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System Thinking is a common concept for understanding how causal relationships and feedbacks work in an everyday problem. Understanding a cause and an effect enables us to analyse, sort out and explain how changes come about both temporarily and spatially in common problems. This is referred to as mental modelling, i.e. to explicitly map the unders...
Contexts in source publication
Context 1
... instance, if we are investigating grass growth in relation to herbivores, climate would be considered as an external influencing factor on the system but herbivores an internal factor within the system. What is interesting from figure 13 is that variables describe different action and events. If the question was; how do different alternative waste management reduce the solid waste production, then we can see that the internal variables are moving in a short timescale (daily) as external variables are moving on a yearly basis, e.g. ...
Context 2
... instance, if we are investigating grass growth in relation to herbivores, climate would be considered as an external influencing factor on the system but herbivores an internal factor within the system. What is interesting from figure 13 is that variables describe different action and events. If the question was; how do different alternative waste management reduce the solid waste production, then we can see that the internal variables are moving in a short timescale (daily) as external variables are moving on a yearly basis, e.g. ...
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Background
Systems thinking is an approach that views systems with a holistic lens, focusing on how components of systems are interconnected. Specifically, the application of systems thinking has proven to be beneficial when applied to health systems. Although there is plenty of theory surrounding systems thinking, there is a gap between the theore...
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Citations
... According to Hoogervorst (2018), the enterprise poses high levels of uncertainty, which require additional JM2 mechanisms to understand "what to do" to improve performance. SD can provide a structured approach to understanding and modelling the complex interdependencies and feedback loops that exist within and from outside the enterprise system (Haraldsson, 2004). SD techniques, particularly "what-if" analysis, can also offer decision-makers valuable tools for predicting the broader consequences of process execution modifications (Pourbafrani et al., 2019). ...
Purpose
This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.
Design/methodology/approach
A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.
Findings
The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.
Originality/value
The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.
... Thereafter, a causal loop diagram (CLD) was developed based on secondary data extracted from selected literature to discern nature of disorder expression in terms of multifactorial associations. CLD involves the extensive mapping of the feedback loops within a defined system (Haraldsson, 2004). IDs in mango were considered an output of a complex system and direct and indirect effects of various factors positively or negatively influencing the expression of IDs were mapped using Stella Architect systems modelling software considering its user-friendly interface and recognised iconography (Walters et al., 2016). ...
The expression of internal disorders is a complex phenomenon due to the interaction of genotype, environment, and management practices at the pre-harvest, harvest, and post-harvest stages. This study aims to provide a comprehensive overview of factors associated with, and have been investigated in relation to, the susceptibility and expression of internal disorders in mango. In this context, a systematic literature review was conducted to identify, extract, and analyse the most pertinent data. Six hundred and fifty-seven studies were sourced from three databases. Of these, 72 were relevant after screening based on exclusion-inclusion criteria. An additional nine relevant studies were identified through manual searches and included in the final data synthesis. From the 81 studies, the prevalent disorders were spongy tissue and jelly seed, followed by flesh browning and soft nose. ‘Alphonso’ expressed only spongy tissue disorder. By contrast, ‘Tommy Atkins’ simultaneously expressed multiple disorders, viz., soft nose, jelly seed, and stem-end cavity. Mineral composition of flesh and exposure to heat post-harvest (viz., sunlight, phytosanitary heat treatment, and elevated storage temperature) were prominent in association with IDs expression. Relatively high N, N/Ca, Mg/Ca, and K/Ca ratios and relatively low Ca and B were related to most disorders. With K and Mg, studies inconsistently suggested positive and inverse relationship with a particular disorder. While individual studies suggest casual relationships, there was a relative dearth of pre-harvest work on soil characteristics, growing conditions (e.g., temperature, rain, and VPD), crop physiology (e.g., age and biennial bearing) and management practices (e.g., fertilisation and irrigation) in the literature. This review provides a benchmark against which future research might adopt holistic approaches to contiguously ascertain predisposition of mango fruit to internal disorders across the pre-harvest, harvest, and post-harvest continuum.
... We conceived both causal loop diagrams based on research experience on ecosystem restoration in Rwanda (BK, JF, MF) and Germany (MPR, VT, JF). Here, we focused on the most important system components that substantially shape dynamics in the respective restoration system (Haraldsson 2004). ...
... To design such causal loop diagrams, elements which have a significant influence on the system need to be identified, the relationships between these elements need to be understood, and the feedbacks between elements need to be analysed. This is best done in cooperation with diverse stakeholders with in-depth knowledge of the system in question (Haraldsson 2004). Next, based on such a detailed contextual understanding, future-oriented methods such as scenario planning or the threehorizons method (Sharpe et al. 2016) can help to define future restoration activities that account for both ecological and social ambitions of diverse groups of interest in a transdisciplinary way. ...
