Background: Society is interested in developing and using models to forecast the biological and environmental health conditions of our planet. Many scientific studies have concluded that there is growing stress on Earth's environmental and biological systems, but there are very few global models to test those claims. The UN-backed Millennium Ecosystem Assessment Synthesis Report found that nearly two-thirds of Earth's life-supporting ecosystems— including clean water, pure air, and stable climate— are being degraded by unsustainable use. Humans are blamed for much of this damage. Soaring demands for food, fresh water, fuel, and timber have contributed to dramatic environmental changes; from deforestation to air, land, and water pollution. Despite the considerable research being conducted on local habitats and regional factors, current models do not adequately inform decision makers how their provincial polices may impact the overall health of the planet. Many models ignore complex global factors and are unable to determine the long-range impacts of potential policies. While scientists realize that the complex relationships and cross-effects in myriad environmental and biological systems impact Earth's biosphere, current models often ignore these relationships or limit the systems' connections. The system complexities manifest in multiple interactions, feedback loops, emergent behaviors, and impending state changes or tipping points. The recent Nature article written by 22 internationally known scientists entitled "Approaching a state shift in Earth's biosphere" outlines many of the issues associated with the need for scientific models and the importance of predicting potential state changes of the planetary health systems. The article provides two specific quantitative modeling challenges in their call for better predictive models: 

 

1) To improve bio-forecasting through global models that embrace the complexity of Earth's interrelated systems and include the effects of local conditions on the global system and vice versa. 

 

2) To identify factors that could produce unhealthy global state-shifts and to show how to use effective ecosystem management to prevent or limit these impending state changes. 

 

The resulting research question is whether we can build global models using local or regional components of the Earth's health that predict potential state changes and help decision makers design effective policies based on their potential impact on Earth's health. Although many warning signs are appearing, no one knows if Planet Earth is truly nearing a global tipping point or if such an extreme state is inevitable. The Nature article and many others point out that there are several important elements at work in the Earth's ecosystem (e.g., local factors, global impacts, multi-dimensional factors and relationships, varying time and spatial scales). There are also many other factors that can be included in a predictive model — human population, resource and habitat stress, habitat transformation, energy consumption, climate change, land use patterns, pollution, atmospheric chemistry, ocean chemistry, bio diversity, and political patterns such as social unrest and economic instability. Paleontologists have studied and modeled ecosystem behavior and response during previous cataclysmic state shifts and thus historic-based qualitative and quantitative information can provide background for future predictive models. However, it should be noted that human effects have increased significantly in our current biosphere situation. 

 

Requirements: 

 

You are members of the International Coalition of Modelers (ICM) which will soon be hosting a workshop entitled "Networks and Health of Planet Earth" and your research leader has asked you to perform modeling and analysis in advance of the workshop. He requires your team to do the following: 

 

Requirement 1: Build a dynamic global network model of some aspect of Earth's health (you develop the measure) by identifying local elements of this condition (network nodes) and appropriately connecting them (network links) to track relationship and attribute effects. Since the dynamic nature of these effects is important, this network model must include a dynamic time element that allows the model to predict future states of this health measure. For example, your nodes could be nations, continents, oceans, habitats, or any combination of these or other elements which together constitute a global model. Your links could represent nodal or environmental influences, or the flow or propagation of physical elements (such as pollution) over time. Your health measure could be any element of Earth's condition to include demographic, biological, environmental, social, political, physical, and/or chemical conditions. Be sure to define all the elements of your model and explain the scientific bases for your modeling decisions about network measures, nodal entities, and link properties. Determine a methodology to set any parameters and explain how you could test your model if sufficient data were available. What kinds of data could be used to validate or verify the efficacy of your model? (Note: If you do not have the necessary data to determine parameters or perform verification, do not throw out the model. Your supervisor realizes that, at this stage, good creative ideas and theories are as important as verified data-based models.) Make sure you include the human element in your model and explain where human behavior and government policies could affect the results of your model. 

 

Requirement 2: Run your model to see how it predicts future Earth health. You may need to estimate parameters that you would normally determine from data. (Remember, this is just to test and understand the elements of your model, not to use it for prediction or decision making.) What kinds of factors will your model produce? Could it predict state change or tipping points in Earth's condition? Could it provide warning about global consequences of changing local conditions? Could it inform decision makers on important policies? Do you take into account the human elements in your measures and network properties? 

