Frank P. Pijpers

By analysing the clustering of bankruptcy activity from administrative register data in the Netherlands over time, the intermittency of this process is investigated. Using methodology from survival analysis a statistical characterization of bursts is obtained: short active periods alternating with quieter periods. Using Statistics Netherlands microdata on Dutch business bankruptcies declared on court hearingdays from 2009 to 2024, combined with input-output tables from the Dutch national accounts, it is studied whether this clustering can be explained by differences between sectors, years, and intersectoral exposure. Sector differences explain most of the heterogeneity in the data, while year effects matter to a lesser extent and the inter-sectoral exposure variables constructed from the input-output tables further improve model fit.

We study dynamic random graphs in which the set of nodes is fixed, but edges evolve over time according to an underlying stochastic mechanism. Using a maximum-entropy approach, we define a probability distribution on graph trajectories that is consistent with observed constraints, capturing the inherent uncertainty in partially observed networks. We introduce a moment-based estimator for the parameters of this distribution and establish its statistical properties, such as consistency and asymptotic normality, with explicit formulas for the covariance structure. Numerical experiments demonstrate the estimator’s accuracy and robustness across various dynamic network scenarios. Our framework bridges probabilistic modeling and statistical inference in time-varying networks, providing practical tools for understanding and predicting complex edge dynamics.

We describe a novel aspect of the structure of the hexagonal phase of crystalline water ice, using vacuum-ultraviolet absorption spectroscopy as a diagnostic tool. The key result is that hexagonal ice (I_h), prepared from condensation of pure water vapour, displays net molecular orientation. This reflects order in the disposition of the protons in ice I_h and our results require revision of the near century old conventional structure in which water molecular dipoles point in random directions in hexagonal ice. Orientation gives rise to spontaneous polarization and internal fields of ∼4 × 10⁶ V m⁻¹ in films of ice I_h tens of nanometres thick, condensed on an insulating MgF₂ substrate. Data are in the form of Stark shifts in absorption spectra, with no free charges or external electric fields introduced in the experiment.

So as to assess systemic economic value and risk, it is of crucial interest to develop methods to detect interwoven production chains in large-scale economies. Commodity transactions between firms induce a complex network in which it is challenging to identify production chains, not only due to the size of the underlying network but also because of its inherent cyclical connections and loops. We present a novel method, Restricted Gradient Extraction (RGE), which is based on Hodge decomposition, which is capable of extracting the gradient flow of a production network. The RGE method, being of relatively low computational complexity, is demonstrated to both a synthetic and real country-sized production network. Application of RGE on the syntactic data set shows that the resulting gradient flow is a directed acyclic graph, a weighted subgraph of the original network, and the gradient flow is retained. Application to the economic production network of the Netherlands shows that production chains can be readily detected and described. The method is applicable to weighted directed networks in general and is not limited to economic production networks.

Sociotechnical systems, where technological and human elements interact in a goal-oriented manner, provide important functional support to our societies. Here, we draw attention to the underappreciated concept of timeliness—i.e., system elements being available at the right place at the right time—that has been ubiquitously and integrally adopted as a quality standard in the modus operandi of sociotechnical systems. We point out that a variety of incentives, often reinforced by competitive pressures, prompt system operators to myopically optimize for efficiencies, running the risk of inadvertently taking timeliness to the limit of its operational performance, correspondingly making the system critically fragile to perturbations by pushing the entire system toward the proverbial “edge of a cliff.” Invoking a stylized model for operational delays, we identify the limiting operational performance of timeliness, as a true critical point, where the smallest of perturbations can lead to a systemic collapse. Specifically for firm-to-firm production networks, we suggest that the proximity to critical fragility is an important ingredient for understanding the fundamental “excess volatility puzzle” in economics. Further, in generality for optimizing sociotechnical systems, we propose that critical fragility is a crucial aspect in managing the trade-off between efficiency and robustness.