On effective temperature in network models of collective behavior

被引:10
|
作者
Porfiri, Maurizio [1 ]
Ariel, Gil [2 ]
机构
[1] NYU, Dept Mech & Aerosp Engn, Brooklyn, NY 11201 USA
[2] Bar Ilan Univ, Dept Math, IL-5290002 Ramat Gan, Israel
基金
美国国家科学基金会;
关键词
SELF-PROPELLED PARTICLES; PHASE-TRANSITION; DRIVEN PARTICLES; LINEAR-ANALYSIS; DYNAMICS; SYSTEMS; MOTION; VICSEK; SYNCHRONIZATION; NOISE;
D O I
10.1063/1.4946775
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Collective behavior of self-propelled units is studied analytically within the Vectorial Network Model (VNM), a mean-field approximation of the well-known Vicsek model. We propose a dynamical systems framework to study the stochastic dynamics of the VNM in the presence of general additive noise. We establish that a single parameter, which is a linear function of the circular mean of the noise, controls the macroscopic phase of the system-ordered or disordered. By establishing a fluctuation-dissipation relation, we posit that this parameter can be regarded as an effective temperature of collective behavior. The exact critical temperature is obtained analytically for systems with small connectivity, equivalent to low-density ensembles of self-propelled units. Numerical simulations are conducted to demonstrate the applicability of this new notion of effective temperature to the Vicsek model. The identification of an effective temperature of collective behavior is an important step toward understanding order-disorder phase transitions, informing consistent coarse-graining techniques and explaining the physics underlying the emergence of collective phenomena. Published by AIP Publishing.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Methods for the effective study of collective behavior in a radial arm maze
    Delcourt, Johann
    Miller, Noam Y.
    Couzin, Iain D.
    Garnier, Simon
    BEHAVIOR RESEARCH METHODS, 2018, 50 (04) : 1673 - 1685
  • [32] Network models of criminal behavior
    Duke University, Department of Mechanical Engineering and Materials Science, Box 90300, Durham, NC 27708-0005, United States
    不详
    不详
    不详
    不详
    IEEE Control Syst Mag, 2008, 4 (65-77):
  • [33] Phototaxis in Cyanobacteria: From Mutants to Models of Collective Behavior
    Menon, Shakti N.
    Varuni, P.
    Bunbury, Freddy
    Bhaya, Devaki
    Menon, Gautam I.
    MBIO, 2021, 12 (06):
  • [34] Methods for the effective study of collective behavior in a radial arm maze
    Johann Delcourt
    Noam Y. Miller
    Iain D. Couzin
    Simon Garnier
    Behavior Research Methods, 2018, 50 : 1673 - 1685
  • [35] Information Flow in a Boolean Network Model of Collective Behavior
    Porfiri, Maurizio
    IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2018, 5 (04): : 1864 - 1874
  • [36] The learning system of collective behavior in students' social network
    Xie, Ying
    Luo, Bin
    Xu, Rongbin
    COMPUTERS & ELECTRICAL ENGINEERING, 2013, 39 (08) : 2521 - 2530
  • [37] Lyapunov analysis of collective behavior in a network of chaotic elements
    Morita, S
    PHYSICS LETTERS A, 1997, 226 (3-4) : 172 - 178
  • [38] Searching for Collective Behavior in a Large Network of Sensory Neurons
    Tkacik, Gasper
    Marre, Olivier
    Amodei, Dario
    Schneidman, Elad
    Bialek, William
    Berry, Michael J., II
    PLOS COMPUTATIONAL BIOLOGY, 2014, 10 (01)
  • [39] On network backbone extraction for modeling online collective behavior
    Ferreira, Carlos Henrique Gomes
    Murai, Fabricio
    Silva, Ana P. C.
    Trevisan, Martino
    Vassio, Luca
    Drago, Idilio
    Mellia, Marco
    Almeida, Jussara M.
    PLOS ONE, 2022, 17 (09):
  • [40] Adding network structure onto the map of collective behavior
    Fortunato, Santo
    Saramaki, Jari
    Onnela, Jukka-Pekka
    BEHAVIORAL AND BRAIN SCIENCES, 2014, 37 (01) : 82 - 83