 

Requirement 3: One of the powerful elements of using network modeling is the ability to analyze the network structure. Can network properties help identify critical nodes or relationships in your model? If so, perform such analysis. How sensitive is your model to missing links or changing relationships? Does your model use feedback loops or take into account uncertainties? What are the data collection issues? Does your model react to various government policies and could it thus help inform planning? 

 

Requirement 4: Write a 20-page report (summary sheet does not count in the 20 pages) that explains your model and its potential. Be sure to detail the strengths and weaknesses of the model. Your supervisor will use your report as a major theme in the upcoming workshop and, if it is appropriate and insightful to planetary health modeling, will ask you to present at the upcoming workshop. Good luck in your network modeling work! 

 

Potentially helpful references include: 

 

Anthony D. Barnosky, Elizabeth A. Hadly, Jordi Bascompte, Eric L. Berlow, James H. Brown, Mikael Fortelius, Wayne M. Getz, John Harte, Alan Hastings, Pablo A. Marquet, Neo D. Martinez, Arne Mooers, Peter Roopnarine, Geerat Vermeij, John W. Williams, Rosemary Gillespie, Justin Kitzes, Charles Marshall, Nicholas Matzke, David P. Mindell, Eloy Revilla, Adam B. Smith. "Approaching a state shift in Earth's biosphere,". Nature, 2012; 486 (7401): 52 DOI: 10.1038/nature11018 

Donella Meadows, Jorgen Randers, and Dennis Meadows. Limits to Growth: The 30-year update, 2004. 

Robert Watson and A.Hamid Zakri. UN Millennium Ecosystem Assessment Synthesis Report, United Nations Report, 2005. 

University of California - Berkeley. "Evidence of impending tipping point for Earth." ScienceDaily, 6 Jun. 2012. Web. 22 Oct. 2012. 

地球的健康网络建模(美国竞赛2013年C题) 

 

背景：社会很有兴趣开发和使用模型来预测我们这个星球的生物和环境的健康状况。许多科学研究得出的结论是，我们对地球环境和生态系统施加的压力越来越大。但是很少有全球性的模型来测试这些结论。由联合国支持的千年生态系统评估综合报告发现，近三分之二的用来支持地球生命的生态系统—包括干净的水，纯净的空气，稳定的气候正在被人们不可持续的利用所损坏。人类被指责为造成这种损坏的最主要因素。飙升的食物，淡水，燃料和木材的需求对环境的变化造成了剧烈的影响，从滥伐森林到空气，土地和水的污染。尽管大量的研究是针对局部栖息地和区域性因素，现在的模型还不足以告知决策制定者他们的局部政策对整个地球健康的影响。许多模型忽略了全局的影响因素，并且无法确定潜在政策所带来的长期的影响。科学家们意识到生态系统中复杂的关系和交叉效应影响到整个生物圈，目前的模型往往忽略这些关系或给系统之间的联系施加限制。系统的复杂性体现在多重交互，反馈回路，突发状况，即将发生的状态的变化或临界点。最近，由22位国际知名科学家发表在《自然》上的题为“走近地球生物圈”文章列出了很多关于需要建立科学模型的相关问题，以及预测地球健康系统潜在状态变化的重要性。这篇文章提供了两个具体的量化建模，并呼吁在此基础上建立更好的预测性模型： 

 

1） 通过具有复杂内部关系的全局模型来提高预测的准确度，而且包括局部对整体产生的影响，反之亦然。 

 

2） 确定那些可能会对全球状态产生不健康变化的因素，并展示如何使用有效的生态系统管理来防止或限制这些即将发生的状态的变化。 

 

因此研究的问题是，我们是否可以通过局部的或者区域性的地球健康状况来建立的全局模型来预测潜在的状态变化，然后通过这些潜在的对地球健康状况的影响来帮助决策者来制定有效的政策。虽然现在许多警报出现，但是没有人知道地球是否真正接近临界点或者这种极端的状态是否可以避免。《自然》上的这篇文章以及其他许多人指出，在研究地球生态系统的工作上有几个重要的因素（例如，局部因素的影响，全球性的影响，多维因素和关系，时间的变化和空间尺度）。除此之外，预测模型还包含很多其他因素—人口，资源和栖息地压力，栖息地改造，能源消耗，气候变化，土地利用模式，污染，大气化学，海洋化学，生物多样性，政策类型，如社会动荡和经济的不稳定。古生物学家已经研究并模拟先前灾难性的生态系统状态的巨变和反应，因此历史的定性和定量信息为未来的预测模型提供背景资料。然而，应该指出的是，人类活动对我们目前的生物圈状况产生的影响显著增加。 

