CNH: Fine-Scale Dynamics of Human Adaptation in Coupled Natural and Social Systems: An Integrated Computational Approach Applied to Three Fisheries

CNH：耦合自然和社会系统中人类适应的精细尺度动力学：应用于三种渔业的综合计算方法

• 批准号: 0909449
• 负责人: James Wilson
• 金额: $ 102.13万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0909449&HistoricalAwards=false
• 关键词: CNH; Fine; Scale; Dynamics; Human

The purpose of this project is to gain a better understanding of the way competition between individual fishermen lead to the emergence of private incentives and informal social arrangements that are (or are not) consistent with conservation of the resource. These informal arrangements and incentives are important because they help us understand the extent to which private interests might strengthen or weaken on-going resource management and, consequently, the sustainability of coupled human and natural systems. The broad hypothesis driving the study is that the informal social structure that emerges from competitive interactions among fishermen reflects the particular circumstances of the natural system. In some cases, successful competition requires secretive non-cooperative behavior; in others, cooperation tends to yield better competitive results. These different outcomes have different, and not always obvious, impacts on the feasibility and effectiveness of resource management. We think of the relevant human social process as one in which individuals compete with one another through time-consuming and costly acquisition of valuable knowledge about a complex resource. To compete successfully, individuals must balance the immediate benefits that come from exploiting knowledge they currently hold with the costly need to explore for new knowledge; additionally, when seeking new knowledge, individuals must balance the costs and benefits of acquiring knowledge through cooperation or through autonomous search. In order to model this kind of competitive process, we employ a significantly modified version of a technique borrowed from computer science called a learning classifier system (LCS). LCS uses a genetic algorithm to mimic the way an agent (here a fisherman) uses his experience to continuously refine his knowledge and decisions about his natural and social environment. The importance of LCS is that it permits simulation of the co-evolving strategic interactions of self-interested fishermen who are only partially informed about the state of the resource they are exploiting and the fishermen with whom they compete. The problem of understanding these kinds of competitive dynamics is evident in almost all coupled natural and human systems. We apply the approach to a comparative study of three Gulf of Maine fisheries which are characterized by significantly different temporal and spatial dynamics - sea urchins, lobster and cod. Each fishery will be modeled using a biophysical simulator of the natural system and a tightly integrated multi-agent learning classifier system that simulates the learning and interactions of fishermen. The design of each model will be based in part on extensive interviews with fishermen about their knowledge of the dynamics of the fisheries in which they work. We will use these models to explore past and prospective policy problems in each fishery. Beyond the immediate applicability of these explorations, we expect this project will provide a foundation for the wider use of multi-agent learning models in other coupled systems. Project outcomes will be transmitted regularly to industry and managers. Principal investigators include economists, biologists, anthropologists and computer scientists. All the PIs have years of experience in the fisheries of the Gulf of Maine and have well developed relationships with individual fishermen and managers. A masters level student in marine policy, a Ph.D. student in computer or marine science and a post-doctoral researcher in computer science will be employed on the project. In addition, the project will develop an undergraduate course in complex adaptive social-ecological systems and a graduate student/faculty workshop in the same area.

该项目的目的是更好地了解各个渔民之间的竞争方式导致私人激励措施和非正式社会安排的出现（或不）与资源的保护一致。这些非正式的安排和激励措施很重要，因为它们有助于我们了解私人利益可能在多大程度上加强或削弱正在进行的资源管理，从而耦合人类和自然系统的可持续性。推动研究的广泛假设是，渔民之间的竞争互动产生的非正式社会结构反映了自然系统的特定情况。在某些情况下，成功的竞争需要秘密的非同伴行为；在其他情况下，合作倾向于产生更好的竞争结果。这些不同的结果对资源管理的可行性和有效性有所不同，也不总是显而易见的影响。我们认为相关的人类社会过程是个人通过耗时且昂贵的有关复杂资源的宝贵知识而相互竞争的人。为了成功竞争，个人必须平衡利用目前拥有的知识和为新知识探索的昂贵需求所带来的直接利益；此外，当寻求新知识时，个人必须平衡通过合作或通过自动搜索获取知识的成本和收益。为了建模这种竞争过程，我们采用了从称为学习分类器系统（LCS）的计算机科学借用的技术的重大修改版本。 LCS使用一种遗传算法来模仿代理商（这里是渔夫）利用他的经验来不断地完善他对自然和社会环境的知识和决定。 LCS的重要性在于，它允许模拟自私自利的渔民的共同发展的战略互动，他们仅部分了解他们正在利用的资源状态以及与之竞争的渔民。在几乎所有耦合的自然和人类系统中，了解这些类型的竞争动态的问题是显而易见的。我们将方法应用于对三个缅因州渔业的比较研究，这些研究的特征是时间和空间动力学明显不同 - 海胆，龙虾和鳕鱼。 每种渔业将使用自然系统的生物物理模拟器和紧密整合的多机构学习分类器系统进行建模，该系统模拟渔民的学习和相互作用。每个模型的设计将基于与渔民有关他们对工作渔业动态的了解的广泛采访。我们将使用这些模型来探索每种渔业中的过去和潜在政策问题。除了这些探索的直接适用性外，我们预计该项目将为在其他耦合系统中更广泛使用多代理学习模型提供基础。项目成果将定期传输给行业和经理。主要研究人员包括经济学家，生物学家，人类学家和计算机科学家。所有PI在缅因州湾的渔业中都有多年的经验，并与个别渔民和经理建立了良好的关系。海洋政策的硕士学位学生，博士学位。该项目将使用计算机或海洋科学领域的学生以及计算机科学的博士后研究员。此外，该项目将在复杂的自适应社会生态系统和同一领域的研究生/教师研讨会上开发一门本科课程。