scholarly journals Lévy walk process in self-organization of pedestrian crowds

2019 ◽  
Vol 16 (153) ◽  
pp. 20180939 ◽  
Author(s):  
Hisashi Murakami ◽  
Claudio Feliciani ◽  
Katsuhiro Nishinari
Keyword(s):  
Self Organization ◽  
Pedestrian Flow ◽  
Direct Path ◽  
Distributed Resources ◽  
Scale Free ◽  
Lévy Walk ◽  
Self Organized ◽  
Levy Walk ◽  
Movement Strategy ◽  
Lane Formation

Similar to other animal groups, human crowds exhibit various collective patterns that emerge from self-organization. Recent studies have emphasized that individuals anticipate their neighbours' motions to seek their paths in dynamical pedestrian flow. This path-seeking behaviour results in deviation of pedestrians from their desired directions (i.e. the direct path to their destination). However, the strategies that individuals adopt for the behaviour and how the deviation of individual movements impact the emergent organization are poorly understood. We here show that the path-seeking behaviour is performed through a scale-free movement strategy called a Lévy walk, which might facilitate transition to the group-level behaviour. In an experiment of lane formation, a striking example of self-organized patterning in human crowds, we observed how flows of oppositely moving pedestrians spontaneously separate into several unidirectional lanes. We found that before (but not after) lane formation, pedestrians deviate from the desired direction by Lévy walk process, which is considered optimal when searching unpredictably distributed resources. Pedestrians balance a trade-off between seeking their direct paths and reaching their goals as quickly as possible; they may achieve their optimal paths through Lévy walk process, facilitating the emergent lane formation.

scholarly journals Mutual anticipation can contribute to self-organization in human crowds

2020 ◽  
Author(s):  
Hisashi Murakami ◽  
Claudio Feliciani ◽  
Yuta Nishiyama ◽  
Katsuhiro Nishinari
Keyword(s):  
Collective Behavior ◽  
Movement Pattern ◽  
Self Organization ◽  
Scale Free ◽  
Crowd Management ◽  
Self Organized ◽  
Seeking Behavior ◽  
Physical Particle ◽  
Lane Formation ◽  
Walking Speeds

AbstractHuman crowds provide paradigmatic examples of collective behavior emerging through self-organization. Although the underlying interaction has been considered to obey the distance-dependent law, resembling physical particle systems, recent findings emphasized that pedestrian motions are fundamentally influenced by the anticipated future positions of their neighbors rather than their current positions. Therefore, anticipatory interaction may play a crucial role in collective patterning. However, whether and how individual anticipation functionally benefits the group is not well-understood. We suggest that collective patterning in human crowds is promoted by anticipatory path-seeking behavior resulting in a scale-free movement pattern, called the Lévy walk. In our experiments of lane formation, a striking example of self-organized patterning in human crowds where people moving in opposite directions spontaneously segregate into several unidirectional lanes, we manipulated some pedestrians’ ability to anticipate by having them type on a mobile phone while walking. The manipulation slowed overall walking speeds and delayed the onset of global patterning, and the distracted pedestrians sometimes failed to achieve their usual walking strategy. Moreover, we observed that the delay of global patterning depends on decisions made by pedestrians who were moving toward the distracted ones and had no choice but to take sudden large steps, presumably because of difficulty in anticipating the motions of their counterparts. These results imply that mutual anticipation between pedestrians facilitates efficient transition to emergent patterning in situations where nobody within a crowd is distracted. Our findings may contribute to efficient crowd management and inform future models of self-organizing systems.

SELF-ORGANIZED CORONA GRAPHS: A DETERMINISTIC COMPLEX NETWORK MODEL WITH HIERARCHICAL STRUCTURE

2019 ◽  
Vol 22 (06) ◽  
pp. 1950019
Author(s):  
ROHAN SHARMA ◽  
BIBHAS ADHIKARI ◽  
TYLL KRUEGER
Keyword(s):  
Power Law ◽  
Small World ◽  
Self Organization ◽  
Scale Free ◽  
Self Organized ◽  
Power Law Exponent ◽  
Growing Networks ◽  
Growing Network ◽  
Hierarchical Pattern ◽  
Corona Graphs

