CNH: RCN: A Research Network for Computational Modeling in the Socioecological Sciences

该项目建立了一个科学研究协作网络，以支持和扩大计算模型在社会科学和生命科学中的开发和使用。COMSES(社会生态科学计算模型)网络旨在广泛涵盖使用(并希望使用)高级模型来研究自然与人类耦合的社会和自然生态系统(SES)的社会科学家和自然科学家。人类社会实践和生物物理环境之间复杂的相互作用所产生的往往是非线性、非直觉的、甚至令人惊讶的关系和结果，使得科学模型--包括其基本假设、算法过程、逻辑一致性以及与经验数据的联系--必须是透明和定量的。因此，一般的计算建模，特别是基于主体的建模(ABM)是社会生态科学研究的重要工具。然而，在涉及自然和人类系统耦合科学的更广泛的科学界中，ABM仍然很少被使用或理解。相反，它被广泛认为是社会科学家和自然科学家无法接触到的，尽管ABM作为一种潜在的研究工具已经存在了近20年的事实。COMSES网络的主要目标是作为一个协调的实践社区和一个渠道，以加速在SES中进行计算建模的知识交流。该网络将建立在美国国家科学基金会资助的OpenABM联盟http://www.openabm.org.的基础上它将寻求发展科学基础设施，以便通过以下方式更好地为使用计算建模的研究服务：-建立计算模型的互动在线档案，并为模型评估提供标准工具和数据；-促进描述模型的共同标准；-开发教育课程，将建模和计算思维嵌入社会科学和自然科学的跨学科研究。-建立一个新的电子期刊，专门发表涉及计算建模、建模方法研究和实证评估的社会科学和自然科学研究。通过开发示范课程和培训机会，该项目将努力为代表下一代科学家的多个层次的学生提供新的跨学科学习机会。随着信息技术和相关网络工具在当今世界的重要性与日俱增，高级建模技术培训及其在社会科学中的应用将使学生的职业生涯广泛受益。我们在21世纪面临的许多环境挑战也可能被视为社会问题，因为它们影响我们的生活质量，只能通过人类行动来解决。COMSES网络具有通过开发计算工具和专业知识以深刻方式改变社会科学和自然科学的潜力，这些工具和专业知识将帮助我们更深入地了解人类与环境之间的关系，并使我们能够更好地预测未来政策和决策的可能影响。

This project establishes a scientific research collaboration network to support and expand the development and use of computational modeling in the social and life sciences. The COMSES (COmputational Modeling for SocioEcological Science) network aims to be broadly inclusive of social and natural scientists using (and desiring to use) advanced modeling to study coupled natural and human, or 'socioecological systems' (SES). The often non-linear, non-intuitive, and even surprising relationships and consequences that emerge from complex interactions between human social practice and the biophysical environment make it imperative that scientific models - including their underlying assumptions, algorithmic processes, logical consistency, and connections with empirical data - be transparent and quantitative. For this reason, computational modeling in general and agent-based modeling (ABM) in particular are important tools for research in socioecological sciences. Yet ABM remains little used or understood in the broader scientific communities involved in the science of coupled natural and human systems. Rather, it is widely perceived as inaccessible to social and natural scientists in spite of the fact that ABM has existed as a potential research tool for nearly two decades. The primary goal of the COMSES network is to serve as a coordinated community of practice and a conduit to expedite knowledge exchange for computational modeling in SES. The network will build on the NSF-funded OpenABM Consortium http://www.openabm.org. It will seek to grow scientific infrastructure so as to better serve research that uses computational modeling by:- establishing an interactive, online archive for computational models and providing standard tools and data for model evaluation;- promoting a common standard for describing models;- developing educational curricula for embedding modeling and computational thinking in the interdisciplinary study of social and natural sciences.- establishing a new e-journal dedicated to publishing research in the social and natural sciences that involves computational modeling, and studies of modeling methods and empirical evaluation.Through the development of exemplar curricula and training opportunities, the project will endeavor to stimulate new interdisciplinary learning opportunities for students at multiple levels who represent the next generation of scientists. With the ever-increasing importance of information technology and related cybertools in today's world, training in advanced modeling techniques and their application in the social sciences will broadly benefit students' careers. Many of the environmental challenges that we face in the 21st century may also be considered social problems because they affect our quality of life and can only be addressed by human actions. The COMSES network has the potential to transform the social and natural sciences in profound ways by developing computational tools and expertise that will help us to gain a much deeper understanding of the relationships between humans and their environment, and allow us to better predict the likely effects of future policies and decisions.