arXiv subjects

Vittorio Nespeca

Publications and source records attributed to Vittorio Nespeca.

4 recordsLinked to original sources

arXiv2025

Resilience of coupled systems under deep uncertainty and dynamic complexity: An integrative literature review

Resilience in coupled systems is increasingly critical in addressing global challenges such as climate change and pandemics. These systems show unpredictable behaviour due to dynamic complexity and deep uncertainty across spatiotemporal scales. Despite growing interest, few studies systematically integrate both concepts when assessing resilience. This paper conducts an integrative review of 102 English-language publications to identify gaps in current approaches. Findings reveal that most papers address lower levels of uncertainty and rarely consider dynamic complexity and deep uncertainty simultaneously, which limits the effectiveness of resilience strategies. To advance systems research, we propose a conceptual framework and practical tools to support researchers and decision-makers in evaluating and improving resilience. The paper also outlines future research directions for more robust, adaptive, and integrative resilience assessments.

physics.soc-ph↗

arXiv2025

FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge

Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the complexities of real-world systems. Expert knowledge and qualitative insights, such as those obtained through interviews, ethnographic research, historical accounts, or participatory workshops, are critical in constructing realistic behavioral rules, interactions, and decision-making processes within these models. However, there is a lack of systematic approaches that are able to incorporate both qualitative and quantitative data across the entire modeling cycle. To address this, we propose FREIDA (FRamework for Expert-Informed Data-driven Agent-based models), a systematic mixed-methods framework to develop, train, and validate ABMs, particularly in data-sparse contexts. Our main technical innovation is to extract what we call Expected System Behaviors (ESBs) from qualitative data, which are testable statements that can be evaluated on model simulations. Divided into Calibration Statements (CS) for model calibration and Validation Statements (VS) for model validation, they provide a quantitative scoring mechanism on the same footing as quantitative data. In this way, qualitative insights can inform not only model specification but also its parameterization and assessment of fitness for purpose, which is a long standing challenge. We illustrate the application of FREIDA through a case study of criminal cocaine networks in the Netherlands.

cs.AI↗

arXiv2024

Learning to connect in action: Measuring and understanding the emergence of boundary spanners in volatile times

Collective intelligence of diverse groups is key for tackling many of today's grand challenges such as fostering resilience and climate adaptation. Information exchange across such diverse groups is crucial for collective intelligence, especially in volatile environments. To facilitate inter-group information exchange, Informational Boundary Spanners (IBSs) as pivotal information exchange 'hubs' are promising. However, the mechanisms that drive the emergence of IBSs remain poorly understood. To address this gap there is first a need for a method to identify and measure the emergence of IBSs. Second, an Agent-Based Modelling (ABM) framework is not available to systematically study mechanisms for the emergence of IBSs in volatile environments. Third, even though the ability to learn who provides high-quality information is thought to be essential to explain the emergence of IBSs, a rigorous test of this mechanism is missing. The learning mechanism is formalized using an ABM framework, with the model's outputs analyzed using the proposed IBS emergence measurement method. To illustrate both the method and the learning mechanism, we present a case study focused on information sharing in the volatile environment of a disaster. The study shows that learning constitutes a mechanism for the emergence of effective IBSs in (a) low-volatility environments characterised by low uncertainty and (b) in high-volatility environments characterised by rapid change if the number of inter-group connections is sufficient. With the method and model, this paper aims to lay the foundations for exploring mechanisms for the emergence of IBSs that facilitate inter-group information exchange. This article advances collective intelligence by providing the essential elements for measuring and understanding the emergence of IBSs and exploring the effect of learning on their emergence in volatile environments.

cs.MA↗

arXiv2024

Towards participatory multi-modeling for policy support across domains and scales: a systematic procedure for integral multi-model design

Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scales or relying on different modeling paradigms offer a potential solution. Such multi-models can be assembled from existing computational models (i.e., integrated modeling) or be designed conceptually as a whole before their computational implementation (i.e., integral modeling). Integral modeling is particularly valuable for novel policy problems, such as those faced in the early stages of a pandemic, where relevant models may be unavailable or lack standard documentation. Designing such multi-models through an integral approach is, however, a complex task requiring the collaboration of modelers and experts from various domains. In this collaborative effort, modelers must precisely define the domain knowledge needed from experts and establish a systematic procedure for translating such knowledge into a multi-model. Yet, these requirements and systematic procedures are currently lacking for multi-models that are both multiscale and multi-paradigm. We address this challenge by introducing a procedure for developing multi-models with an integral approach based on clearly defined domain knowledge requirements derived from literature. We illustrate this procedure using the case of school closure policies in the Netherlands during the COVID-19 pandemic, revealing their potential implications in the short and long term and across the healthcare and educational domains. The requirements and procedure provided in this article advance the application of integral multi-modeling for policy support in multiscale and multidomain contexts.

stat.ME↗