System Dynamics Review最新文献

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Integrating AI language models in qualitative research: Replicating interview data analysis with ChatGPT 在定性研究中整合人工智能语言模型：用 ChatGPT 复制访谈数据分析

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-05-22 DOI: 10.1002/sdr.1772

Mohammad S. Jalali, Ali Akhavan

The recent advent of artificial intelligence (AI) language tools like ChatGPT has opened up new opportunities in qualitative research. We revisited a previous project on obesity prevention interventions, where we developed a causal loop diagram through in‐depth interview data analysis. Utilizing ChatGPT in our replication process, we compared its results against our original approach. We discuss that ChatGPT contributes to improved efficiency and unbiased data processing; however, it also reveals limitations in context understanding. Our findings suggest that AI language tools currently have great potential to serve as an augmentative tool rather than a replacement for the intricate analytical tasks performed by humans. With ongoing advancements, AI technologies may soon offer more substantial support in enhancing qualitative research capabilities, an area that deserves more investigation. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

最近，ChatGPT 等人工智能（AI）语言工具的出现为定性研究带来了新的机遇。我们重新审视了之前一个关于肥胖预防干预的项目，在该项目中，我们通过深入的访谈数据分析绘制了一个因果循环图。我们在复制过程中使用了 ChatGPT，并将其结果与我们最初的方法进行了比较。我们讨论了 ChatGPT 对提高效率和无偏见数据处理的贡献，但它也揭示了上下文理解的局限性。我们的研究结果表明，人工智能语言工具目前具有很大的潜力，可以作为一种辅助工具，而不是取代人类执行的复杂分析任务。随着技术的不断进步，人工智能技术可能很快就会在提高定性研究能力方面提供更多实质性支持，这一领域值得更多研究。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。

引用次数: 0

Preparing for pandemic response in the context of limited resources 在资源有限的情况下准备应对大流行病

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-05-17 DOI: 10.1002/sdr.1775

Jair Andrade, B. Beishuizen, M. Stein, Máire Connolly, Jim Duggan

Pandemics are outbreaks of an infectious disease that increase morbidity and mortality over a wide geographic area. These events require large‐scale interventions from governments and health agencies to curtail transmission within a population. In assessing the impact of these interventions, policymakers avail themselves of simulations from compartmental models. However, it is commonplace that modellers employ fixed fractions to represent interventions, which implicitly assume that resources instantaneously and boundlessly adjust to the desired target and may predict overly optimistic outcomes. Here, we propose a model that integrates four crucial countermeasures subject to resource constraints. We present this structure utilising the concept of small models and frame the simulations in the context of a hypothetical spread of influenza in the Netherlands, drawing on empirical data. After incorporating resource constraints, we find that only a comprehensive response controls the disease. These findings highlight the need for improving strategic plans to support pandemic preparedness. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

大流行病是一种传染病的暴发，它使广大地区的发病率和死亡率上升。这些事件需要政府和卫生机构采取大规模干预措施，以遏制疾病在人群中的传播。在评估这些干预措施的影响时，决策者会利用分区模型进行模拟。然而，建模者通常采用固定分数来表示干预措施，这暗含了一种假设，即资源会瞬时、无限制地调整到预期目标，因此可能会预测出过于乐观的结果。在此，我们提出了一个模型，该模型整合了四种受资源限制的关键对策。我们利用小型模型的概念提出了这一结构，并在假设流感在荷兰蔓延的背景下，借鉴经验数据进行了模拟。在纳入资源限制后，我们发现只有全面的应对措施才能控制疾病。这些发现凸显了改进战略计划以支持大流行病防备的必要性。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。

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引用次数: 0

Developing model‐based storytelling to share systemic insights to the public during the COVID‐19 pandemic 在 COVID-19 大流行期间，开发基于模型的故事讲述方式，向公众分享系统见解

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-05-16 DOI: 10.1002/sdr.1771

Dan Gordon, Ali N. Mashayekhi, Andrada Tomoaia‐Cotisel, Hyunjung Kim, Babak Bahaddin, Luis F. Luna‐Reyes, David F. Andersen

