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

这个项目将着眼于治理、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。它将把几种社会科学技术(如社会网络分析和基于主体的建模)联系起来，在生态系统服务面临压力的两个实质性领域全面绘制与生态系统有关的所有相关社会和生态知识流动图。我们将与当地伙伴一起研究西印度洋(肯尼亚)和孟加拉湾(孟加拉国)关于沿海地区资源利用和管理的两个案例。其目的是通过比较研究产生更多关于地方一级如何作出政策和行动决定的一般性数据。我们将看看个人用来帮助他们做出关于生计的决策的网络。这些将在全球经济和其他决策网络对地方一级的社会-生态相互关系产生影响的背景下加以理解。我们自己的理解将在文献中围绕社会生态复杂性和复杂的适应性管理来框定。该项目将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。该项目要求为两个英国伙伴(约克大学和牛津斯德哥尔摩环境研究所)提供资金，以便与孟加拉国和肯尼亚的六个伙伴密切合作，与那里的政策制定者和从业人员密切合作，以便加深对利用沿海生态系统服务减少贫穷的决策的可持续规范的理解。最简单地说，我们将与肯尼亚和孟加拉国的相关人士交谈，看看他们从哪里以及从谁那里获得信息，关于什么，并将其作为知识网络地图呈现出来。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)。我们将通过使用基于代理的系统模型来加深我们和他们对需要发生什么的理解。这还将结合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(第7页)中确定的知识差距相对应。然而，我们建议，应将退化和可持续性驱动因素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、反馈或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发展的模式。为了抓住这一复杂性，我们提出了4项任务：a)调查一系列学科(生物物理、社会、政治和经济)的多种驱动因素。B)绘制横向和纵向网络和知识传播模式图。C)使用社会生态模型来分析和模拟司机之间的相互作用。D)反映出WD-NACE项目的影响(另见影响计划)。我们选择的成功标准将是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成功的标准。然后，这些标准可以被用作影响的试金石。将发展和改进连接生态系统和生计领域、知识网络和决策结构的概念框架，这是世界可持续发展大会的预期成果。在制定这一模式时，我们期望它可以适用于各种环境，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.