Evolution and Resilience of Industrial Ecosystems (ERIE)

工业生态系统进化和复原力方案(ERIE)将解决与复杂性科学在社会和经济系统中的应用有关的一系列基本问题。我们的研究方案旨在将尖端复杂性科学方法和技术嵌入到原型计算工具中，为决策者提供现实和可靠的平台，以便在现实世界的社会经济系统中进行战略测试。该方案包括从案例研究中收集数据，开发和应用适当的理论和计算技术，使用基于主体的模型进行模拟，并将所有这些要素纳入供决策者使用的“严肃游戏”中。我们将研究政策目标和选项的谈判，探索模型在政策制定中的作用，并让政策制定者参与我们战略工具的设计和测试。该计划将专注于英国经济的一个关键方面：企业相互依赖的方式，这种相互关系是多层次和多价值的。在工业“生态系统”内，可能存在供求关系；知识转让；劳动力竞争；向供应链下游转移材料；标准谈判；贸易协会和工会之间的合作；以及创新、产品差异化和品牌。我们将使用数学和计算方法来模拟这些分层的、嵌套的、多尺度的系统，其中参与者之间的联系是动态的，他们之间的交换是不可预测的、波动的，甚至可能是零星的。在这一背景下，我们将考察复原力(从外部冲击中恢复的能力)、涌现(社会机构从个人活动中产生的方式)和沉浸(个人对体制限制的反应方式)的概念和衡量标准。这将我们引向一些最耐人寻味的复杂性科学公开问题。我们将从我们的案例研究中捕捉到的真实世界工业生态系统中寻找答案。我们的愿景是提供多层次社会经济系统的模型，这些模型对旨在“引导”实现与政策相关的目标的决策者来说是有用的。我们的意图不是为政策问题提供政策解决方案(具体地说，我们的意图不仅仅是展示如何使特定的生态系统更具弹性或更可持续)，而是提供一套工具，允许决策者及其代表在给定一组假设和初始条件的情况下调查替代方案。我们将应用数据同化方法，主要是在天气预报的背景下开发的，将案例研究中不可避免的不完整数据纳入基于代理的模型中，持续进行，目的是提供不断更新的真实世界数据的“预测”工具。在这里，我们所说的“预测”是指对备选情景的识别，以及对每一种情景在给定时间框架内实现的概率的估计，以及对这些情景对数据和基础模型中不确定性的敏感性的估计。研究--并让--参与案例研究现场的人参与，是Erie不可或缺的一部分。其中一个研究方向是研究那些在系统中有利害关系的人，如控制者、决策者、客户、工人等，他们的目标、政策选择及其与他们互动的工业生态系统的联系。研究方案分为四个方向，每个方向由若干跨学科项目组成。4名博士后研究人员和1名项目干事将参与该方案的工作，7名来自数学、计算机科学、环境科学和社会学学科的研究人员，以及9名博士生，后者由萨里大学内部资源资助。

The Evolution and Resilience of Industrial Ecosystems programme (ERIE) will address a series of fundamental questions relating to the application of complexity science to social and economic systems. Our programme of research aims to embed cutting-edge complexity science methods and techniques within prototype computational tools that will provide policymakers with realistic and reliable platforms for strategy-testing in real-world socio-economic systems. The programme includes the gathering of data from case studies, the development and application of appropriate theoretical and computational techniques, simulation using agent-based models and the incorporation of all these elements into 'serious games' for use by policymakers. We will study the negotiation of policy goals and options, explore the role of models in policymaking and involve policymakers in the design and testing of our strategy tools.The programme will focus on a crucial aspect of the UK economy: the way in which firms are interdependent on each other, with the interrelationships being multi-level and multi-valued. Within an industrial 'ecosystem', there can be relationships of supply and demand; the transfer of knowledge; competition for labour; the transfer of materials down supply chains; negotiation over standards; collaboration in trade associations and unions; and innovation, product differentiation and branding. We will use mathematical and computational approaches to model these layered, nested, multiscale systems, where the links between actors are dynamic and the exchanges between them are unpredictable, fluctuating and perhaps sporadic. Within this context we will examine concepts and measures of resilience (the ability to recover from external shocks), emergence (the ways in which social institutions arise from individual activities) and immergence (the ways in which individuals react to institutional constraints). This leads us to some of the most intriguing open questions of complexity science. We will seek answers inspired by the real-world industrial ecosystems as captured in our case studies. Our vision is to provide models of multi-level socio-economic systems that are useful for decision-makers aiming to 'steer' towards policy-relevant goals. It is not our intention to provide 'the' policy solution to policy problems (specifically, it is not our intention just to show how particular ecosystems may be made more resilient or more sustainable), but rather to provide a suite of tools which will allow decision makers and their representatives to investigate alternative scenarios given a set of assumptions and initial conditions.We will apply the methods of data assimilation, largely developed in the context of weather forecasting, to incorporate the inevitably incomplete data from case studies into agent-based models, on an ongoing basis, with the aim of providing 'predictive' tools that are continually updated with real-world data. By 'prediction' here we mean the identification of alternative scenarios along with estimates of the probability that each will be realised over given time frames, and estimates of the sensitivity of these to uncertainties in the data and underlying model. It is an integral part of ERIE to study - and involve - those involved in the case study sites. One research stream is concerned with studying those with a stake in the system, as controllers, decision makers, customers, workers, etc., their goals, policy options and their links with the industrial ecosystems that they are interacting with.The research programme is divided into four streams, each consisting of a number of cross-disciplinary projects. Four post-doctoral researchers and a project officer will work on the programme, with seven Investigators from the disciplines of mathematics, computing science, environmental science and sociology, and 9 PhD research students, the latter funded from internal University of Surrey resources.