Neural Network Modeling for Organizational Psychology

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Author(s): Eliano Pessa (University of Pavia, Italy)
Copyright: 2016
Pages: 13
Source title: Relational Methodologies and Epistemology in Economics and Management Sciences
Source Author(s)/Editor(s): Lucio Biggiero (Università dell’ Aquila, Italy), Pier Paolo Angelini (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Mario Basevi (Italian National Institute of Statistics, Italy), Nunzia Carbonara (Politecnico di Bari, Italy), Antonio Mastrogiorgio (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Eliano Pessa (University of Pavia, Italy), Enrico Sevi (Università dell’ Aquila, Italy)and Marco Valente (Università dell’ Aquila, Italy)
DOI: 10.4018/978-1-4666-9770-6.ch011

Keywords: Business & Management / Business Science Reference / Economics & Economic Theory / Economics and Economic Theory

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Abstract

The nature itself of organizational psychology makes the study and modeling of emergence processes the key topic of this science. In this regard we can distinguish between two kinds of emergence: the one related to individual constructs and the other to collective constructs. In the former case the presence of suitable individual and contextual features gives rise to the emergence of suitable individual attitudes of holistic nature. In the latter case the features of single individuals belonging to a group, and reciprocally interacting, give rise to the occurrence of collective features and phenomena. In the last years both kinds of emergence have been studied through computational models. In this chapter we focus on the contribution of Artificial Neural Network (ANN) models to this modeling activity. As regards the emergence of individual constructs there is a consistent number of ANN-based models, most of which formulated in terms of recurrent networks. A review of their successes and failures constitutes a first part of the chapter. Instead, the emergence of collective constructs has been so far modelled by resorting to agent-based models. However, in recent times the ANN models have begun to be used with increasing frequency in this field. Namely, each agent can be modelled in an easier way by representing its structure through a suitable neural network. The final part of the chapter is, therefore, devoted to the problems underlying the use of ANNs as constituents of agent models.

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