Understanding and learning to reconcile differences between disciplines through constructing an artificial personality

The purpose of this paper is to investigate the conflicting varieties between cognitive linguistics and animal motivation on one hand and between emotions and motivations on another, and to show how the construction of an artificial personality led to understanding the differences from a cybernetic perspective, and to reconcile and benefit from them.

The design of the artificial personality is built on a core of dual regulator structure as a model of motivation and cognition. The regulating core does not address areas of linguistics and emotions, thus the need to interface with functions from well-researched disciplines in these areas as peripheries to the core. Different disciplines were viewed from a cybernetic perspective, where the variety of categories primarily used in these disciplines was compared numerically and the problem was defined as a search for methods to reducing the varieties between disciplines.

The interfaces between the core of artificial personality and the peripheries are seen as either regulators, which reduce variety, or generators of variety.

The approach to reconcile cross-disciplinary differences based on comparing the numerical variety of categories is understood to be original. The reduction of comparison to numerical counts removes hard-to-reconcile qualitative differences and retains the simplicity of quantitative differences. Qualitative cross-disciplinary differences benefit the specialists and protect them from competition with each other. By reconciling cross-disciplinary differences the artificial personality develops across disciplines and achieves multi-disciplinary transparency. The specialists may not welcome the competition but science, technology and society in general will benefit from reduced duplication, improved information flow and integration. This work is a small step in understanding and learning to reconcile differences between disciplines.