Models are not true, but some are useful. Models are either used as mental constructs by
individuals or they are used by groups as social constructs. Conceptual modeling has always
focused on socially constructed, explicit representations that are useful for gaining shared
understanding of an affair or even for designing and implementing technical systems, most
of all information systems.
For mental representations of individuals, the working hypothesis of neuroscience is that
mental representations are embossed into neural structures and ultimately into electric
signals. Socially constructed conceptual models require explication by representations
governed by some shared conceptual-modelling grammar (Wand & Weber 2001), i.e., they
become social reality by information in a medium.
Different phases of conceptual modeling have been conducted in the past decades. Initially,
conceptual models were fully controlled and “closed” representations, e.g., frames. This was
followed by a phase of conceptual models that are unrestricted (“open”) for capturing the
richness of human knowledge in general, most of all ontologies. Statistical models and
machine learning models often lack direct connections to individual knowledge and socially
constructed knowledge but emerge from data alone. The underlying assumption is that data
is taken directly from reality and is therefore objective. Recent discussions on biases and
distortions of data raise the question of the social construction of data as well. Current
research on explainability and interpretability tries to build bridges between both fields.
Hybrid models are an attempt in this direction by trying to merge socially constructed
conceptual models with machine learning models. The success of machine learning models
has initiated chip design research to develop dedicated chips that can directly support AI
processing. This might have repercussions on preferred designs of information systems and
machine learning models. Even more advanced are quantum computing and quantum
information theory when transforming data representations into quantum representations
that are accessible by quantum computing algorithms.
In this talk, common aspects between all these fields are discussed and some thoughts on
research questions will be presented. A focus will be laid on the interplay between
conceptual modeling and machine learning models but also some connections to advanced
chip designs and quantum computing are given.