Dynamics of social networks: A deterministic approach

David Pearson; Mark McCartney

International Journal of Applied Mathematics and Computer Science (2002)

  • Volume: 12, Issue: 4, page 545-551
  • ISSN: 1641-876X

Abstract

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Our aim is to model the dynamics of social networks, which comprises the problem of how people get to know each other, like each other, detest each other, etc. Most existing models are stochastic in nature and, obviously, based on random events. Our approach is deterministic and based on ordinary differential equations. This should not be seen as a challenge to stochastic models, but rather as a complement.

How to cite

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Pearson, David, and McCartney, Mark. "Dynamics of social networks: A deterministic approach." International Journal of Applied Mathematics and Computer Science 12.4 (2002): 545-551. <http://eudml.org/doc/207610>.

@article{Pearson2002,
abstract = {Our aim is to model the dynamics of social networks, which comprises the problem of how people get to know each other, like each other, detest each other, etc. Most existing models are stochastic in nature and, obviously, based on random events. Our approach is deterministic and based on ordinary differential equations. This should not be seen as a challenge to stochastic models, but rather as a complement.},
author = {Pearson, David, McCartney, Mark},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {quantitative sociodynamics; dynamic systems; social networks},
language = {eng},
number = {4},
pages = {545-551},
title = {Dynamics of social networks: A deterministic approach},
url = {http://eudml.org/doc/207610},
volume = {12},
year = {2002},
}

TY - JOUR
AU - Pearson, David
AU - McCartney, Mark
TI - Dynamics of social networks: A deterministic approach
JO - International Journal of Applied Mathematics and Computer Science
PY - 2002
VL - 12
IS - 4
SP - 545
EP - 551
AB - Our aim is to model the dynamics of social networks, which comprises the problem of how people get to know each other, like each other, detest each other, etc. Most existing models are stochastic in nature and, obviously, based on random events. Our approach is deterministic and based on ordinary differential equations. This should not be seen as a challenge to stochastic models, but rather as a complement.
LA - eng
KW - quantitative sociodynamics; dynamic systems; social networks
UR - http://eudml.org/doc/207610
ER -

References

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  1. Barber B. (1992): Neofunctionalism and the theory of the social system, In: The Dynamics of Social Systems (P. Colomy, Ed.). - London: Sage, pp. 36-55. 
  2. Gilbert N. and Doran J. (Eds.) (1994): Simulating Societies: The Computer Simulation of Social Phenomena. - London: UCL Press. 
  3. Golub G.H. and Van Loan C.F (1986): Matrix Computations. - London: North Oxford Academic. 
  4. Helbing D. (1995): Quantitative Sociodynamics. - Dordrecht: Kluwer Academic Publishers. 
  5. Lewin K. (1936): Principles of Topological Psychology. - McGraw-Hill. 
  6. Pearson D.W. and Dray G. (2001): A fuzzy approach to sociodynamical interactions. - Proc. Int. Conf. Artificial Neural Networks and Genetic Algorithms, Prague, Czech Republic, pp. 122-125. Zbl1008.91104
  7. Pearson D.W. and Boudarel M.-R. (2001): Pair interactions: Real and perceived attitudes. - J. Artif. Soc. Soc. Simul., Vol. 4, No. 4, www.soc.surrey.ac.uk/JASSS/4/4/4.html. 
  8. Snijders T. and van Duijn M. (1997): Simulation for statistical inference in dynamic network models, In: Simulating Social Phenomena (R. Conte, R. Hegselmann and P. Terno, Eds.). - Berlin: Springer, pp. 493-512. 
  9. Weidlich W. (2000): Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. - Amsterdam: Harwood Academic Publishers. Zbl0978.91074

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