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个体渔民之间的竞争如何导致与资源保护相一致（或不一致）的私人激励措施和非正式社会安排的出现。这些非正式的安排和激励措施很重要，因为它们帮助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，从而了解人类和自然系统耦合的可持续性。推动这项研究的广泛假设是，渔民之间的竞争性互动产生的非正式社会结构反映了自然系统的特殊环境。在某些情况下，成功的竞争需要秘密的不合作行为；在另一些情况下，合作往往会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不同的但并不总是明显的影响。我们认为相关的人类社会过程是个体通过耗时且昂贵的获取有关复杂资源的宝贵知识而相互竞争的过程。为了在竞争中取得成功，个人必须在利用现有知识所带来的直接利益与探索新知识的昂贵需求之间取得平衡；此外，在寻求新知识时，个人必须平衡通过合作或自主搜索获取知识的成本和收益。为了模拟这种竞争过程，我们采用了一种从计算机科学中借用的技术的显着修改版本，称为学习分类器系统（LCS）。 LCS 使用遗传算法来模仿代理（此处为渔民）利用其经验不断完善其关于自然和社会环境的知识和决策的方式。 LCS 的重要性在于，它可以模拟自利渔民之间共同演化的战略互动，这些渔民仅部分了解他们正在开发的资源状况以及与他们竞争的渔民。理解这些竞争动态的问题在几乎所有耦合的自然和人类系统中都是显而易见的。我们将该方法应用于缅因湾三种渔业的比较研究，这三种渔业的特点是时空动态显着不同——海胆、龙虾和鳕鱼。 每个渔业都将使用自然系统的生物物理模拟器和紧密集成的多智能体学习分类器系统来模拟渔民的学习和交互。每个模型的设计将部分基于对渔民的广泛访谈，了解他们对其工作的渔业动态的了解。我们将使用这些模型来探讨每个渔业过去和未来的政策问题。除了这些探索的直接适用性之外，我们预计该项目将为在其他耦合系统中更广泛地使用多智能体学习模型奠定基础。项目成果将定期传达给行业和管理者。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 均在缅因湾渔业领域拥有多年经验，并与个体渔民和管理人员建立了良好的关系。海洋政策硕士生，博士生。该项目将雇用计算机或海洋科学专业的学生和计算机科学专业的博士后研究员。此外，该项目还将开发复杂适应性社会生态系统的本科课程以及同一领域的研究生/教师研讨会。