Les modèles de simulation des processus de formation de décisions collectives sont fondés sur des aperçus théoriques et empiriques du processus de décision mais contiennent des paramètres dont les valeurs sont déterminées ad hoc. Certains de ces paramètres, du modèle d'accès dynamique sont discutés et il est proposé de compléter la fonction d'utilité par un terme aléatoire dont la variance serait un paramètre inconnu. Ces paramètres peuvent être estimés en confrontant aux données les prévisions du modèle qui peuvent être des décisions mais aussi des structures relationnelles générées comme un élément du processus de prise de décision. Etant donné la nature stochastique du modèle, l'estimation de ces paramètres peut être obtenue par l'algorithme de Robins Monro. Cet ajustement n'est pas absolument direct. Il faut choisir les statistiques sur lesquelles se fonde l'estimation des paramètres. Il n'est pas certain a priori que l'équation d'estimation admette une solution et que l'algorithme de Robins Monro converge. La méthode est illustrée par des données concernant la restructuration financière d'une grande société.
Simulation models for collective decision making are based on theoretical and empirical insight in the decision making process, but still contain a number of parameters of which the values are determined ad hoc. For the dynamic access model, some of such parameters are discussed, and it is proposed to extend the utility functions with a random term of which the variance also is an unknown parameter. These parameters can be estimated by fitting model predictions to data, where the predictions can refer to decision outcomes but also to network structure generated as a part of the decision making process. Given the stochastic nature of the model, this parameter estimation can be carried out with the Robbins Monro process. Such fitting is not completely straightforward: statistics must be chosen on which to base the parameter estimation, it is not certain a priori that there will be a solution to the estimating equation and that the Robbins Monro process will converge. The method is illustrated with data from the financial restructuring of a large company.
@article{MSH_1997__137__81_0,
author = {Snijders, Tom A. B. and Zeggelink, Evelien P. H. and Stokman, Frans N.},
title = {Parameters in collective decision making models : estimation and sensitivity},
journal = {Math\'ematiques et Sciences humaines},
volume = {140},
year = {1997},
pages = {81-99},
zbl = {01072406},
language = {en},
url = {http://dml.mathdoc.fr/item/MSH_1997__137__81_0}
}
Snijders, Tom A. B.; Zeggelink, Evelien P. H.; Stokman, Frans N. Parameters in collective decision making models : estimation and sensitivity. Mathématiques et Sciences humaines, Tome 140 (1997) pp. 81-99. http://gdmltest.u-ga.fr/item/MSH_1997__137__81_0/
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