In this paper, we propose a self-organization mechanism for newly appeared nodes during the formation of corona graphs that define a hierarchical pattern in the resulting corona graphs and we call it self-organized corona graphs (SoCG). We show that the degree distribution of SoCG follows power-law in its tail with power-law exponent approximately 2. We also show that the diameter is less equal to 4 for SoCG defined by any seed graph and for certain seed graphs, the diameter remains constant during its formation. We derive lower bounds of clustering coefficients of SoCG defined by certain seed graphs. Thus, the proposed SoCG can be considered as a growing network generative model which is defined by using the corona graphs and a self-organization process such that the resulting graphs are scale-free small-world highly clustered growing networks. The SoCG defined by a seed graph can also be considered as a network with a desired motif which is the seed graph itself.

scholarly journals Signatures of active and passive optimized Lévy searching in jellyfish

2014 ◽  
Vol 11 (99) ◽  
pp. 20140665 ◽  
Author(s):  
Andy M. Reynolds
Keyword(s):  
Simulated Annealing ◽  
Water Column ◽  
Movement Patterns ◽  
Search Theory ◽  
Search Pattern ◽  
Global Maximum ◽  
Distributed Resources ◽  
Lévy Walk ◽  
Lévy Walks ◽  
Levy Walk

Some of the strongest empirical support for Lévy search theory has come from telemetry data for the dive patterns of marine predators (sharks, bony fishes, sea turtles and penguins). The dive patterns of the unusually large jellyfish Rhizostoma octopus do, however, sit outside of current Lévy search theory which predicts that a single search strategy is optimal. When searching the water column, the movement patterns of these jellyfish change over time. Movement bouts can be approximated by a variety of Lévy and Brownian (exponential) walks. The adaptive value of this variation is not known. On some occasions movement pattern data are consistent with the jellyfish prospecting away from a preferred depth, not finding an improvement in conditions elsewhere and so returning to their original depth. This ‘bounce’ behaviour also sits outside of current Lévy walk search theory. Here, it is shown that the jellyfish movement patterns are consistent with their using optimized ‘fast simulated annealing’—a novel kind of Lévy walk search pattern—to locate the maximum prey concentration in the water column and/or to locate the strongest of many olfactory trails emanating from more distant prey. Fast simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a large search space. This new finding shows that the notion of active optimized Lévy walk searching is not limited to the search for randomly and sparsely distributed resources, as previously thought, but can be extended to embrace other scenarios, including that of the jellyfish R. octopus . In the presence of convective currents, it could become energetically favourable to search the water column by riding the convective currents. Here, it is shown that these passive movements can be represented accurately by Lévy walks of the type occasionally seen in R. octopus . This result vividly illustrates that Lévy walks are not necessarily the result of selection pressures for advantageous searching behaviour but can instead arise freely and naturally from simple processes. It also shows that the family of Lévy walkers is vastly larger than previously thought and includes spores, pollens, seeds and minute wingless arthropods that on warm days disperse passively within the atmospheric boundary layer.

ERRATA: FERMIONIC AND BOSONIC SELF-ORGANIZED NETWORKS: A REPRESENTATION OF QUANTUM STATISTICS

2001 ◽  
Vol 15 (03) ◽  
pp. 313-320 ◽  
Author(s):  
GINESTRA BIANCONI
Keyword(s):  
Cayley Tree ◽  
Self Organization ◽  
High Tc Superconductors ◽  
Quantum Distribution ◽  
High Tc ◽  
Scale Free ◽  
New Class ◽  
Self Organized ◽  
The Right ◽  
Quantum Distributions

A new class of self organized networks is described that is relevant to understand the emerging order in a large number of complex systems such as biological systems, the web, and heterogeneous phases in high Tc superconductors. The Bose and Fermi quantum distributions are shown to be the right tool to describe the two extreme limit distributions, the scale-free and the Cayley-tree network respectively. The new class of self-organized networks is described by a 'mixed' quantum distribution. Here the bosonic and fermionic types of self organization coexists, maintaining the same 'ergodic' nature.