 

要求： 

 

你是国际建模联盟（ICM）的会员，ICM不久将举办一个主题为“地球网络与健康”的研讨会，你项目组的领导者希望你在这个会议上来进行建模和分析。他要求你的团队做到以下几点： 

 

要求1：通过确定局部状态（网络节点）来建立一个关于地球某方面健康状态（你来测量）的动态的全局网络模型，并且适当连接（网络连接）来追踪关系，考察分配效果。由于这些效果的动态性质是很重要的，这个网络模型必须包含一个动态的时间元素以便预测你制定的这个健康措施的未来状态。例如，节点可以是国家，大陆，海洋，栖息地，或这些元素的任意组合，或者其他元素，所有这些共同构成了全球性的模型。您的连接可以代表节点或环境的影响，或随着时间推移的流量或物理因素的扩散（如污染）。您的测量目标可以是地球上的任何状态元素，包括人口统计，生物，环境，社会，政治，物理和/或化学条件。确保正确定义你模型中的所有因素，并解释你模型中关于网络措施，节点实体，链路属性选择的科学依据。制定一种方法用来设置任何参数，并解释如果有足够的数据，如何来检测你的模型。什么样的数据可以让你的模型起作用或什么样的数据可以用来验证你的模型的有效性？（注意：如果您没有必要的数据来确定参数或者进行验证，不要丢掉模型。你的领导会意识到，在这个阶段，良好的创新思想和理论和基于数据验证的模型一样重要）。请确保你在模型中加入了人类的因素并能够解释人类行为和政府政策对模型的哪个地方会造成影响 

 

要求2：运行你的模型看看它如何预测未来的地球健康。你可以估计那些通常需要从数据来确定的参数。（请记住，这仅仅是为了测试并且理解你的模型中的元素，而不是用来预测或决策）。你的模型会产生什么因素？它可以用来预测地球的状态变化或者临界点吗？如果让局部状态发生变化可以预测出对全局带来的影响吗？它能在决策者制定一些重要的决策时提供信息吗？在你的措施和网络性质中有没有加入人类因素？ 

 

要求3： 用网络建模一个有力的因素就是对网络结构的分析能力。网络的特性可以帮助你确定模型中的关键节点或者相互关系吗？如果可以，请给出分析过程。你的模型对缺失链条的情况以及对关系改变的敏感度怎样？你的模型有没有用到反馈回路，或者有没有考虑不定性因素？你收集的数据是怎样发布的？你的模型对各种政府政策都适用吗？可以帮助制定计划吗？ 

 

要求4：写一份20页的报告来解释你的模型以及它的潜力。确保详细的阐述模型的优点以及缺点。你的领导将用你的报告作为将要举行的研讨会的主要议题，如果你的报告写的很切题，而且对地球健康的模拟很有洞察力，他会要求你在即将举行的研讨会上做个报告。 

 

祝你在网络模型工作上取得成功！ 

 

 

可能会有帮助的参考文献有： 

 

Anthony D. Barnosky, Elizabeth A. Hadly, Jordi Bascompte, Eric L. Berlow, James H. Brown, Mikael Fortelius, Wayne M. Getz, John Harte, Alan Hastings, Pablo A. Marquet, Neo D. Martinez, Arne Mooers, Peter Roopnarine, Geerat Vermeij, John W. Williams, Rosemary Gillespie, Justin Kitzes, Charles Marshall, Nicholas Matzke, David P. Mindell, Eloy Revilla, Adam B. Smith. "Approaching a state shift in Earth's biosphere,". Nature, 2012; 486 (7401): 52 DOI: 10.1038/nature11018 

Donella Meadows, Jorgen Randers, and Dennis Meadows. Limits to Growth: The 30-year update, 2004. 

Robert Watson and A.Hamid Zakri. UN Millennium Ecosystem Assessment Synthesis Report, United Nations Report, 2005. 

University of California - Berkeley. "Evidence of impending tipping point for Earth." ScienceDaily, 6 Jun. 2012. Web. 22 Oct. 2012.