The Consumer Prototype - Explaining the Underlying Psychological Factors of Consumer Behaviour with Artificial Neural Networks

Max N. Greene

2013

Abstract

Consumer behaviour is examined using artificial neural networks as a method of analysis to identify the underlying psychological factors that influence consumer choice. Artificial consumer prototype is developed and consequently studied using supervised and unsupervised neural networks. A number of network architectures are constructed and optimized for comparative purposes. Learning obtained using artificial agents is interpreted and subsequently propagated towards human consumer behaviour. Philosophical issues including the network structure interpretation and appropriateness of using artificial agents to explain human behaviour are discussed.

References

  1. Andersson, F., Aberg, M., & Jacobsson, S., 2000. Algorithmic approaches for studies of variable influence, contribution and selection in neural networks. In Chemometrics and intelligent laboratory systems.
  2. Bishop, C., 1995. Neural networks for pattern recognition: Oxford university press.
  3. Gallant, S. I., 1993. Neural network learning and expert systems: The MIT Press.
  4. Garson, D., 1991. Interpreting neural-network connection weights. In AI expert.
  5. Gevrey, M., Dimopoulos, I., & Lek, S., 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. In Ecological Modelling.
  6. Goh, A., 1995. Back-propagation neural networks for modeling complex systems. In Artificial Intelligence in Engineering.
  7. Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S., 2004. Credit rating analysis with support vector machines and neural networks: a market comparative study. In Decision support systems.
  8. Nord, L. I., & Jacobsson, S. P., 1998. A novel method for examination of the variable contribution to computational neural network models. In Chemometrics and intelligent laboratory systems.
  9. Olden, J. D., & Jackson, D. A., 2001. Fish-habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. In Transactions of the American Fisheries Society.
  10. Olden, J. D., & Jackson, D. A., 2002. A comparison of statistical approaches for modelling fish species distributions. In Freshwater Biology.
  11. Olden, J. D., Joy, M. K., & Death, R. G., 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. In Ecological Modelling.
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Paper Citation


in Harvard Style

N. Greene M. (2013). The Consumer Prototype - Explaining the Underlying Psychological Factors of Consumer Behaviour with Artificial Neural Networks . In Doctoral Consortium - Doctoral Consortium, (IJCCI 2013) ISBN Not Available, pages 38-43


in Bibtex Style

@conference{doctoral consortium13,
author={Max N. Greene},
title={The Consumer Prototype - Explaining the Underlying Psychological Factors of Consumer Behaviour with Artificial Neural Networks},
booktitle={Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)},
year={2013},
pages={38-43},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)
TI - The Consumer Prototype - Explaining the Underlying Psychological Factors of Consumer Behaviour with Artificial Neural Networks
SN - Not Available
AU - N. Greene M.
PY - 2013
SP - 38
EP - 43
DO -