Modelling Choice Behaviour Using Artificial Intelligence

doi:10.1017/9781108939010.022

17 - Modelling Choice Behaviour Using Artificial Intelligence

from Part III - Algorithmic Behaviour

Published online by Cambridge University Press: 29 May 2026

Andreas Glöckner

Edited by

Ganna Pogrebna and

Thomas T. Hills

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Ganna Pogrebna: Affiliation:
The Alan Turing Institute
Thomas T. Hills: Affiliation:
University of Warwick

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Summary

Individuals have a surprisingly high capacity for making decisions quickly and still considering a multitude of information. This capability – often referred to as intuition – relies on automatic processes that can be described with neural networks. Particularly parallel constraint satisfaction (PCS) networks – a specific type of interactive activation networks – have been successful in capturing multiple aspects of choice behaviour. PCS models include restrictions to neural networks that capture specific features of cognition. This chapter will describe how PCS and other content models of decision-making can be evaluated and potentially improved by using artificial intelligence, specifically generic multi-layer (deep learning) neural network models. It will exemplify how choice behaviour can be modelled and predicted with PCS. The predictive performance of PCS will be contrasted with that of a generic neural network model. Possibilities and implications for the improvement of content models for choice behaviour using artificial intelligence are discussed.

Keywords

artificial intelligence neural networks parallel constraint satisfaction methods

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Type: Chapter
Information: The Cambridge Handbook of Behavioural Data Science , pp. 306 - 320
DOI: https://doi.org/10.1017/9781108939010.022 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2026

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