Conference: Agent-Based Modeling 2017: Agent-Based Models in the Social, Human-Environment, and Life Sciences

会议：2017 年基于主体的建模：社会、人类环境和生命科学中的基于主体的模型

• 批准号: 1638446
• 负责人: Li An
• 金额: $ 9.5万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1638446&HistoricalAwards=false
• 关键词: Conference; Agent; Based; Modeling; 2017

This award will support a workshop that will bring together experts from a broad range of fields to examine and advance the development and use of agent-based models (ABMs) in the social, human-environment, and life sciences. The conference will facilitate community-wide consideration of issues that enhance and broaden the value and utility of agent-based modeling for scholarly and practical purposes. The conference will advance a multi-faceted, community-based examination of current strengths and shortcomings of ABM development and use. The conference and its follow-up products will identify incentives and directions for many related fields to develop more robust, user-friendly tools based on service-oriented ABM designs and platforms. The workshop also has the potential to facilitate interactions between academic-based ABM developers and users and commercial companies in ways that may facilitate advances in ABM software and capabilities. Because of the diversity of fields from which participants are drawn, the conference will promote more interdisciplinary development and use of advanced methodologies.The use of agent-based models has increased rapidly over the last two decades. The number of articles reporting the development or use of ABMs has increased at an exponential rate since the 1990s, spanning a diverse range of scientific communities that includes geography, ecology, human-environmental science, land-system science, and sociology. A strength of agent-based models has been the potential for representing agent-agent and agent-environment interactions, local-level information and heterogeneity, nonlinearity, feedback, and individual-level activity and decision-making. ABMs also are useful for integrating data and models across multiple systems and for addressing problems across spatial, temporal, and organizational scales. Despite these advances, a number of challenges confront ABM developers and users, including difficulty in model verification and validation, steep learning curves for non-modeling experts, and the lack of common standards or protocols, and limitations regarding their transparency and reusability. To address these challenges, more than three dozen individuals who are ABM modelers and users will gather to identify strengths and weaknesses in the state-of-the-science of agent-based modeling. Participants will engage in a set of discussions on a range of topics that may include model validation, modeling of human decisions, model transparency and reusability, and developing ABMs that can better use "big data." Participants also will identify resources, areas for collaboration, potential tasks, and future directions for the ABM community. Insights and products after the conclusion of the conference are expected to include a synthesis paper and several topical papers dealing with ABMs; a book about ABM science, technology, and application; and an online repository that shares useful ABM resources. Organizers expect this conference to be conducted during the spring months of 2017, most likely in San Diego, California.

该奖项将支持一个研讨会，该研讨会将汇集来自广泛领域的专家，以研究和推进社会，人类环境和生命科学中基于代理的模型（ABM）的开发和使用。 会议将促进社区范围内的问题，提高和扩大基于代理的建模的学术和实践目的的价值和效用的考虑。 会议将推动对反弹道导弹发展和使用的现有长处和短处进行多方面的、以社区为基础的审查。 会议及其后续产品将为许多相关领域确定激励措施和方向，以开发基于面向服务的反弹道导弹设计和平台的更强大、用户友好的工具。 讲习班还可能促进学术界反弹道导弹开发人员和用户与商业公司之间的互动，从而促进反弹道导弹软件和能力的进步。 由于与会者来自不同领域，会议将促进更多跨学科的发展和先进方法的使用。基于代理的模型的使用在过去二十年中迅速增加。 自20世纪90年代以来，报告ABM开发或使用的文章数量呈指数级增长，跨越了包括地理学、生态学、人类环境科学、土地系统科学和社会学在内的各种科学界。 基于代理的模型的优势一直是潜在的代理代理和代理环境的相互作用，本地级别的信息和异质性，非线性，反馈，和个人级别的活动和决策。 ABM还可用于跨多个系统集成数据和模型，以及解决跨空间、时间和组织规模的问题。 尽管取得了这些进展，ABM开发人员和用户面临着许多挑战，包括模型验证和确认的困难，非建模专家的陡峭学习曲线，缺乏通用标准或协议，以及透明度和可重用性的限制。 为了应对这些挑战，超过30位ABM建模人员和用户将聚集在一起，以确定基于代理的建模科学的优势和劣势。 与会者将参与一系列主题的讨论，其中可能包括模型验证，人类决策建模，模型透明度和可重用性，以及开发可以更好地使用“大数据”的ABM。“与会者还将确定反弹道导弹界的资源、合作领域、潜在任务和未来方向。 会议结束后的见解和产品预计将包括一份综合文件和几份关于反弹道导弹的专题文件;一本关于反弹道导弹科学、技术和应用的书;以及一个分享有用的反弹道导弹资源的在线资料库。 组织者预计这次会议将在2017年春季举行，最有可能在加州的圣地亚哥举行。

项目成果

‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models

“一刀切”：基于主体模型敏感性分析的目的驱动混合方法路径路线图

• DOI: 10.18564/jasss.4201
• 发表时间: 2020
• 期刊: Journal of Artificial Societies and Social Simulation
• 作者: Ligmann-Zielinska, Arika;Siebers, Peer-Olaf;Magliocca, Nicholas;Parker, Dawn C.;Grimm, Volker;Du, Jing;Cenek, Martin;Radchuk, Viktoriia;Arbab, Nazia N.;Li, Sheng
• 通讯作者: Li, Sheng

Methodological Issues of Spatial Agent-Based Models

基于空间代理的模型的方法论问题

• DOI: 10.18564/jasss.4174
• 发表时间: 2020
• 期刊: Journal of Artificial Societies and Social Simulation
• 作者: Manson, Steven;An, Li;Clarke, Keith C.;Heppenstall, Alison;Koch, Jennifer;Krzyzanowski, Brittany;Morgan, Fraser;O'Sullivan, David;Runck, Bryan C;Shook, Eric
• 通讯作者: Shook, Eric