Expanding in both scope and scale, ecosystem restoration needs to embrace complex social–ecological dynamics. To help scientists and practitioners navigate ever new demands on restoration, we propose the “social–ecological ladder of restoration ambition” as a conceptual model to approach dynamically shifting social and ecological restoration goals. The model focuses on three dynamic aspects of restoration, namely degrading processes, restoration goals and remedial actions. As these three change through time, new reinforcing and balancing feedback mechanisms characterize the restoration process. We illustrate our model through case studies in which restoration has become increasingly ambitious through time, namely forest landscape restoration in Rwanda and grassland restoration in Germany. The ladder of restoration ambition offers a new way of applying social–ecological systems thinking to ecosystem restoration. Additionally, it raises awareness of social–ecological trade-offs, power imbalances and conflicting goals in restoration projects, thereby laying an important foundation for finding more practicable and fairer solutions.
... One of the methods that can be used to assist humans in building a better mental model of an XAI-CF system is to represent the system, including explanations, graphically/visually, such as causal loop, stock, and flow diagrams [177]. Causal loop diagrams (CLDs) [178] can be used to explicitly map an XAI-CF system and make it transparent and visible to others. A CLD consists of three basic elements, boxes, connections, and feedback loops. ...
With the rise of complex cyber devices such as smartphones, Internet of Things, and control systems in automotive and drones, the proliferation of operating systems and file formats, pervasive encryption, use of the cloud for remote processing and storage, and legal standards, Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify and explain the results to the stakeholders of CF, such as forensic analysts and members of the court, for them to make an informed decision. If we want to apply AI successfully in CF, there is a need to develop trust in AI systems. Some other factors in accepting the use of AI in CF are to make AI authentic, interpretable, understandable, and interactive. This way, AI systems will be more acceptable to the public and ensure alignment with legal standards. An explainable AI (XAI) system can play this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and is still in its infancy. In this paper, we explore and make a case for the significance and advantages of XAI-CF. We strongly emphasize the need to build a successful and practical XAI-CF system and discuss some of the main requirements and prerequisites of such a system. We present a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF. To make the reader familiar with the study carried out in this paper, in addition to background, we also present a critical and short review of the works carried out in the last ten years in XAI and CF. We discuss some challenges facing XAI-CF, such as adversarial attacks, bias management, oversimplification, CF and AI chasm, and human-computer interaction. We also provide some concrete solutions to these challenges. We identify key insights and future research directions for building XAI applications for CF. This paper is an effort to explore and familiarize the readers with the role of XAI applications in CF, and we believe that our work provides a promising basis for future researchers interested in XAI-CF.
... The mental model refers to a collection of assumptions that encapsulate the understanding of a complex system. "All models are wrong: but some are useful", it is to be updated continuously [45,46]. In this context, the proposed model focuses on a specific set of 23 variables. ...
Basic reproduction number (R0) is a mathematical expression used in epidemiology to address expected number of secondary cases. R0 helps to predict outbreak diffusion and preventive measures. As African swine fever (ASF) is a viral infectious disease, there are significant studies related to R0 of the ASF outbreak, but most of them are investigated in a zonal and geospecific boundary. This study explores the general overview of African swine fever virus (ASFV) R0 based on existing literature and examines for the global scale using a doubling time approach using wild boar outbreaks. Further, a qualitative mental model is developed to explore the ASF disease dynamics. It was found that the average R0 was 3.56 from existing literature. Based on the global scenario, ASFV spread in wild boar was above the threshold line ( R 0 ≥ 1 ). The recent growth trend ( R 0 = 5.87 ) flagging the very high risk. ASFV is threatening the world. The qualitative mental model highlighted the veterinary services as awareness and R&D support are highlighted as the control measures. This study provides a reference to researchers and veterinarians in setting strategies of developing preventive measures and highlights the importance of awareness programs and R&D support for mitigating the ASFV spread.
... Systems thinking has its roots in mental models and has evolved and is increasingly used to understand complex dynamic systems (CDS) [20]. Framework energetic modeling is commonly used for its effective problem-solving capabilities. ...
... Framework energetic modeling is commonly used for its effective problem-solving capabilities. To further enhance its effectiveness, the introduction of energetic frameworks provides a deeper understanding of emerging issues and behaviors [20]. ...
