Simone Di Zio, Co-Chair of Italy Node of The Millennium Project, in collaboration with other colleagues, recently published conference proceedings that explore innovative approaches for future scenarios and statistical analyses.

The following publications showcase innovative research and methodologies aimed at advancing our understanding and modelling of future trends and scenarios.

1. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 𝐨𝐟 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐂𝐨𝐧𝐬𝐞𝐧𝐬𝐮𝐬 𝐀𝐝𝐨𝐩𝐭𝐢𝐧𝐠 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐞𝐥𝐩𝐡𝐢 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐨𝐟 𝐅𝐮𝐭𝐮𝐫𝐞 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬, by Yuri Calleo, Francesco Pilla, and Simone Di Zio, presented at the 52nd Scientific Meeting of the Italian Statistical Society (University of Bari Aldo Moro, Bari, Italy):

Abstract: Spatial complexity, defined by a multitude of interconnected variables, is recognized as one of the most challenging forms to predict. Given the inherent unpredictability of the future and the limited utility of forecasting models for long-term predictions, this paper proposes an integrated approach for navigating various potential futures. For the development of spatial scenarios, various methods are currently adopted, including the Real-Time Spatial Delphi approach. This method involves leveraging expert judgments through a real-time process to attain a spatial consensus on the territory. Nevertheless, the outputs of this method encompass spatial analyses and statistical indicator results which, from a communicative standpoint, may fail to capture the attention of non-experts, such as citizens or policymakers. To overcome this challenge, the paper suggests integrating Generative Adversarial Networks models to produce realistic visualiza-tions of scenario-based policies. Through a case study conducted in Massa, Italy, as part of the EU H2020 SCORE project, the effectiveness of this approach is demonstrated. The findings highlight the significance of incorporating artificial intelligence methods to enhance the communicative aspect of spatial scenarios, providing efficient visual representations of scenarios and emerging policies ready for assessment.

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2. 𝐌𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐄𝐮𝐫𝐨𝐩𝐞𝐚𝐧 𝐔𝐧𝐢𝐨𝐧 𝐂𝐨𝐮𝐧𝐭𝐫𝐢𝐞𝐬: 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐌𝐞𝐭𝐡𝐨𝐝𝐬, by Yuri Calleo, Leonardo Salvatore A. and Simone Di Zio, presented at the 52nd Scientific Meeting of the Italian Statistical Society (University of Bari Aldo Moro, Bari, Italy).

Abstract: This paper addresses the challenge of analyzing future tra-jectories, crucial for goal attainment. While some models like the State of the Future Index (SOFI) provide insights into potential trends, they necessitate complementarity with qualitative methods for comprehensive understanding. Despite its efficacy, SOFI’s condensation of diverse variables into a single index may obscure nuances and regional disparities, potentially oversimplifying systemic dynamics. Critique has been leveled against its construction, particularly regarding its weighting system. This study aims to mitigate these concerns by comparing synthesis methods and proposing a statistically rigorous framework for measuring SOFI at the European Union level. By employing those techniques, this paper endeavors to enhance the precision and reliability of SOFI as a tool for identifying future dynamics and evaluating present policies.

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3. 𝐂𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐞𝐥𝐩𝐡𝐢 𝐉𝐮𝐝𝐠𝐦𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐨𝐟 𝐅𝐮𝐭𝐮𝐫𝐞 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬, by Yuri Calleo, Simone Di Zio, and Francesco Pilla, presented at the Statistics, Technology and Data Science for Economic and Social Development conference (University of Bologna, Bologna, Italy).

Abstract: In the Futures Studies context (FS), scenarios refer to speculative visions of potential future states or situations that may arise based on a combination of existing trends, emerging technologies, and various factors (Kosow and Gaßner, 2008). Scenarios provide a framework for exploring and anticipating various possibilities, enabling individuals, organizations, and policymakers to better prepare for the uncertainties of tomorrow. Among the many methods used, the scenario method can be combined with different approaches including the Delphi method, forming the so-called Delphi-based scenarios. In the spatial context, one of the main implementations is the Real-Time Spatial Delphi (Di Zio et al. 2017), a spatial version of the conventional Delphi method, integrating real-time feedback and visualization of group judgments. Its primary objective is to facilitate consensus-building among experts regarding geographical locations in the decision-making and forecasting process. Nevertheless, while these assessments provide spatial areas represented on digital maps, they do not manifest in the physical realm. With the emergence of AI models, alternative implementations, such as leveraging Generative Adversarial Networks (GANs), can enhance the visioning and planning process. In this paper, we propose a hybrid approach combining Real-Time Spatial Delphi and GANs to enhance the envisioning of spatial scenarios with the aim both to sensitize experts’ opinions with clear and realistic images of the future and plan suitable policies in the present. In our approach, once a narrow area is denoted by the experts, we capture digital images of these areas and employ GANs models to generate visual outputs of the scenarios or suggested plans for specific points inside the consensus area. This innovative approach aids experts and policymakers in visualizing the proposed policies more effectively, enabling them to assess the impacts with greater accuracy. We apply the method to the city of Dublin in the climate change context.

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Moreover, the Italy Node recently launched a new study on Artificial Intelligence and scenarios, “New forms of visual communication of scenarios in Delphi-Based research“. Main aim of this survey is to evaluate different types of AI-generated visual communication of future scenarios. The survey will be open until September 8, 2024.

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