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Igor Hołowacz

Publications and source records attributed to Igor Hołowacz.

2 recordsLinked to original sources

I see you, do you see me? Perception-based crowdedness and behavioral responses in pedestrian dynamics

Pedestrian traffic is commonly characterized using local density, yet the interactions experienced by individuals depend on the relative positions and perceptual relevance of surrounding pedestrians. This raises the question of whether behavioral relationships inferred from local crowdedness are robust to the representation of perceptual anisotropy, and how interaction geometry shapes pedestrian adaptation over time. We analyze experimental pedestrian crossing flows over angles from 0 to 180 degrees using a distance-weighted measure of local crowdedness. Perceptual anisotropy is varied by reducing the contribution of pedestrians outside the focal pedestrian's field of view. We examine the temporal evolution of crowdedness and its relationships with velocity, directional deviation, and acceleration. Anisotropy primarily changes the numerical scale of crowdedness, while the qualitative dynamics, temporal progression, and crossing-angle dependence remain largely preserved. Pedestrians deviate appreciably from their expected group directions, but changes between successive walking directions remain small, indicating adaptation through smooth, incremental corrections rather than abrupt turns. Acceleration dynamics reveal an asymmetry between disruption and recovery: initial deceleration varies strongly with crossing geometry, whereas recovery accelerations are more similar across angles. Non-retracing trajectories in the behavioral phase spaces show that similar instantaneous conditions can correspond to different phases of the interaction. Overall, interaction geometry has a stronger influence on the organization of crossing flows than the perceptual weighting used to quantify local crowdedness. More broadly, dynamic fundamental diagrams provide a more complete characterization of transient pedestrian interactions than conventional relationships based on instantaneous state variables alone.

physics.soc-ph

Beyond Individual Influence: The Role of Echo Chambers and Community Seeding in the Multilayer three state q-Voter Model

The diffusion of complex opinions is severely hindered in multilayer social networks by echo chambers and cognitive consistency mechanisms. We investigate Influence Maximization strategies within the 3-state multilayer q-voter model. Utilizing the mABCD benchmark, we simulate social environments ranging from integrated Open Worlds to segregated Fortress Worlds. Our results reveal a topological paradox that we term the "Fortress Trap". In highly modular networks, strategies maximizing local density such as Clique Influence Maximization (CIM) and k-Shell fail to trigger global cascades, creating isolated bunkers of consensus due to the Overkill Effect. Furthermore, we identify a Redundancy Trap in perfectly aligned Clan topologies, where the structural overlap of layers creates a "Perfect Prison," rendering it the most resistant environment to diffusion. We demonstrate that VoteRank, a strategy that prioritizes diversity of reach over local intensity, consistently outperforms structure-based methods. These findings suggest that, for complex contagion, maximizing topological entropy is more effective than reinforcing local clusters.

cs.SI