通过实时空间Delphi促进未来复杂场景中的空间共识:一个新颖的基于web的开放平台

Yuri Calleo, Simone Di Zio, Francesco Pilla
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引用次数: 0

摘要

Delphi技术是一种常用的方法,用于(在各种用途中)从一群知识渊博的专家那里达成共识。这种方法经常用于生成想法并确定其优先级,确定潜在的解决方案,并通过一系列迭代回合在各种情况下做出决策。在未来研究(FS)的背景下,德尔菲方法经常与情景方法结合使用,以探索不同的未来,在当前实施策略,目的是避免反乌托邦结果和/或促进规范情景。然而,假设意见的趋同也可能发生在空间环境中,该方法的一个缺点是缺乏在规划过程中有用的空间参考。在本文中,我们介绍了实时地理空间共识系统,这是一个新颖的基于web的开放平台,可用于开发基于Delphi的空间场景(DBSS),具有交互式和创新性的界面。该平台采用实时空间德尔菲技术,以获得专家之间意见的空间趋同,为研究人员、决策者、决策者和地方当局提供一种新的复杂空间决策工具。该平台的主要创新,包括其架构、统计算法、工具、功能和结果,通过在爱尔兰都柏林针对未来潜在气候危害的初步应用进行了展示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Facilitating spatial consensus in complex future scenarios through Real-Time Spatial Delphi: A novel web-based open platform

Facilitating spatial consensus in complex future scenarios through Real-Time Spatial Delphi: A novel web-based open platform

The Delphi technique is a commonly applied method for (among the various uses) achieving consensus from a group of knowledgeable experts. This approach is frequently employed to generate and prioritize ideas, identify potential solutions, and make decisions in various contexts through a series of iterative rounds. In the Futures Studies (FS) context, the Delphi method is regularly used in combination with the scenario method to explore different futures, implementing strategies in the present with the aim of averting dystopian outcomes and/or facilitating normative scenarios. Nevertheless, assuming that the convergence of opinions can also occur in spatial contexts, a shortcoming of the method is the deficiency of spatial references useful in the planning process. In this paper, we introduce the Real-Time Geo-Spatial Consensus System, a novel web-based open platform useful to develop Delphi-based Spatial Scenarios (DBSS), in an interactive and innovative interface. The platform adopts the Real-Time spatial Delphi technique to obtain a spatial convergence of opinions among experts to offer researchers, decision-makers, policymakers, and local authorities a new tool for complex spatial decisions. The primary innovations of the platform, including its architecture, statistical algorithms, tools, features, and outcomes, are demonstrated through a preliminary application focused on potential future climatic hazards in Dublin, Ireland.

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