Parameters in collective decision making models : estimation and sensitivity

Tom A. B. Snijders; Evelien P. H. Zeggelink; Frans N. Stokman

Mathématiques et Sciences Humaines (1997)

  • Volume: 137, page 81-99
  • ISSN: 0987-6936

Abstract

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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.

How to cite

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Snijders, Tom A. B., Zeggelink, Evelien P. H., and Stokman, Frans N.. "Parameters in collective decision making models : estimation and sensitivity." Mathématiques et Sciences Humaines 137 (1997): 81-99. <http://eudml.org/doc/94497>.

@article{Snijders1997,
abstract = {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.},
author = {Snijders, Tom A. B., Zeggelink, Evelien P. H., Stokman, Frans N.},
journal = {Mathématiques et Sciences Humaines},
keywords = {Decision Theory; Game Theory; Networks; Process; Social Sciences; Stochastic Processes; Voting},
language = {eng},
pages = {81-99},
publisher = {Ecole des hautes-études en sciences sociales},
title = {Parameters in collective decision making models : estimation and sensitivity},
url = {http://eudml.org/doc/94497},
volume = {137},
year = {1997},
}

TY - JOUR
AU - Snijders, Tom A. B.
AU - Zeggelink, Evelien P. H.
AU - Stokman, Frans N.
TI - Parameters in collective decision making models : estimation and sensitivity
JO - Mathématiques et Sciences Humaines
PY - 1997
PB - Ecole des hautes-études en sciences sociales
VL - 137
SP - 81
EP - 99
AB - 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.
LA - eng
KW - Decision Theory; Game Theory; Networks; Process; Social Sciences; Stochastic Processes; Voting
UR - http://eudml.org/doc/94497
ER -

References

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