Improving heuristics-based model to reproduce lane formation

2018 ◽  
Vol 29 (08) ◽  
pp. 1850069 ◽  
Author(s):  
Ning Guo ◽  
Hao-Xiang Liu ◽  
Rui Jiang ◽  
Bin Jia ◽  
Mao-Bin Hu
Keyword(s):  
Objective Function ◽  
Pedestrian Flow ◽  
Target Direction ◽  
Self Organized ◽  
Wide Range ◽  
Lane Formation ◽  
Simulation Results

Lane formation is an important self-organized phenomenon in bidirectional pedestrian flow. Our experiment shows that in a wide range of pedestrian density, quick lane formation can be observed in a ring corridor. It is shown that the original heuristics-based model fails to reproduce lane formation with the increase of pedestrian density. This is because a pedestrian cannot correctly evaluate the target direction, when he/she is too close to others. We propose an improved heuristics-based model, in which the objective function of the target direction has been modified. Simulation results are in agreement with experimental ones.

scholarly journals Scale-free versus multi-scale: Statistical analysis of livestock mobility patterns across species

2016 ◽  
Author(s):  
Takuto Sakamoto ◽  
Lloyd Sanders ◽  
Nobu Inazumi
Keyword(s):  
Strong Support ◽  
Spatial Behavior ◽  
Mobility Patterns ◽  
Exponential Distributions ◽  
Scale Free ◽  
Movement Trajectories ◽  
Multi Scale ◽  
Lévy Walk ◽  
Quantitative Studies ◽  
Levy Walk

ABSTRACTIn quantitative studies on animal movements and foraging, there has been ongoing debate over the relevance of Lévy walk and related stochastic models to understanding mobility patterns of diverse organisms. In this study, we collected and analyzed a large number of GPS logs that tracked the movements of different livestock species in northwestern Kenya. Statistically principled analysis has only found limited evidence for the scale-free movement patterns of the Lévy walk and its variants, even though most of the tracked movements clearly show super-diffusive behavior within the relevant temporal duration. Instead, the analysis has given strong support to composite exponential distributions (composite Brownian walks) as the best description of livestock movement trajectories in a wide array of parameter settings. Furthermore, this support has become overwhelming and near universal under an alternative criterion for model selection. These results illuminate the multi-scale and multi-modal nature of livestock spatial behavior. They also have broader theoretical and empirical implications for the related literature.

scholarly journals Self-Organization Toward Criticality by Synaptic Plasticity

2021 ◽  
Vol 9 ◽  
Author(s):  
Roxana Zeraati ◽  
Viola Priesemann ◽  
Anna Levina
Keyword(s):  
Synaptic Plasticity ◽  
Network Models ◽  
Self Organization ◽  
Scale Free ◽  
Neural Recordings ◽  
Self Organized ◽  
Self Organized Criticality ◽  
The Brain ◽  
Universal Mechanism

Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics toward a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network’s dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.

Diffusion

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Random Walks ◽  
Spectral Properties ◽  
Diffusion Constant ◽  
Network Structures ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Lévy Walk ◽  
Number Of Layers ◽  
Levy Walk ◽  
Speed Up

This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the improved navigability of multilayer networks with more layers. These results are here compared with the results of traffic on multilayer networks that, on the contrary, point out that increasing the number of layers could be detrimental and could lead to congestion.

scholarly journals Self-replication of a quantum artificial organism driven by single-photon pulses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Valente
Keyword(s):  
Self Assembly ◽  
Artificial Life ◽  
Single Photon ◽  
Self Organization ◽  
Physical Systems ◽  
Self Organized ◽  
Living Organisms ◽  
A Chain ◽  
Thermodynamic Mechanism ◽  
Self Replication

AbstractImitating the transition from inanimate to living matter is a longstanding challenge. Artificial life has achieved computer programs that self-replicate, mutate, compete and evolve, but lacks self-organized hardwares akin to the self-assembly of the first living cells. Nonequilibrium thermodynamics has achieved lifelike self-organization in diverse physical systems, but has not yet met the open-ended evolution of living organisms. Here, I look for the emergence of an artificial-life code in a nonequilibrium physical system undergoing self-organization. I devise a toy model where the onset of self-replication of a quantum artificial organism (a chain of lambda systems) is owing to single-photon pulses added to a zero-temperature environment. I find that spontaneous mutations during self-replication are unavoidable in this model, due to rare but finite absorption of off-resonant photons. I also show that the replication probability is proportional to the absorbed work from the photon, thereby fulfilling a dissipative adaptation (a thermodynamic mechanism underlying lifelike self-organization). These results hint at self-replication as the scenario where dissipative adaptation (pointing towards convergence) coexists with open-ended evolution (pointing towards divergence).

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