Communicating dynamic insights to a wide audience constitutes a long‐standing challenge among system dynamics modelers. We propose that telling stories is a compelling way of introducing feedback insights, as well as an understanding of how our individual decisions are linked to social behaviors in our day‐to‐day living. These decisions often involve moral and ethical trade‐offs. In this article, we use examples and heuristics to illustrate how the process of building a system dynamics model can drive a storytelling project. In a complimentary manner, the process of storytelling supports and enriches a model building project by exploring new dynamic hypotheses. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

向广大受众传达动态见解是系统动力学建模者长期面临的挑战。我们认为，讲故事是介绍反馈见解以及理解我们的个人决策如何与日常生活中的社会行为相关联的一种令人信服的方式。这些决定往往涉及道德和伦理方面的权衡。在本文中，我们将通过实例和启发式方法来说明建立系统动力学模型的过程是如何推动讲故事项目的。与此相辅相成的是，讲故事的过程可以通过探索新的动态假设来支持和丰富模型构建项目。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。

引用次数: 0

The lowly constant, elevated 卑微的常数，升高的

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-05-15 DOI: 10.1002/sdr.1774

Leonard A Malczynski

引用次数: 0

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT 生成式人工智能和仿真建模：如何（不）使用 ChatGPT 等大型语言模型

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-05-13 DOI: 10.1002/sdr.1773

Ali Akhavan, Mohammad S. Jalali

Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This article endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID‐19's impact on economic growth in the United States. However, our guidelines are generic and can be applied to a broader range of generative AI tools. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method. We emphasize while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

生成式人工智能（AI）工具，如大型语言模型（LLM）和聊天机器人（如 ChatGPT），有望推动仿真建模的发展。尽管生成式人工智能日益突出并引发了相关争论，但在理解生成式人工智能在这一领域的潜力方面仍存在差距，也缺乏有效部署生成式人工智能的指导原则。本文致力于弥补这些不足。我们以模拟 COVID-19 对美国经济增长的影响为例，讨论了 ChatGPT 的应用。不过，我们的指导原则是通用的，可以应用于更广泛的生成式人工智能工具。我们的工作提出了一种系统方法，用于将生成式人工智能整合到整个仿真研究过程中，从问题阐述到见解推导和记录，与具体的仿真建模方法无关。我们强调，虽然这些工具在完善想法和加快进程方面提供了增强功能，但它们应该补充而不是取代研究中固有的批判性思维。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。

引用次数: 0

Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics modelling process 处理软变量和数据稀缺问题：从参与式系统动力学建模过程中的量化工作中汲取的经验教训

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-04-14 DOI: 10.1002/sdr.1770

Irene Pluchinotta, Ke Zhou, Nici Zimmermann

System 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. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

系统动力学（SD）模型通常用于构建复杂问题的结构以支持决策。它们用于研究知识有限的领域，描述非线性关系，并包括无形要素。尽管这种探索性是其主要优势之一，但它也对量化 SD 模型的无形要素，即更多的定性要素提出了挑战，尤其是在由于数据匮乏而无法采用传统分析方法的情况下。利用参与式方法获取和分析信息的程序十分有限。首先，本文概述了处理软变量和数据稀缺问题时的现有量化方法和相关开放性问题。其次，本文总结了在参与式可持续发展过程中开发的量化程序，描述了我们如何处理数据稀缺和软变量问题。最后，我们提出了一个与数据可用性和利益相关者参与程度相关的量化框架。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。

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引用次数: 0

Building confidence in exploratory models 建立对探索性模型的信心

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-04-12 DOI: 10.1002/sdr.1769

George P. Richardson

引用次数: 0

A framework for using Theory of Constraints thinking processes and tools to complement qualitative system dynamics modelling 利用约束理论思维过程和工具补充定性系统动力学建模的框架

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-03-14 DOI: 10.1002/sdr.1768

Victoria J. Mabin, Robert Y. Cavana

While the tools currently used for qualitative system dynamics (Qual SD) modelling are very powerful in providing a holistic perspective and a framework for understanding complexity and change, they are often not explicitly designed to build and implement long‐term solutions based on that understanding. The Theory of Constraints (TOC) thinking processes and tools focus on these important aspects of the decision‐making process. We provide a six‐stage framework combining selected tools from TOC's thinking process and Qual SD's modelling process to provide a more rigorous systems thinking change process. We illustrate the methods and component tools via a case study on a complex societal issue (sale of alcohol in New Zealand supermarkets) developed for teaching purposes. We demonstrate the value of the framework, highlighting complementarities between tools, and show how specific insights emerged using each of the tools, with more insights resulting from using Qual SD and TOC collectively than separately. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

