Whole decision network analysis for coastal ecosystems (WD-NACE)

该项目将研究治理、权力和知识结构之间的关系，以及这些结构如何影响可持续生态系统的行为、行动和决策。它将把若干社会科学技术（例如社会网络分析和基于代理的建模）联系起来，以全面绘制关于生态系统服务面临压力的两个实质性领域中的生态系统的所有相关社会和生态知识流。我们将与当地合作伙伴一起研究西印度洋（肯尼亚）和孟加拉湾（孟加拉国）关于沿海区资源利用和管理的两个案例。其目的是通过比较研究，就地方一级如何作出政策决定和采取行动提供更通用的数据。我们将研究个人用来帮助他们就生计做出决定的网络。这些将在全球经济和影响地方一级社会生态相互关系的其他决策网络的范围内加以理解。我们自己的理解将围绕社会生态复杂性和复杂适应性管理的文献框架内。该项目将在实质上侧重于加强沿海生态系统的可持续管理，其进程侧重于减贫的政治经济学，并更好地了解如何维持生计以及如何重视和保护生态系统服务。该项目正在为两个联合王国合作伙伴（约克大学和牛津大学斯德哥尔摩环境研究所）申请资金，以便与孟加拉国和肯尼亚的六个合作伙伴密切合作，与当地的决策者和从业人员密切合作，了解利用沿海生态系统服务促进减贫的可持续决策规范。简而言之，我们将与肯尼亚和孟加拉国的相关人员交谈，看看他们从哪里和谁那里获得信息，关于什么，并将其作为知识网络地图呈现。然后，我们将与这些利益相关者合作，看看这些信息是否符合他们的需求（即做出他们需要做出的决定）。我们将加深我们和他们的理解需要通过使用基于代理的系统模型发生什么。这也将汇集社会对信息传播和使用环境的理解，并对信息本身及其效用进行更技术性的分析。我们的工作任务与ESPA电话会议中确定的知识差距相对应（第7页）。然而，我们建议退化和可持续性驱动因素应被视为同一复杂系统的一部分。人们对多个驱动因素相互作用、相互反馈或相互平衡的方式知之甚少，因此目前很难将单个驱动因素归因于退化或可持续性模式。为了捕捉这种复杂性，我们提出了4项任务：A）调查跨一系列学科（生物物理，社会，政治和经济）的多个驱动因素。B）绘制水平和垂直网络以及知识扩散模式。C）使用社会生态模型分析和模拟驱动因素之间的相互作用。D）反思WD-NACE项目的影响（另见影响计划）。我们选择的成功标准将通过评估研讨会后的反馈，但我们也将与每个研究合作者和我们的合作伙伴组织商定一个成功的标准。这些标准可以作为衡量影响力的试金石。世界发展日-NACE的一个预期成果是，将制定和改进将生态系统和生计领域、知识网络和决策结构联系起来的概念框架。在发展这种模式时，我们期望它可以适用于一系列环境，如决策机构和国际发展组织以及更多的当地用户。

This project will look at the relationship between governance, power and knowledge structures and how these influence behaviour, actions and decision taking for sustainable ecosystems. It will link several social science techniques (e.g. social network analysis and agent-based modelling) to comprehensively map all relevant social and ecological knowledge flows regarding ecosystems in two substantive areas where there is pressure on ecosystem services. With our local partners, we will look at two cases in the western Indian Ocean (Kenya) and the Bay of Bengal (Bangladesh) regarding coastal zone resource uses and management. The purpose is to generate more generic data across comparative studies about how decisions for policy and for action are taken at local levels. We will look at the networks that individuals use to help them make decisions regarding their livelihoods. These will be understood within the context of the global economy and other decision networks impacting upon the social-ecological interrelationships at the local level. Our own understanding will be framed within the literature around social-ecological complexity and complex adaptive management. The project will substantively focus on strengthening the sustainable management of coastal ecosystems with a process focus on the political economy for poverty reduction and to understand better how livelihoods can be maintained and ecosystem services valued and preserved. The project is asking for funds for two UK partners (The University of York and the Stockholm Environment Institute, Oxford) to work with six partners in Bangladesh and in Kenya in close collaboration with policy makers and practitioners there in order to develop an understanding of sustainable norms for decision making in the use of coastal ecosystem services for poverty reduction. Put most simply, we are going to talk to relevant people in Kenya and Bangladesh to see where and from whom they get information, about what, and present this as a knowledge network map. We will then work with those stakeholders to see if it is the right information for their needs (i.e. to make the decisions that they need to make). We will deepen our and their understanding of what needs to happen by the use of agent based models of the systems. This will also bring together the social understanding of the milieu within which information is passed around and used with a more technical analysis of that information itself and its utility. Our work tasks are mapped to the knowledge gaps identified in the ESPA call (page 7). However, we propose that degradation and sustainability drivers should be considered as a part of the same complex system. Little is known about the way in which multiple drivers interact, feedback upon, or balance each other so it is currently very difficult to attribute individual drivers to patterns of degradation or sustainability. To capture this complexity we propose 4 tasks: A) Investigate multiple drivers across a range of disciplines (biophysical, social, political and economic). B) Map the horizontal and vertical networks and knowledge diffusion patterns. C) Use socio-ecological models to analyse and model interactions among drivers. D) Reflectively link the impact of the WD-NACE project (see also Impact Plan). Our chosen criteria of success will be by means of an assessment of post-workshop feedback, but we shall also agree a criterion of success with each research collaborator and with our partner organisation. These criteria can then be used as the touchstone of impact. It is an expected outcome of WD-NACE that the conceptual framework linking ecosystem and livelihoods domains, knowledge networks and decision-making structures will be developed and improved. In developing such a model, we would expect that it could be applied to a range of settings such as by policy-making institutions and international development organizations as well as by more local users.