arXiv subjects

Roy Lindelauf

Publications and source records attributed to Roy Lindelauf.

5 recordsLinked to original sources

arXiv2026

Network-based modeling of cocaine trafficking flows and displacement effects

The worldwide cocaine market is undergoing an extraordinary surge. Insights on how cocaine is trafficked from production countries to consumer markets are limited, and often based on observed trafficking routes (e.g., seizures) alone. In this paper, we introduce a network-based model of actual transnational cocaine flows, beyond flows seized. In this model, cocaine is routed through an international transportation network of land and sea connections, where each link is assigned an interception risk metric for containerized transport. This interception risk metric combines features related to law enforcement inspections, containerized trade, and transport connectivity into three key driving components of interception risk using Principal Component Analysis (PCA). The interception risk metric on each link is then constructed from these driving components, where the importance of each component is characterized by a strength parameter. Given a set of strength parameters, cocaine is assumed to be routed from production countries to consumption countries via routes that minimize the total risk of interception, subject to country-specific supply and demand constraints. In the end, the strength parameters are calibrated by comparing the corresponding flow outcomes with known trafficking activity. From these final trafficking flows associated with the calibrated parameters, we are able to identify `blind spots' (e.g., nonzero flows on links with small or none observed trafficking activity) and provide more evidence of the waterbed effect (e.g., changing routes as a result of increased interdiction efforts by law enforcement).

physics.soc-ph↗

arXiv2026

DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization

Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. Existing approaches rely on Gaussian Process (GP) surrogates, which provide strong performance but limited interpretability. This limits real-world usability in high-stakes domains, such as healthcare, where interpretability and trust are essential. We propose DT-PBO, a novel tree-based surrogate model for PBO that is inherently interpretable while capturing preference uncertainty. Specifically, we introduce a novel splitting heuristic that constructs interpretable shallow decision trees directly from pairwise comparison data, and use Laplace approximation to obtain probabilistic estimates for each leaf. This enables efficient preference modeling without sacrificing interpretability. Across eight benchmark functions, our method achieves competitive convergence to GP-based PBO, particularly on functions with rugged optimization landscapes. Additional experiments show robustness against noise and a fast computational running time. Experiments on real-world datasets further demonstrate that our model provides interpretable insights into decision-maker preferences that would remain opaque under GP-based approaches.

cs.LG↗

arXiv2024

General Lotto Games with Scouts: Information versus Strength

We introduce General Lotto games with Scouts: a General Lotto game with asymmetric information. There are two players, Red and Blue, who both allocate resources to a field. However, scouting capabilities afford Blue to gain information, with some probability, on the number of Red's resources before allocating his own. We derive optimal strategies for this game in the case of a single field. In addition we provide upper and lower bounds of the value of the game in a multi-stage case with multiple battlefields. We devise several ways to characterise the influence of information versus strength. We conclude by drawing qualitative insights from these characterisations and the game values, and draw parallels with military practice.

cs.GT↗

arXiv2020

"Stay Nearby or Get Checked": A Covid-19 Lockdown Exit Strategy

This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid-19 across nations, regions, and provinces, providing some model validation. Results suggest that the proposed strategy may significantly flatten a second wave. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. Public policy may target long ties by heavily focusing medical testing and mobility tracking efforts on traffic and transport. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.

cs.SI↗

arXiv2014

Power Grid Defense Against Malicious Cascading Failure

An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has the capability to launch cyber-attacks as practical concerns (i.e. avoiding disruption of service, presence of legacy systems, etc.) may hinder security. Hence, a defender can harden the security posture at certain power stations but may lack the time and resources to do this for the entire power grid. We model a power grid as a graph and introduce the cascading failure game in which both the defender and attacker choose a subset of power stations such as to minimize (maximize) the number of consumers having access to producers of power. We formalize problems for identifying both mixed and deterministic strategies for both players, prove complexity results under a variety of different scenarios, identify tractable cases, and develop algorithms for these problems. We also perform an experimental evaluation of the model and game on a real-world power grid network. Empirically, we noted that the game favors the attacker as he benefits more from increased resources than the defender. Further, the minimax defense produces roughly the same expected payoff as an easy-to-compute deterministic load based (DLB) defense when played against a minimax attack strategy. However, DLB performs more poorly than minimax defense when faced with the attacker's best response to DLB. This is likely due to the presence of low-load yet high-payoff nodes, which we also found in our empirical analysis.

cs.CR↗