Search Results (71)

Building a causal-loop diagram (CLD) is central to system-dynamics modeling but demands domain insight, the mastery of CLD notation, and the ability to juggle AI, mathematical, and execution tools. Pipeline Algebra (PA) reduces that burden by treating each step—LLM prompting, symbolic or numeric computation, algorithmic transforms, and cloud execution—as a typed, idempotent operator in one algebraic expression. Operators are intrinsically idempotent (implemented through memoization), so every intermediate result is re-used verbatim, yielding bit-level reproducibility even when individual components are stochastic. Unlike DAG (directed acyclic graph) frameworks such as Airflow or Snakemake, which force analysts to wire heterogeneous APIs together with glue code, PA’s compact notation lets them think in the problem space, rather than in workflow plumbing—echoing Iverson’s dictum that “notation is a tool of thought.” We demonstrated PA on a peer-reviewed study of novel-energy commercialization. Starting only from the article’s abstract, an AI-extracted problem statement, and an AI-assisted web search, PA produced an initial CLD. A senior system-dynamics practitioner identified two shortcomings: missing best-practice patterns and lingering dependence on the problem statement. A one-hour rewrite that embedded best-practice rules, used iterative prompting, and removed the problem statement yielded a diagram that conformed to accepted conventions and better captured the system. The results suggest that earlier gaps were implementation artifacts, not flaws in PA’s design; quantitative validation will be the subject of future work. Full article

Introduction: Polio supplementary immunisation activities (SIA) are implemented to rapidly increase vaccination coverage and interrupt the transmission of poliovirus in a specified geographical area. Polio SIA complements routine immunisation and is crucial for the eradication of the disease by increasing population immunity. However, several contextual factors (i.e., implementation determinants) can influence the success or failure of polio SIA implementation; as such, understanding their dynamics can enhance proactive planning for practice improvement. This study aimed to explore and map the contextual factors of polio SIA implementation in the African region using a critical systems thinking approach. Methods: A rapid review of published and grey literature was conducted. The search included the Global Polio Eradication Initiative library for programmatic reports and two databases (PubMed and Google Scholar). Data extraction was performed using a structured tool. Thematic analysis was performed to categorise the identified contextual factors according to the domains and constructs of the Consolidated Framework for Implementation Research (CFIR). Then, a causal loop diagram (CLD) was used to map the linkages between the identified factors. Results: A total of seventy-eight contextual factors across the five CFIR domains were identified: three for innovation, twenty for outer setting, sixteen for inner setting, twenty-six for individuals, and thirteen for the implementation process. A system map of all the factors using CLD revealed multiple contingent connections, with eleven reinforcing loops and four balancing loops. Conclusions: This study identified the multilevel nature of the contextual factors that influence polio SIA, including their dynamics. The integration of CLD and CFIR in this study offers critical insights into the potential feedback loops that exists between the contextual factors which can be used as leverage points for policy and practice improvements, including tailoring strategies to enhance polio campaign implementation effectiveness, especially with the expanded use of the novel Oral Polio Vaccine type 2 (nOPV2) across countries in the region. Full article

Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R² = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding. Full article

Addressing climate change and advancing clean energy transitions demand holistic approaches that capture complex, interconnected system behaviors. This review focuses on the application of causal loop diagrams (CLDs) as a core systems-thinking methodology to understand and manage dynamic feedback within environmental, social, and technological domains. CLDs visually map the reinforcing and balancing loops that drive climate risks, clean energy adoption, and sustainable development, offering intuitive insights into system structure and behavior. Through a synthesis of empirical studies and case examples, this paper demonstrates how CLDs help identify leverage points in renewable energy policy, carbon management, and ecosystem resilience. Despite their strengths in simplifying complexity and enhancing stakeholder communication, challenges remain—including data gaps, model validation, and the integration of diverse knowledge systems. The review also examines recent innovations that improve CLD effectiveness, such as hybrid modeling approaches and digital tools that enhance transparency and decision support. By emphasizing CLDs’ unique capacity to reveal feedback mechanisms critical for climate action and energy planning, this study provides actionable recommendations for researchers, policymakers, and practitioners seeking to leverage systems thinking for transformative, sustainable solutions. Full article

This study aimed to develop a Causal Loop Diagram (CLD) to visualise how urban environment factors impact dementia and cognitive decline, and potential causal mechanisms. In Group Model Building workshops with 12 researchers, a CLD was created to identify factors contributing to cognitive decline, and the dynamic interrelationships between these factors. The factors were classified in nine main themes: urban design, social environment, travel behaviours, urban design by-products, lifestyle, mental health conditions, disease/physiology, brain physiology, and cognitive decline outcomes. Five selected feedback loops illustrated some dynamics in the system. The workshops helped develop a shared language and understanding of different perspectives from an interdisciplinary team. The CLD creation was part of a comprehensive modelling approach based on experts’ knowledge which informed other research outputs such as an evidence gap map and an umbrella review, helped the identification of environmental variables for future studies and analyses, and helped to identify future possible systems-based interventions to prevent cognitive decline. The study highlights the utility of CLDs and Group Model Building workshops in interdisciplinary research projects investigating complex systems. Full article

