arXiv subjects

Pasquale Casaburi

Publications and source records attributed to Pasquale Casaburi.

3 recordsLinked to original sources

arXiv2026

Data as Commodity: a Game-Theoretic Principle for Information Pricing

Data is the central commodity of the digital economy. Unlike physical goods, data exhibits properties that defy the standard theory of supply and demand: it is non-rival (the same dataset can be sold to multiple buyers without degradation), it is replicable at near-zero cost, and it is traded under heterogeneous licensing rules that restrict lawful use. Determining a new pricing principle to attach a fair price tag to datasets is therefore a difficult but central problem. We propose a game-theoretic framework in which the value of a data string emerges from strategic competition among $N$ players betting on a stochastic process with asymmetric information about past outcomes. A better-informed player may either exploit her advantage or sell part of her dataset to less informed competitors. By analytically deriving the Nash equilibrium, we identify the price range for a mutually beneficial trade. The model reveals market dynamics that depart from textbook intuition: informed players may compete or jointly exploit the least informed; data can be shared even at zero price without reducing the seller`s utility; rivalry among well-informed players can benefit uninformed ones; and trades infeasible in small markets can be viable in larger ones. These findings establish a theoretical foundation for the pricing of intangible goods in interacting digital markets, which are in need of robust valuation principles.

physics.soc-ph↗

arXiv2026

Largest eigenvalue and top eigenvector statistics of large Euclidean random matrices

Euclidean random matrices arise in a wide range of physical systems where interactions are determined by spatial configurations, including disordered media and cooperative phenomena in atomic ensembles. Unlike classical random matrix ensembles, their entries are strongly correlated through the geometry of the underlying random points, making their analytical treatment challenging. While global spectral properties such as the spectral density are relatively well understood, much less is known about extremal eigenvalues and the associated eigenvectors, despite their central role in applications. Here we address the problem of characterising the largest eigenvalue and the corresponding top eigenvector of large Euclidean random matrices with a generic symmetric kernel. For vectors in any dimension $d\geq 1$ drawn independently from a common distribution, we show that both quantities can be computed within a unified replica-based framework. In the large-$N$ limit, the replica saddle-point equations reduce to an eigenvalue problem for a Fredholm integral operator determined by the kernel and by the underlying distribution. We then specialise this general formulation to the quadratic distance kernel, for which the Fredholm problem reduces to a finite set of $d+2$ self-consistent equations. In this case, we obtain an explicit expression for the average largest eigenvalue, fully determined by low-order moments of the underlying distribution, and an analytical characterisation of the density of the top eigenvector's components. We further perform extensive numerical simulations that confirm these predictions. More broadly, our work provides a general framework to access extremal spectral properties of Euclidean random matrices.

cond-mat.stat-mech↗

arXiv2024

Resilience of mobility network to dynamic population response across COVID-19 interventions: evidences from Chile

The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time. Less is known about how different population strata changed their response to repeated interventions and how this impacted the resulting mobility network. We study population response during the first and second infection waves of the COVID-19 pandemic in Chile and Spain. Via spatial lag and regression models, we investigate the adherence to mobility interventions at the municipality level in Chile, highlighting the significant role of wealth, labor structure, COVID-19 incidence, and network metrics characterizing business-as-usual municipality connectivity in shaping mobility changes during the two waves. We assess network structural similarities in the two periods by defining mobility hotspots and traveling probabilities in the two countries. As a proof of concept, we simulate and compare outcomes of an epidemic diffusion occurring in the two waves. Our analysis reveals the resilience of the mobility network across waves. We test the robustness of our findings recovering similar results for Spain. Finally, epidemic modeling suggests that historical mobility data from past waves can be leveraged to inform future disease spatial invasion models in repeated interventions. This study highlights the value of historical mobile phone data for building pandemic preparedness and lessens the need for real-time data streams for risk assessment and outbreak response. Our work provides valuable insights into the complex interplay of factors driving mobility across repeated interventions, aiding in developing targeted mitigation strategies.

physics.soc-ph↗