Artificial Intelligence meets Multi-Criteria Decision Aiding in smart sustainble cities

人工智能在可持续智慧城市中满足多标准决策辅助

• 批准号: RGPIN-2020-05642
• 负责人: AbiZeid, Irène
• 金额: $ 1.89万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717722
• 关键词: Artificial; Intelligence; meets; Multi; Criteria

In today's organizations, transparency and accountability are increasingly required, especially for decisions involving environmental and public resources, which have an impact on groups with diverse vulnerability levels and conflicting interests. This is particularly true when it comes to city planning and management where issues with drinking water source protection, wastewater management, transportation planning, social housing planning or street rehabilitation must be addressed in a context of increasing awareness of social inequities and environmental degradation related to anthropogenic activities, and in an era of social media, citizens participation and big data. This context has led, in the mid 2010s, to the emergence of the concept of smart sustainable cities, i.e. cities which, "supported by information and communications technologies (ICT) and aware of local or environmental limitations, meet the needs of their current inhabitants without compromising the ability of other people or other generations to meet their needs" (Höjer & Wangel, 2015). But how can a city evolve toward a smart sustainable city? How can public decision processes be conducted to ensure sustainable, robust, rigorous, inclusive, transparent, acceptable and fair decisions while taking advantage of the ICT infrastructure? The answer is interdisciplinary and lies partly in the field of Analytics, more precisely in participatory multicriteria decision aiding (MCDA), a sub-discipline of operations research, which has gained an increasing momentum over the last five decades, and in its combination with techniques from Artificial Intelligence, Problem Structuring Methods, Geographic Information Systems, and Preference Learning as they pertain to MCDA decision processes. In this research program, we will design a spatial MCDA framework and develop, validate and evaluate tools to support the MCDA process in relation to the following questions with applications related to smart sustainable cities: 1) What are the environmental characteristics, activities, factors and main difficulties that significantly influence the conduct of a MCDA process and how to deal with them? 2) How can the preferences of all stakeholders be captured and taken into account? 3) How can massive amount of quantitative and qualitative information, sometimes called big data, be integrated in the process? 4) How to evaluate the quality of a MCDA process? 5) How to explain and argue the decision process and the results to the stakeholders to ensure acceptability? Our research program will be conducted following a Design Science orientation. Our theoretical framework will be interdisciplinary and the underpinning philosophical paradigm is critical realism, a philosophy that is consistent with our multimethodological approach. We will apply and test our framework and tools in the following application domains: municipal asset management, drinking water source protection and social housing planning.

在当今的组织中，越来越需要透明度和问责制，特别是涉及环境和公共资源的决策，这些决策对不同脆弱程度和利益冲突的群体产生影响。在城市规划和管理方面尤其如此，饮用水水源保护、废水管理、交通规划、社会住房规划或街道修复等问题必须在人们日益意识到与人为活动有关的社会不平等和环境退化的背景下，在社交媒体、公民参与和大数据时代得到解决。在这种背景下，在2010年代中期，出现了智能可持续城市的概念，即“由信息和通信技术（ICT）支持并意识到当地或环境限制的城市，满足其当前居民的需求，而不损害其他人或其他世代满足其需求的能力”（Höjer & Wangel, 2015）。