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AbstractFounded in the mid‐1950s, the field of system dynamics has intellectual roots reaching much further into the past. The Archives section of the Review seeks to publish material from that past which can contribute to current theory and practice. The section welcomes previously unpublished but deserving system dynamics work, classics from past system dynamics literature that should receive renewed attention, and previously published articles from other disciplines of particular significance to current system dynamicists. Contributions emphasizing the philosophy and theory of model building, validation, implementation, education, and generic structures are particularly encouraged. Submissions may range in length from notes to main articles but may be edited. Send suggested material, together with a brief introduction placing it in historical and current contexts, to John D. Sterman, System Dynamics Group, M.I.T. E40‐294, Cambridge, MA 02139, U.S.A.

Abstract"Tackling the Root Causes Upstream of Unhealth Urban Development" is a trans‐disciplinary research project seeking to map and understand urban development decision‐making, visualise stakeholder mental models and codevelop improvement interventions. The project's primary data was gathered through 123 semistructured interviews. This article applies, compares, and discusses four variations on a method for constructing causal loop diagrams to illuminate mental models and collective decision‐making, based on manual and semiautomated processes applied to individual interview transcripts and datasets collected by thematic analysis. It concludes that while semiautomated approaches offer some time saving over manual approaches when applied to large data sets, care is required in interpreting and including peripheral contextual variables at the boundaries of the thematic analysis. Decisions regarding automation depend on the purpose of the modelling. Finally, the article recommends future applications record quantitative descriptors characterising the process of constructing CLDs from large qualitative data sets. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Penelitian ini bertujuan untuk menganalisis pelaksanaan pelayanan izin usaha perdagangan pada Kantor Pelayanan Perizinan Terpadu Kota Padangsidimpuan, dengan menggunakan metode penelitian deskriptif kualitatif. Informasi dan data dikumpulkan dengan wawancara, observasi, dan studi dokumen. Landasan teori yang digunakan dalam penelitian ini adalah teori berpikir serba sistem (system thinking), yaitu untuk memahami suatu perubahan dalam lingkungan yang serba kompleks Salah satu metode analisis dalam system thinking adalah Causal Loop Diagram (CLD). Pemodelan ini dapat digunakan untuk memahami struktur yang terdapat didalam sebuah sistim, hubungan diantara masing-masing komponen, dan bagaimana perubahan diberbagai bidang akan mempengaruhi unsur dan sistim secara keseluruhan dalam mengukur dan memprediksi sistim perilaku.Hasil analisis CLD menunjukkan bahwa : 1. kontribusi yang paling tinggi terhadap kualitas pelayanan izin usaha perdagangan adalah variable kepuasan masyarakat , dan pada peingkat kedua ditempati oleh anggaran sedangkan pada peringkat ketiga, ada pada kebijakan pimpinan Kepuasan masyarakat sebagai aspek yang fundamental yang menentukan peringkat kualitas pelayanan, ditemukan adanya berbagai masalah pada lima (5) dimensi kualitas pelayanan yaitu pada dimensi tangible, reliability, responsiveness, empathy, dan assurance. Penyebab terbesar dari kelemahan ke lima dmensi kualitas pelayanan tersebut diatas terletak pada kualitas sumber daya manusia dan kurangnya pemeliharaan sarana pelayanan. Kontribusi anggaran sebagai peringkat ke -2 dalam kontribusi terhadap kualitas pelayanan, ternyata masalah utamanya ialah ketepatan alokasi anggaran untuk menunjang penyelenggaraan kebijakan kualitas pelayanan . Kebijakan pimpinan pada peringkat ketiga secara langsung mempengaruhi pola perilaku yang berorientasi kepada komitmen untuk meningkatkan kualitas pelayanan.Berdasarkan hasil analisis Causal Loop Digram, disarankan agar :a.Kebijakan eksekutif diarahkan kepada pengalokasian anggaran untuk pengembangan kelembagaan, peningkatan kompetensi sumber daya manusia, dan melakukan pengukuran kinerja untuk mendapatkan sumber daya manusia yang tepat.b.Memaksimalkan akuntabilitas pelayanan publik baik dari segi organisasional atau administrative, politik, legalitas atau hukum, profesionalisme , dan juga aspek moral didalam memberikan kepuasan masyarakat secara akuntabelc. Mengembangkan jiwa kewirausahaan, jiwa inovatif dalam menghadapi persaingan regional maupun global.

Well-managed logistics activities will support the economic sustainability of a region. There are three important elements that need to be improved to create an efficient and efficient flow of goods distribution in urban areas, namely transportation facilities, actors, and other matters related to the urban context. At the same time, these elements must also be synergized with local government policies involving economic, transportation, infrastructure and environmental issues. This research will develop Smart Urban Logistics in DKI Jakarta, as the area with the highest congestion. The purpose of this study is to recommend a smart urban logistics model in the supply chain of goods in DKI Jakarta and to develop a smart urban system policy scenario in DKI Jakarta. Meanwhile, the urgency of this research is to contribute to increasing the mobility of goods and values. This research also has implications for sustainable mobility/mobility in the supply chain of goods in DKI Jakarta Province. In this study, the Causal Loop Diagram (CLD) approach was used to answer the research objectives. CLD helps understand what to do and where to start a system, which in this study is smart goods logistics. Although the Causal Loop Diagram (CLD) approach has helped to determine the identification, further research is needed to simulate a system similar to the actual system.