Climate change is becoming increasingly unpredictable. This also has an impact on agriculture, such as the increase in rice productivity. The internet of things is one of the latest technologies that can help increase land productivity. One of the uses of the Internet of things is to identify the temperature and rainfall. The problems currently faced by farmers are determining planting schedules and pest control that caused the decline in rice production. In this study, IoT will be designed to develop smart agriculture using system thinking to increase agricultural dryland productivity where agriculture in Madura is classified as dryland agriculture with rainfall amounts falling into the low-medium category of 50 -150 mm/day, this also affects the temperature in rice cultivation, so the use of IoT will focus on controlling the temperature, pest and stability of the rice paddy soaking water while planting rice until the harvest process for maximum results. The result of this study is a causal loop diagram of internet-based system thinking that can be used as a recommendation to increase agricultural land productivity with temperature, pest and irrigation control using IoT, especially in the form of sensor technology.
... Additionally to the mathematical analysis of equilibrium points and their stability, we present causal loop diagrams that qual-195 itatively summarize causal links in a system and the feedbacks they create (Haraldsson, 2004). This analysis can help to understand the behaviour a system exhibits after a perturbation around an equilibrium point. ...
... This analysis can help to understand the behaviour a system exhibits after a perturbation around an equilibrium point. In a causal loop diagram, causal connections are depicted by arrows, tying a cause (at the tail of the arrow) to its direct effect (at the head of the arrow) (Haraldsson, 2004). The sign of the causal relation (+ or −) depends on whether an isolated change in one element causes another to change in the same (+) or opposite (−) direction of the initial change (relative to the unchanged state) (Haraldsson, 2004;200 Richardson, 1986). ...
... In a causal loop diagram, causal connections are depicted by arrows, tying a cause (at the tail of the arrow) to its direct effect (at the head of the arrow) (Haraldsson, 2004). The sign of the causal relation (+ or −) depends on whether an isolated change in one element causes another to change in the same (+) or opposite (−) direction of the initial change (relative to the unchanged state) (Haraldsson, 2004;200 Richardson, 1986). For example, a decrease in the microbial uptake rate would lead to relatively less microbial biomass (compared to the case that the uptake rate had not changed), describing a positive causal relation. ...
Microbial-explicit soil organic carbon (SOC) cycling models are increasingly recognized for their advantages over linear models in describing SOC dynamics. These models are known to exhibit oscillations, but it is not clear when they yield stable vs. unstable equilibrium points (EPs) – i.e. EPs that exist analytically, but are not stable to small perturbations and cannot be reached by transient simulations. Occurrence of such unstable EPs can lead to unexpected model behaviour in transient simulations or unrealistic predictions of steady state soil organic carbon (SOC) stocks. Here we ask when and why unstable EPs can occur in an archetypal microbial-explicit model (representing SOC, dissolved OC [DOC], microbial biomass, and extracellular enzymes) and some simplified versions of it. Further, if a model formulation allows for physically meaningful but unstable EPs, can we find constraints in the model parameters (i.e. environmental conditions and microbial traits) that ensure stability of the EPs? We use analytical, numerical and descriptive tools to answer these questions. We found that instability can occur when the resupply of a growth substrate (DOC) is (via a positive feedback loop) dependent on its abundance. We identified a conservative, sufficient condition on model parameters to ensure stability of EPs. Interactive effects of environmental conditions and parameters describing microbial physiology point to the relevance of basic ecological principles for avoidance of unrealistic (i.e. unstable) simulation outcomes. These insights can help to improve applicability of microbial-explicit models, aid our understanding of the dynamics of these models and highlight the relation between mathematical requirements and (in silico) microbial ecology.
... The effects among the variables can be positive (denoted by "+") or negative (denoted by "-") (Delgado-Maciel et al. 2018). CLDs have two types of loops: reinforcing ones that strengthen a behavior, and balancing ones that counteract the effect of a change (Haraldsson 2004). Stock and Flow diagrams consist of four main components: stocks, flows, auxiliary variables, and connectors (Cagliano et al. 2015). ...
... A useful visual tool to represent the complex interactions between changing climatic conditions and health outcomes are causal loop diagrams (CLDs) [50,51]. "Cause and effect" indicates that one element must lead to another either in absolute (deterministic) or probable (stochastic) ways [52]. ...
Abstract:
Introduction:
Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes.
Objectives:
The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate.
Methods:
Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework.
Results and Discussion:
The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change.
Conclusion:
This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.
... According to reference [40] over the past 60 years, system thinking has changed and grown, and it is now influencing science more and more. In order to grasp the patterns and relationships of complex situations, system thinking is a science that works with the organization of logic and integration of disciplines. ...
