This chapter overviews existing applications of agent-based modeling (ABMg) in organization science, pointing to possible cross-contaminations of these research fields. The reviewed applications include the garbage can model of organizational choice, the usage of cellular automata and of the NK model in order to investigate various problems of organizational interdependencies, and realistic agent-based models of agile productive plants. Possible future applications may include employing unsupervised neural networks in applied research on organizational routines, as well as employing sophisticated models of organizational evolution in order to understand such neglected features as punctuated equilibria and exaptation. Given the scope of the research agendas that ABMg can provide, it is quite surprising that this tool has been largely ignored by organization science hitherto. One possible explanation is that ABMg, which presents itself as a computational technique, inadvertently conceives its very nature of a tool for the exploration of novel research hypotheses. It is eventually perceived by non-practitioners as one more statistical technique for the validation of given hypotheses, and possibly a needlessly complex one.
Fioretti, G. (2016). Emergent Organizations. Cham Heidelberg New York Dordrecht London : Springer International Publishing [10.1007/978-3-319-18153-0_2].
Emergent Organizations
FIORETTI, GUIDO
2016
Abstract
This chapter overviews existing applications of agent-based modeling (ABMg) in organization science, pointing to possible cross-contaminations of these research fields. The reviewed applications include the garbage can model of organizational choice, the usage of cellular automata and of the NK model in order to investigate various problems of organizational interdependencies, and realistic agent-based models of agile productive plants. Possible future applications may include employing unsupervised neural networks in applied research on organizational routines, as well as employing sophisticated models of organizational evolution in order to understand such neglected features as punctuated equilibria and exaptation. Given the scope of the research agendas that ABMg can provide, it is quite surprising that this tool has been largely ignored by organization science hitherto. One possible explanation is that ABMg, which presents itself as a computational technique, inadvertently conceives its very nature of a tool for the exploration of novel research hypotheses. It is eventually perceived by non-practitioners as one more statistical technique for the validation of given hypotheses, and possibly a needlessly complex one.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