AbstractAs a field, system dynamics has made more progress in developing formal model evaluation methods that use quantitative data than ones that use qualitative data. This article describes a formal method for evaluating a causal loop diagram (CLD) in late‐stage conceptualization – referred to as rigorously interpreted quotation analysis – and illustrates its application in a case study. The method uses a systematic and explicit interpretive process to confirm or disconfirm all diagram elements in a CLD by comparing it to stakeholders' verbatim descriptions of their experiences in a complex dynamic situation. In so doing, this method enables the resolution of discrepancies between a CLD and qualitative data, building confidence in the structural aspects of a dynamic hypothesis. It does so via a process that is approachable for experts and stakeholders alike. © 2022 System Dynamics Society.

AbstractTransparency is a critical aspect of systems science. While transparency of quantitative models has been assessed, transparency of their qualitative structures has been less scrutinized. We assess the transparency of causal loop diagrams (CLDs), a key qualitative visualization tool in system dynamics. We evaluate System Dynamics Review (SDR) publications and a sample of most‐cited comparable articles in other journals. We assess the inclusion of a plain‐language methods statement, overall discernibility of the methods, and identification of causal link sources. Reviewing 72 articles (SDR: 36; other journals: 36), only 44%, 38%, and 25% fully satisfy each criterion, respectively. SDR articles are characterized by higher transparency in the clarity of CLD development method and communication of causal link sources, yet the potential for enhancement is evident. We provide specific recommendations to increase the transparency of CLDs. Transparent reporting benefits original research authors, future expansion of CLDs, and the systems science community. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

The capacity to engage in research, evaluation and evidence-informed decision-making supports effective public health policy and practice. Little is known about partnership-based approaches that aim to build capacity across a system or how to evaluate them. This study examines the impacts of a research and evaluation capacity building partnership called the Western Australian Sexual Health and Blood-borne Virus Applied Research and Evaluation Network (hereafter, SiREN). SiREN aims to strengthen capacity across a system of clinical and medical services and government and non-government organizations. These organizations are connected through their shared aim of preventing and managing sexually transmissible infections and blood-borne viruses. To examine SiREN, systems concepts and methods were used. Data were collected from SiREN organizational documents (n = 42), a survey tool (n = 104), in-depth interviews (n = 17), a workshop and three meetings with SiREN stakeholders and used to develop two causal loop diagrams. Findings show engagement with SiREN was influenced by a complex interplay of contextual (e.g., organizational capacity) and process (e.g., presence of trusting relationships) factors. SiREN contributed to system level changes, including increased resources for research and evaluation, the development of networks and partnerships that led to more efficient responses to emerging health issues, evidence sharing, and sustainable research and evaluation practice. The use of causal loop diagrams enabled the identification of key leverage points that SiREN can use for continuous improvement or evaluation. The focus on how contextual factors influenced SiREN's ability to create change provides valuable information for researchers, policymakers or practitioners seeking to develop a similar partnership.

Scholarly research on peace has overwhelmingly focused on negative peace, or the absence of conflict, aggression, violence, and war. We seek to understand holistic peace systems, the political, economic, and social systems that sustain peaceful societies. We show how two methods can help us understand the properties and dynamics of such complex peace systems. Each method provides insights from different perspectives to help understand sustaining peace. The causal loop diagram helps us to identify the peace factors and the connections between them. The mathematical model helps us determine the quantitative results of the interactions between all the peace factors. Using these methods, we found that there is no single "leverage" factor that is the lynchpin in creating sustainable peace. Rather, the small effects of a large number of positive peace factors that support peace can collectively overcome the stronger emotional response to the negative conflict factors that jeopardize peace.

AbstractRealist evaluation is an approach with a strong emphasis on causal mechanisms and the context in which they are triggered. However, recent reviews of published realist evaluations show that context is often understudied. This is problematic, as a thorough understanding of the relationship between context and causal mechanisms is crucial in assisting policymakers to make appropriate and targeted decisions that improve the intervention. Therefore, we set out to test whether combining realist evaluation with the "systems thinking" approach and, more specifically, causal loop diagramming, could help strengthen the analysis of context. We did this through a study of a performance‐based financing (PBF) intervention in the Ugandan health care sector by the Belgian development agency, Enabel. PBF allocates funds to health workers and/or health facilities based on their performance, and introduces additional management support tools, provides extra monitoring and supervision, and promotes community participation in management issues, among other activities. In this case, we found that the proposed combined methodological approach indeed adds value to the analysis, as it leads to insights into the role played by the underlying system that otherwise may have been overlooked. Moreover, such information may provide clear directions to policymakers on how to improve the intervention in a sustainable way. Finally, causal loop diagrams help to visualize complex causal interactions and to communicate them to policymakers.