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

Global refugee flows’ increasing scale and complexity pose significant challenges to national education systems. Turkey, hosting one of the largest populations of refugees and individuals under temporary protection, faces unique pressures in ensuring equitable educational access for refugee students. Addressing these challenges requires a shift from linear, fragmented interventions toward holistic, systemic approaches. This study applies a Community-Based System Dynamics (CBSD) methodology to explore the systemic barriers affecting refugee students’ participation in education. Through structured Group Model Building workshops involving teachers, administrators, and Non-Governmental Organization (NGO) representatives, a causal loop diagram (CLD) was collaboratively developed to capture the feedback mechanisms and interdependencies sustaining educational inequalities. Five thematic subsystems emerged: language and academic integration, economic and family dynamics, psychosocial health and trauma, institutional access and legal barriers, and social cohesion and discrimination. The analysis reveals how structural constraints, social dynamics, and individual behaviors interact to perpetuate exclusion or facilitate integration. This study identifies critical feedback loops and leverage points and provides actionable insights for policymakers and practitioners seeking to design sustainable, systems-informed interventions. Our findings emphasize the importance of participatory modeling in addressing complex societal challenges and contribute to advancing systems thinking in refugee education. Full article

Over 85% of buildings in the European Union were constructed before 2001, contributing to energy inefficiencies, material waste, and increasing socio-economic disparities. While deep energy renovations (DER) are critical to EU climate goals, their implementation remains hindered by financial, regulatory, and social barriers. Integrating circular economy (CE) principles into DER offers a pathway to enhance resource efficiency and sustainability yet requires a systemic understanding of feedback dynamics. This study applies a systems-thinking approach to examine the interdependencies influencing CE-DER implementation. Five thematic clusters—technical enablers, economic and policy barriers, social sustainability factors, environmental considerations, and digitalization for climate resilience—are identified, informing the development of causal loop diagrams (CLDs). The CLDs reveal key reinforcing loops such as innovation investment, policy learning, stakeholder co-design, operational efficiency, and balancing loops, including certification bottlenecks, financial fragmentation, and digital resistance. The findings suggest that CE-DER success relies on activating reinforcing dynamics while addressing systemic constraints through coordinated financial incentives, ethical digitalization, and inclusive governance. By visualizing interdependencies across technical, social, and policy domains, the feedback-oriented framework developed provides actionable insights for advancing socially equitable, resource-efficient, and climate-resilient renovation strategies. Full article

Workplace integration in hierarchical collectivist societies is shaped by structured social mechanisms rather than collectivist values alone. While collectivism is often assumed to foster inclusiveness, its structural manifestations regulate workplace inclusion through feedback loops of hierarchical loyalty, trust building, and kinship-based exclusivity. This study employs causal loop diagrams (CLDs) to conceptually map how cultural structures regulate workplace inclusion—not to assert empirical causality, but to illustrate the culturally grounded feedback loops in Indonesia and the Philippines. The findings identify the reinforcing loops that sustain hierarchical exclusivity in Indonesia and a counterbalancing loop that facilitates immediate kinship-based trust in the Philippines. By conceptualizing workplace inclusion as an emergent property of interdependent social mechanisms, this study highlights how structured exclusivity stabilizes hierarchical workplaces while limiting adaptability. Unlike frameworks that treat collectivism as a static cultural trait, CLDs provide a dynamic lens to analyze how workplace inclusion evolves through structured feedback loops—revealing how structured exclusivity in collectivist systems governs trust, inclusion, and legitimacy not through ideology alone, but through relational sponsorship, time-dependent trust, and group-based gatekeeping. These insights contribute to cross-cultural management and organizational studies by demonstrating how structured exclusion functions as a self-reinforcing mechanism. The findings have implications for multinational corporations, policymakers, and organizational leaders seeking to design adaptive strategies for workplace integration in hierarchical collectivist environments. While both countries are analyzed, Indonesia serves as the primary site of investigation, with the Philippines providing a contrast to illuminate structured exclusivity mechanisms in hierarchical collectivist contexts. Full article

The construction industry faces persistent challenges from disputes and claims, leading to delays, cost overruns, and strained stakeholder relationships. This study proposes a strategic framework that integrates building information modeling (BIM) as a proactive tool for dispute avoidance. Using a causal loop diagram (CLD), the research maps the relationships among systemic factors contributing to disputes, such as poor communication, ambiguous specifications, and ineffective stakeholder engagement. The study highlights BIM’s transformative potential in enhancing visualization, improving collaboration, and fostering proactive conflict resolution. Validated through expert insights, the framework provides actionable recommendations for integrating BIM (with ISO19650 specs) into construction workflows, addressing the root causes of disputes, and driving project efficiency. This research contributes a structured roadmap for advancing construction management practices, emphasizing early BIM adoption considered with ISO19650, stakeholder alignment, and balancing systemic dynamics. The findings underscore BIM’s pivotal role in reshaping conflict prevention strategies, paving the way for sustainable and dispute-free project delivery. Full article

