Modelling Team Cohesion during Military Conscription: a Multidimensional Model for Task Cohesion

  • Svajone Bekesiene ,
  • Rasa Smaliukiene,
  • Ramute Vaicaitiene
  • abcThe General Jonas Žemaitis Military Academy of Lithuania, Silo 5a, Vilnius, 10322, Lithuania
Cite as
Bekesiene S., Smaliukiene R., Vaicaitiene R. (2021). Modelling Team Cohesion during Military Conscription: a Multidimensional Model for Task Cohesion. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 25-34. DOI:
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This research aims to predict conscripts’ task cohesion in groups using artificial neural network modelling (NNM). The prediction of task cohesion during military conscription lies on two domains of research. The first is related to team cohesion, its deconstruction, and its measurement, while the second is allied to nonlinear modelling in group behaviour research. To predict this multidimensional and complex phenomenon, the multilayer perceptron (MLP) and the radial basis function (RBF) neural networks are used. As a result, the team cohesion in conscript groups, which is a key variable in conscription service effectiveness, was predicted with high accuracy (MPL MOD2= 88% and RBF MOD8=90%) by the models created. The performed modeling shows that according to MPL MOD2 norm cohesion has 100% of normalized importance, while according to RBF MOD8, interpersonal cohesion is the best predictor (normalized importance=100%) for task cohesion in groups during conscription service. 


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