MONNET: a software system for modular neural networks based on object passing

Modular neural networks integrate several neural networks and possibly standard processing methods. Tackling such models is a challenge, since various modules have to be combined, either sequentially or in parallel, and the simulations are time critical in many cases. For this, specific tools are prerequisite that are both flexible and efficient. We have developed the MONNET software system that supports the investigation of complex modular models. The design of MONNET is based on the object oriented paradigm, the environment is C++/UNIX. The basic concepts are dynamic modularity, object passing, scalability, reusability, and extensibility. MONNET features flexible and compact definition of complex simulations, and minimal overhead in order to run computationally demanding simulations efficiently

Explaining the underlying psychological factors of consumer behaviour with artificial neural networks

This thesis intends to advance our understanding of consumer behaviour, and proposes an extension to the theoretical and methodological framework of the Behavioural Perspective Model. Drawing on the intellectual tradition of connectionism and employing advanced artificial neural network modelling techniques, the research programme described here assesses the appropriateness of connectionist architectures in explaining consumer behaviour. This thesis traces the developments in the fields of consumer behaviour analysis to critically evaluate the significance of limitations inherited from radical behaviourism, and proposes a hybrid connectionist approach to address these methodological constraints. The study is both highly quantitative and interpretative in nature, and generates a large body of empirical evidence to support the methodological and theoretical deliberations. Two types of data are used here: a simulated dataset to assess the capacity of the pruning algorithms to reveal the underlying relations within the data; and a consumer panel dataset to which the neural network algorithms are applied to develop predictive, descriptive, and interpretative connectionist models that aim to explain the consumer purchasing decision-making process. Even though it is beyond the scope of this research project to propose mechanisms to explain all elements of consumer purchasing decision and it will therefore remain to be addressed as part of an ongoing collaborative research programme, the main conclusion to be drawn from this work is that the connectionist framework and artificial neural networks can be considered a significant contribution to the extension of theoretical and philosophical framework of intentional behaviourism