SYSTEM DYNAMICS REVIEW系统动力学评论
SYSTEM DYNAMICS REVIEW(英文缩写 SYST DYNAM REV),ISSN 0883-7066,eISSN 1099-1727,中文译名:系统动力学评论 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.040 | Q2 |
| 2022 | 4.800 | Q1 |
| 2023 | 1.700 | Q2 |
| 2024 | 2.500 | Q1 |
| 2025 | 4.300 | Q1 |
SYSTEM DYNAMICS REVIEW 最新收录文献
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1. One Month, Two Projects, Many Lessons: Insights From Concurrent System Dynamics Group Model Building Approaches.
PMID:日期:2025-08-22Although maternal morbidity and mortality are persistent public health burdens in the U.S., few studies have addressed these outcomes from a complex systems perspective. To bridge this knowledge gap, two SD modeling projects that apply community-based system dynamics group model building (SD GMB) are ongoing. With core SD GMB meetings with community participants having taken place within the same calendar month, the close timing of these processes provided a unique opportunity for rapid learning by the research team and the adaptation of the specific approaches employed across projects. Applying tenets of reflexivity, this paper describes how SD GMB activities were conceptualized, planned, and implemented, offering reflections on the effectiveness of our approach by drawing upon our own insights about successes, failures, and apparent enigmas in each project. These reflections are described as five 'lessons' that may offer valuable insights for others engaging in the formative phase of SD GMB projects.
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2. Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics modelling process.
2. 应对软变量和数据稀缺：参与式系统动力学建模过程中量化研究的经验教训PMID:DOI:日期:2024-04-14System dynamics (SD) models are commonly used for structuring complex problems to support decision-making. They are used to investigate areas in which limited knowledge is available, describing nonlinear relationships and including intangible elements. Although this explorative nature is one of the key advantages, it also represents a challenge for quantifying the intangible, i.e. more qualitative aspects of an SD model, especially when it is not possible to apply conventional analytical methods due to data scarcity. Procedures to obtain and analyse information using participatory approaches are limited. First, this article outlines existing quantification methods and related open questions when dealing with soft variables and data scarcity. Secondly, it summarises the quantification process developed during a participatory SD process, describing how we dealt with data scarcity and soft variables. Lastly, we suggest a quantification framework in relation to data availability and level of stakeholder engagement.
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3. Participatory Modeling for High Complexity, Multi-System Issues: Challenges and Recommendations for Balancing Qualitative Understanding and Quantitative Questions.
3. 面向高复杂度、多系统问题的参与式建模：平衡定性理解与定量问题的挑战与建议PMID:DOI:日期:2024-02-22Community stakeholder participation can be incredibly valuable for the qualitative model development process. However, modelers often encounter challenges for participatory modeling projects focusing on high-complexity, synergistic interactions between multiple issues, systems, and granularity. The diverse stakeholder perspectives and volumes of information necessary for developing such models can yield qualitative models that are difficult to translate into quantitative simulation or clear insight for informed decision-making. There are few reccomended best practices for developing high-complexity, participatory models. We use an ongoing project as a case study to highlight three practical challenges for tackling high-complexity, multi-system issues with system dynamics tools. These challenges include balanced and respectful stakeholder engagement, defining boundaries and levels of variable aggregation, and timing and processes for qualitative/quantitative model integration. Our five recommendations to address these challenges serve as a foundation for further research on methods for developing translatable qualitative multi-system models for informing actions for systemic change.
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4. Strengthening a Weak Link: Transparency of Causal Loop Diagrams, Current State and Recommendation.
PMID:DOI:日期:2023-11Transparency 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 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 (: 36; other journals: 36), only 44%, 38%, and 25% fully satisfy each criterion, respectively. 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.
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5. Constructing causal loop diagrams from large interview data sets.
5. 从大型访谈数据集中构建因果循环图PMID:DOI:日期:2023-09-12"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.
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6. Grounding alcohol simulation models in empirical and theoretical alcohol research: a model for a Northern Plains population in the United States.
6. 将酒精模拟模型建立在实证和理论酒精研究的基础上：一个针对美国北部平原人群的模型PMID:DOI:日期:2023-06-13The growing number of systems science simulation models for alcohol use (AU) are often disconnected from AU models within empirical and theoretical alcohol research. As AU prevention/intervention efforts are typically grounded in alcohol research, this disconnect may reduce policy testing results, impact, and implementation. We developed a simulation model guided by AU research (accounting for the multiple AU stages defined by AU behavior and risk for harm and diverse transitions between stages). Simulated projections were compared to historical data to evaluate model accuracy and potential policy leverage points for prevention and intervention at risky drinking (RD) and alcohol use disorder (AUD) stages. Results indicated prevention provided the greatest RD and AUD reduction; however, focusing exclusively on AUD prevention may not be effective for long-term change, given the continued increase in RD. This study makes a case for the strength and importance of aligning subject-based research with systems science simulation models.
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7. Quantifying the COVID-19 endgame: Is a new normal within reach?
7. 量化COVID-19疫情的终局：新常态是否触手可及？PMID:DOI:日期:2022-08-24Eradication of COVID-19 is out of reach. Are we close to a "new normal" in which people can leave behind restrictive non-pharmaceutical interventions (NPIs) yet face a tolerable burden of disease? The answer depends on the ongoing risks versus communities' tolerance for those risks. Using a detailed model of the COVID-19 pandemic spanning 93 countries, we estimate the biological and behavioral factors determining the risks and responses, and project the likely course of COVID-19. Infection fatality rates have fallen significantly due to vaccination, prior infections, better treatments, and the less severe Omicron variant. Yet based on their estimated tolerance for deaths, most nations are not ready to live with COVID-19 without any NPIs. Across the world the increased transmissibility of Omicron, combined with the decay of immunity, leads to repeated episodes of reinfections, hospitalizations, and deaths, complicating the emergence of a new normal in many nations. © 2022 The Authors. published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
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8. Behavioral responses to risk promote vaccinating high-contact individuals first.
PMID:DOI:日期:2022-08-23How should communities prioritize COVID-19 vaccinations? Prior studies found that prioritizing the elderly and most vulnerable minimizes deaths. However, prior research has ignored how behavioral responses to risk of disease endogenously change transmission rates. We show that incorporating risk-driven behavioral responses enhances fit to data and may change prioritization to vaccinating high-contact individuals. Behavioral responses matter because deaths grow exponentially until communities are compelled to reduce contacts, with deaths stabilizing at levels that oblige higher-contact groups to sufficiently cut their interactions and slow transmissions. More lives may be saved by vaccinating and taking those high-contact groups out of transmission chains earlier because the remaining groups will take more precautions while waiting for their turn for vaccination. These findings are especially important considering the need for further vaccination in many countries, the emergence of new variants, and the expected challenge of distributing new vaccines in the coming months and years. © 2022 The Author. published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
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9. Planning, implementing, and evaluating an online group-model-building workshop during the COVID-19 pandemic: celebrating successes and learning from shortcomings.
9. 在COVID-19大流行期间规划、实施和评估在线团队模式构建研讨会：庆祝成功并从不足中学习PMID:DOI:日期:2022-03-22该文献暂无摘要。
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10. Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations.
10. COVID-19的行为动力学：估算92个国家的漏报、多波疫情和遵守疲劳PMID:DOI:日期:2021-03-16Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.