Parameters in collective decision making models: estimation and sensitivity
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...
Enregistré dans:
| Udgivet i: | URI:https://journals.openedition.org/msh, |
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| Auteurs principaux: | , , |
| Format: | Article ou chapitre numérique |
| Sprog: | Anglais |
| Udgivet: |
Mathématiques et sciences humaines
2006
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| Online adgang: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Summary: | 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. |
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