltage is incorporated to ensure the produced real power follows the reference power in the presence of power disturbance. The proposed control scheme with the consideration of the inverter's rated voltage shows improved system dynamics. Simulations in various conditions are conducted to prove the effectiveness of the proposed control strategies. CIGRE 13-node benchmark system is used as the case study to evaluate the results of the PV system connected to a more complex power system. It has been shown that the proposed controller allows the total production of the energy under maximum power point conditions and also improves the overall stability of distributed generation connected to the grid. Therefore, it can be implemented to increase the capacity of renewable inverter-based power generation and the power system quality.

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.

Derivation of a generalized Langevin equation from a generic time-dependent Hamiltonian

Abstract The traditional Mori-Zwanzig formalism yields equations of motion, so-called generalized Langevin equations (GLEs), for phase-space observables of interest from the microscopic dynamics of a many-body system governed by a time-independent Hamiltonian using projection techniques. By using time-ordered propagators and time-independent projection operators, we derive the GLE for a scalar observable from a generic time-dependent Hamiltonian. The only restriction in our derivation is that the time-dependent part of the Hamiltonian and the observable of interest depend on spatial phase-space variables only. If the observable obeys Gaussian statistics and the time-dependent part of the Hamiltonian can be expressed as an odd power of the observable, the friction memory kernel in the GLE becomes proportional to the second moment of the complementary force, as is the case for a time-independent Hamiltonian in the Mori-Zwanzig formalism.