Households account for 20–40% of carbon emissions in urban areas, making them critical to achieving urban sustainability. Integrating smart technologies in households offers a promising pathway to enhance energy efficiency, mitigate climate change, and support the transition from Smart Cities to Sustainable Smart Cities (SSCs). However, achieving this transition requires not only technological adoption but also behavioural shifts that influence energy consumption—a gap in existing studies. This study examines how household engagement with smart technologies impacts behavioural change and systemic sustainability transitions. Using the Multi-Level Perspective (MLP) framework enriched with System Thinking through Causal Loop Diagrams (CLDs), qualitative data were collected via 11 household interviews exhibiting varying engagement levels. The findings revealed three household-regime dynamics: proactive households driving systemic change through innovation, moderately engaged households contributing to regime stability with financial incentives fostering gradual adoption, and resistant households reinforcing existing structures due to privacy concerns. By extending the MLP framework to incorporate behavioural and social dimensions, the study provided insights into how micro-level behaviours influence macro-level transitions, challenging techno-centric narratives. The findings underscore the need for policies that enhance awareness, address privacy concerns, and provide tailored incentives to catalyse smart technology adoption and energy efficiency, fostering a more inclusive and effective pathway toward sustainable urban futures. Full article

Groundwater, a crucial natural resource on a global scale, plays a significant role in Texas, impacting various essential ecosystem services either directly or indirectly. Despite efforts of state- and community-level regulations and conservation efforts, there is an ongoing trend of declining groundwater levels in the state of Texas. In this study, we utilized the systems thinking and system dynamics modeling approach to better understand this problem and investigate possible leverage points to achieve more sustainable groundwater resource levels. After conceptualizing a causal loop diagram (CLD) of the underlying feedback structure of the issue (informed by the existing literature), a small system dynamics (SD) model was developed to connect the feedback factors identified in the CLD to the stocks (groundwater level) and flows (recharge rate and groundwater pumping) that steer the behaviors of groundwater systems across time. After completing model assessment, experimental simulations were conducted to evaluate the current state relative to simulated treatments for improved irrigation efficiency, restricted pumping rates, cooperative conservation protocols among users, and combination strategy (of all treatments above) in the long-term. Results showed that groundwater stress (and the associated repercussions on related ecosystem service) could be alleviated with a combination strategy, albeit without complete groundwater level recovery. Full article

In the context of growing environmental concerns and the increasing impact of the manufacturing sector on sustainability, this paper introduces the concept of “Product 4.0” (P4.0) as a novel approach to harnessing the potential of Artificial Intelligence (AI) within Industry 4.0 (I4.0) technologies. P4.0 focuses on optimizing the performance of the product throughout its lifecycle and improving recovery strategies at End of Use (EoU) and End of Life (EoL) stages. Through a comprehensive review of the literature, this study identifies critical gaps in the current application of AI within I4.0 for sustainable manufacturing, particularly in regard to smart product systems and their interactions with external environments. To address these gaps, the paper proposes a holistic approach for the P4.0 that leverages AI-driven data analysis and decision making to facilitate efficient product recovery and resource utilization. Additionally, a Causal Loop Diagram (CLD) model is developed to illustrate the relationships between sustainability dimensions—environmental, economic, and social—and product demand influenced by P4.0, while also discussing the challenges and limitations associated with its implementation. By bridging theoretical insights with practical recovery solutions, this research contributes to the sustainable manufacturing discourse and offers actionable directions for future investigations into AI-enhanced P4.0 applications within the manufacturing industry. Full article

(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. Full article

Intelligent Robotic Process Automation (IRPA) combines Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate complex unstructured tasks, improve decision-making, and cope with changing scenarios. A process analysis framework for IRPA adoption was developed by identifying key factors through a literature review and semi-structured expert opinion survey. The employed experts in the survey comprised RPA/IRPA consultants, RPA/IRPA initiative team leaders, and RPA/IRPA developers with three years or more experience. For the initial factor collection phase, there were a total of eighteen (18) responses, and for the factor evaluation phase, a total of twenty-six (26) experts were used to collect responses. Identified factors were shortlisted and evaluated using a Relative Importance Index (RII) analysis. The study’s findings are presented through a Causal-Loop Diagram (CLD) to illustrate the relationships between factors. The framework provides practical guidance for organizations planning to adopt IRPA, informing decision-making, resource allocation, and strategy development. The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption. Three major secondary factors were identified: digital data input, integration with existing systems, and the cost of adopting new technologies. This research contributes to the added value to existing knowledge and serves as a foundation for future research in IRPA adoption. Full article