An agent-based opinion dynamics model with a language model-based belief system

Abstract

As society's mutual problems grow, there is an increasing demand for understanding the intra- and inter-cultural differences. Rising polarization within national borders and stranded dialogues between nations on mutual problems, daily reach the headlines. It is argued that current models of opinion only scratch the surface of actual human opinion formation with the traditional value based exact approach. Developments in the domain of natural language processing highlight the overlap between real world cultural biases and biased language models. Although within their own domain these biases are seen as problematic, it is argued that it is exactly this associative prejudice that can be used to model human like opinion formation. This idea is emphasized by an conceptual approach on opinion, belief and knowledge, in which only the probability of an association distinguishes between 'objective' and 'subjective'. Through this concept, and by using identified significant cognitive tendencies, a framework is developed for inferring an opinion from text. Using this framework, an opinion model based on a language model is proposed. The contribution of this thesis is two-fold. Firstly, the proposed model provides evidence that agent perception through language is a significant model element, in which the bias in a language model can possibly be exploited to approach cultural perception. Secondly, as the bridge between the two domains has not yet been build, the findings of the results as well as the research process give direction for much needed future research.