arXiv subjects

Mike H. Lees

Publications and source records attributed to Mike H. Lees.

3 recordsLinked to original sources

arXiv2026

Slow Context, Fast Symptoms: Multiscale Temporal Dynamics and Context-Induced Coupling in Psychological Systems

Psychological dynamics unfold across multiple timescales: symptoms and other psychological states can change rapidly, whereas social, environmental, biological, and developmental conditions often evolve more slowly. We formulate this structure as a stochastic slow-fast system in which binary symptom states form a fast interacting network embedded within a slow contextual field. Pairwise symptom coupling governs interactions within the fast layer, while the contextual field shifts symptom-specific activation tendencies and can itself receive feedback from sustained symptom activation. Simulations show that changes in the slow field can shift the macroscopic activation state of the symptom system even when the underlying interaction matrix remains fixed. Perturbations to the field generate transient increases in symptom activation followed by recovery, while feedback between the fast and slow layers delays recovery and, when sufficiently strong, produces dependence on initial conditions. We further show that when between-person variation in the contextual field is omitted from network estimation, the inferred system exhibits stronger total coupling and nonzero couplings between symptom pairs that are uncoupled in the data-generating model. Thus, slowly varying context can alter both the dynamics and the apparent interaction structure of a fast psychological system. The framework connects psychological network models with slow-fast dynamical systems and provides a formal basis for distinguishing changes in activation from changes in coupling.

physics.soc-ph↗

arXiv2025

The Paradox of Intervention: Resilience in Adaptive Multi-Role Coordination Networks

Complex adaptive networks exhibit remarkable resilience, driven by the dynamic interplay of structure (interactions) and function (state). While static-network analyses offer valuable insights, understanding how structure and function co-evolve under external interventions is critical for explaining system-level adaptation. Using a unique dataset of clandestine criminal networks, we combine empirical observations with computational modeling to test the impact of various interventions on network adaptation. Our analysis examines how networks with specialized roles adapt and form emergent structures to optimize cost-benefit trade-offs. We find that emergent sparsely connected networks exhibit greater resilience, revealing a security-efficiency trade-off. Notably, interventions can trigger a "criminal opacity amplification" effect, where criminal activity increases despite reduced network visibility. While node isolation fragments networks, it strengthens remaining active ties. In contrast, deactivating nodes (analogous to social reintegration) can unintentionally boost criminal coordination, increasing activity or connectivity. Failed interventions often lead to temporary functional surges before reverting to baseline. Surprisingly, stimulating connectivity destabilizes networks. Effective interventions require precise calibration to node roles, connection types, and external conditions. These findings challenge conventional assumptions about connectivity and intervention efficacy in complex adaptive systems across diverse domains.

physics.soc-ph↗

arXiv2019

SEVA: A Data driven model of Electric Vehicle Charging Behavior

Governments and cities around the world are currently facing rapid growth in the use of Electric Vehicles and therewith the need for Charging Infrastructure. For these cities, the struggle remains how to further roll out charging infrastructure in the most efficient way, both in terms of cost and use. Forecasting models are not able to predict more long-term developments, and as such more complex simulation models offer opportunities to simulate various scenarios. Agent based simulation models provide insight into the effects of incentives and roll-out strategies before they are implemented in practice and thus allow for scenario testing. This paper describes the build up of an agent based model that enables policy makers to anticipate on charging infrastructure development. The model is able to simulate charging transactions of individual users and is both calibrated and validated using a dataset of charging transactions from the public charging infrastructure of the four largest cities in the Netherlands.

physics.soc-ph↗