虽然目前用于定性系统动力学（Qual SD）建模的工具在提供整体视角和理解复杂性与变化的框架方面非常强大，但这些工具往往没有明确设计用于在这种理解的基础上建立和实施长期解决方案。约束理论（TOC）思维过程和工具侧重于决策过程的这些重要方面。我们提供了一个六阶段框架，将 TOC 思考过程中的选定工具与 Qual SD 的建模过程相结合，以提供一个更严格的系统思考变革过程。我们通过一个为教学目的而开发的复杂社会问题（新西兰超市的酒类销售）的案例研究来说明这些方法和组成工具。我们展示了该框架的价值，强调了工具之间的互补性，并说明了如何通过使用每种工具获得具体的见解，与单独使用相比，集体使用 Qual SD 和 TOC 获得的见解更多。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学协会出版。

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引用次数: 0

Participatory modeling for high complexity, multi‐system issues: challenges and recommendations for balancing qualitative understanding and quantitative questions 高复杂性、多系统问题的参与式建模：平衡定性理解和定量问题的挑战与建议

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-02-23 DOI: 10.1002/sdr.1765

Arielle R. Deutsch, Leah Frerichs, Madeleine Perry, Mohammad S. Jalali

Community 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 recommended 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. © 2024 System Dynamics Society.

社区利益相关者的参与对于定性模型的开发过程具有极大的价值。然而，建模人员在参与式建模项目中经常会遇到挑战，这些项目侧重于高复杂性、多个问题、系统和粒度之间的协同互动。利益相关者的不同观点和开发此类模型所需的大量信息可能会导致定性模型难以转化为定量模拟或用于知情决策的清晰见解。在开发高复杂性的参与式模型方面，几乎没有推荐的最佳实践。我们将一个正在进行的项目作为案例研究，重点介绍利用系统动力学工具解决高复杂性、多系统问题的三个实际挑战。这些挑战包括平衡和尊重利益相关者的参与、定义变量聚合的边界和水平，以及定性/定量模型整合的时间和流程。我们针对这些挑战提出了五项建议，为进一步研究开发可转化的定性多系统模型的方法奠定了基础，以便为系统变革行动提供信息。© 2024 系统动力学学会。

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引用次数: 0

History of the Beer Game 啤酒游戏的历史

IF 4.8 3区管理学 Q1 Social Sciences

System Dynamics Review

Pub Date : 2024-02-22 DOI: 10.1002/sdr.1767

Ignacio J. Martinez-Moyano

This article describes the history of the Beer Game. By triangulating information from literature, archival analysis, and interviews with experts in the field, the main changes in the game over its almost 70-year history are identified. The article discusses three aspects of the game: 1) its structure (phases of its history, stocks and flows, parameters, etc.); 2) the process for playing the game; and 3) the game debrief. The structure of the Beer Game, and the process for running it, have stabilized over the years into what is now a de facto standard approach. Additional work is needed in the game debrief, specifically in the clarification of key insights and messages (depending on the context of the use of the game), in how to communicate such messages to different audiences, and in the development of support materials for its delivery. © 2024 UChicago Argonne, LLC. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

本文介绍了啤酒游戏的历史。通过对文献资料、档案分析以及与该领域专家的访谈进行三角分析，确定了啤酒游戏在近 70 年历史中的主要变化。文章从三个方面讨论了啤酒游戏：1) 游戏结构（历史阶段、存量和流量、参数等）；2) 游戏过程；3) 游戏汇报。多年来，"啤酒游戏 "的结构和游戏过程已经稳定下来，成为事实上的标准方法。在游戏汇报方面还需要做更多的工作，特别是要明确关键的见解和信息（取决于游戏的使用环境），如何向不同的受众传达这些信息，以及为游戏的实施编写辅助材料。©2024芝加哥阿贡大学有限责任公司。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学协会出版。

引用次数: 0

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System Dynamics Review

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