arXiv subjects

Pierpaolo Massoli

Publications and source records attributed to Pierpaolo Massoli.

3 recordsLinked to original sources

arXiv2026

Modelling Territorial Dynamics through Statistical Mechanics: An Application to Resident Foreign Population

This study proposes an energy-based framework for Official Statistics aimed at investigating the local stability of territorial socio-demographic configurations. The observed distribution of a continuous variable is interpreted as the reference configuration of an interacting territorial system, where endogenous interactions among local units and exogenous socio-economic drivers jointly determine the underlying energy landscape. Socio-economic characteristics are summarized through interpretable composite indices and synthesized by Principal Component Analysis to construct a univariate external field acting on the territorial system. Two complementary formulations are investigated: a Continuous Ising model and a stochastic alternative based on Langevin dynamics. Both models are simulated through Monte Carlo procedures with Simulated Annealing to explore energetically favourable configurations within the local neighbourhood of the observed state. Rather than searching for a global optimum, the proposed framework investigates local perturbations of the reference configuration in order to analyse the influence of socio-economic drivers acting as external forces on territorial dynamics. The stochastic exploration is analysed through the stability of the energy trajectories, the descriptive agreement between observed and simulated territorial configurations, and the statistical properties of the stationary distributions. Uncertainty is quantified through a model-agnostic Conformal Prediction framework employed as a diagnostic tool for evaluating the variability of stationary simulated configurations. The methodology is illustrated through the analysis of the distribution of the resident foreign population across Italian municipalities, but it is formulated in general terms and is applicable to a broad class of territorial systems in Official Statistics.

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arXiv2026

An Energy-Driven Framework for Privacy-Aware Synthetic Data Generation

The increasing demand for access to microdata in official statistics and data-intensive applications raises important challenges concerning disclosure risk, inferential validity and preservation of statistical utility. This paper proposes an interpretable energy-driven framework for privacy-aware synthetic data generation in mixed-type data. The proposed methodology combines discriminative modelling, Bayesian-Network proposal mechanisms, Metropolis--Hastings sampling and post-generation optimization within a constrained probabilistic framework. Unlike perturbation-based approaches, privacy-aware behaviour is achieved through constrained stochastic exploration guided by explicit plausibility, privacy, diversity and structural-coherence penalties. The framework is specifically designed for mixed-type tabular data characterized by sparse configurations, heterogeneous variable types and complex multivariate dependency structures. The generation process is formulated as a multi-objective sampling problem balancing statistical fidelity and disclosure-risk while preserving predictive utility. An extensive empirical evaluation is conducted using a mixed-type individual-level dataset containing demographic, behavioural and health-related variables. The validation strategy combines statistical fidelity diagnostics, predictive analyses, diversity measures, nearest-neighbour risk analysis, membership inference attacks and Split Conformal Prediction. The empirical results suggest that the proposed framework is capable of preserving a substantial portion of the predictive and multivariate structure of the original data while limiting exact memorization phenomena and maintaining favourable privacy-aware behaviour. The proposed methodology provides an interpretable framework for synthetic data generation under competing utility and privacy constraints.

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arXiv2025

Unveiling Complex Territorial Socio-Economic Dynamics: A Statistical Mechanics Approach

This study proposes a novel approach based on the Ising model for analyzing socio-economic emerging patterns between municipalities by investigating the observed configuration of a network of selected territorial units which are classified as being central hubs or peripheral areas. This is interpreted as being a reference of a system of interacting territorial binary units. The socio-economic structure of the municipalities is synthesized into interpretable composite indices, which are further aggregated by means of Principal Components Analysis in order to reduce dimensionality and construct a univariate external field compatible with the Ising framework. Monte Carlo simulations via parallel computing are conducted adopting a Simulated Annealing variant of the classic Metropolis-Hastings algorithm. This ensures an efficient local exploration of the configuration space in the neighbourhood of the reference of the system. Model consistency is assessed both in terms of energy stability and the likelihood of these configurations. The comparison between observed configuration and simulated ones is crucial in the analysis of multivariate phenomena, concomitantly accounting for territorial interactions. Model uncertainty in estimating the probability of each municipality being a central hub or peripheral area is quantified by adopting the model-agnostic Conformal Prediction framework which yields adaptive intervals with guaranteed coverage. The innovative use of geographical maps of the prediction intervals renders this approach an effective tool. It combines statistical mechanics, multivariate analysis and uncertainty quantification, providing a robust and interpretable framework for modeling socio-economic territorial dynamics, with potential applications in Official Statistics.

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