Evolution and Resilience of Industrial Ecosystems (ERIE)

工业生态系统的演变和弹性（ERIE）

• 批准号: EP/H021779/1
• 负责人: Nigel Gilbert
• 金额: $ 426.16万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH021779%2F1
• 关键词: Evolution; Resilience; Industrial; Ecosystems; ERIE

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.

工业生态系统的演变和复原力计划（伊利）将解决一系列与复杂性科学在社会和经济系统中的应用有关的基本问题。我们的研究计划旨在将尖端的复杂性科学方法和技术嵌入原型计算工具中，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，使用基于代理的模型进行模拟，并将所有这些要素纳入“严肃游戏”，供决策者使用。我们将研究政策目标和选项的谈判，探索模型在决策中的作用，并让政策制定者参与我们的战略工具的设计和测试。该计划将侧重于英国经济的一个重要方面：企业相互依赖的方式，相互关系是多层次和多价值的。在一个工业“生态系统”内，可能存在供求关系;知识转让;劳动力竞争;材料沿供应链向下转移;标准谈判;行业协会和工会合作;以及创新、产品差异化和品牌化。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，其中演员之间的联系是动态的，他们之间的交流是不可预测的，波动的，也许零星的。在此背景下，我们将研究弹性（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（个人对制度约束的反应方式）的概念和措施。这将我们引向复杂性科学中一些最有趣的开放性问题。我们将从案例研究中捕捉的现实世界工业生态系统中寻求答案。我们的愿景是提供多层次的社会经济系统模型，这些模型对旨在“引导”政策相关目标的决策者有用。我们无意为政策问题提供“最佳”的政策解决方案（具体来说，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具可持续性），而是提供一套工具，使决策者及其代表能够在给定一组假设和初始条件的情况下研究替代方案。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，在持续的基础上，旨在提供不断更新真实世界数据的“预测”工具。这里的“预测”指的是确定备选方案沿着，并估计每种方案在给定时间范围内实现的概率，以及估计这些方案对数据和基础模型中的不确定性的敏感性。伊利的一个组成部分是研究-并参与-案例研究地点的参与者。一个研究流关注于研究那些与系统有利害关系的人，如控制者、决策者、客户、工人等，其目标，政策选择及其与工业生态系统的联系，他们正在相互作用。研究方案分为四个流，每个流包括一些跨学科的项目。四名博士后研究人员和一名项目官员将参与该计划，其中七名研究人员来自数学，计算科学，环境科学和社会学学科，以及9名博士研究生，后者由萨里大学内部资源资助。

项目成果

A Dynamical Model of the Industrial Economy of the Humber Region

汉伯地区工业经济的动态模型

• DOI: 10.48550/arxiv.1404.3167
• 发表时间: 2014
• 期刊: arXiv e-prints
• 作者: Knight Christopher J. K.

Service Choreography, SBVR, and Time

服务编排、SBVR 和时间

• DOI: 10.4204/eptcs.201.5
• 发表时间: 2015
• 期刊: Electronic Proceedings in Theoretical Computer Science
• 作者: A. Manaf N

Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points

• DOI: 10.1016/j.asoc.2013.10.030
• 发表时间: 2014-02-01
• 期刊: APPLIED SOFT COMPUTING
• 影响因子: 8.7
• 作者: Knight, Christopher J. K.;Lloyd, David J. B.;Penn, Alexandra S.
• 通讯作者: Penn, Alexandra S.

Comparing the effects of mutualism and competition on industrial districts

• DOI: 10.1016/j.physa.2014.09.001
• 发表时间: 2014-12
• 期刊: Physica A-statistical Mechanics and Its Applications
• 影响因子: 3.3
• 作者: Christopher J. K. Knight;A. Penn;R. Hoyle

Analyzing regime shifts in agent-based models with equation-free analysis

通过无方程分析来分析基于主体的模型中的状态转变

• 发表时间: 2016
• 期刊: Environmental Modelling and Software for Supporting a Sustainable Future, Proceedings - 8th International Congress on Environmental Modelling and Software, iEMSs 2016
• 作者: Martin R.