Models can greatly aid in thinking about systems: A model can be a stake in the ground to disagree with. A model can be something to help evaluate the properties it exhibits. A model can be used for ensuring that the thinking around the model is consistent with reality, for visualizing of just for general communicating an idea among numerous other uses. Some use cases however takes the potential impact of the models much further.
A model can be formal, in between or just ad hoc, depending on the need. Formal models can be expressed in mathematical notation and even queueing models in some cases, not to mention UML. Based on the model various calculations can be performed to draw new conclusions apart from communicating the idea behind the system depicted by the model. The model can live on the back of a napkin, on paper, on a whiteboard, in mathematics, in computer programs or in an AI implementation generated by machine learning, or just as an abstract concept in ones mind.
With a well equipped mental model you can more easily spot counterfactual misinformation or other issues. Models can however host various kind of bias. When the underlying assumptions are flawed, also the model will be flawed. The model can be too simple to capture what is seen as crucial, like for example when missing feedback loops.
In the obvious domain it is simple to create models of well behaved systems. As a system grows more complex, the difficulties grow. Modelling a chaotic system, or a system in disorder should be seen as virtually impossible.
Constructing a model used to be a human activity, and the impact somewhat limited, but with the spread of AI / ML the origins of models and use of models will be breaking more new ground. There has been a lot of discussion about the importance of model explainability, fairness and transparency, and i support the idea, although i'm skeptic that this will have the desired result of reducing harm. The outcomes of the use of models need to be tested, evaluated and verified from a very diverse set of points of view, including those affected by the model. The currently proposed regulation and limitations seems prone to circumvention. Perhaps part of the solution is that models with impact to humans need to be evolved to become better over time.
Any model will always be imperfect in some aspect. There is no such thing as a perfect model, except for the system itself in a very limited sense. Some models will even cause harm. Modelling a complex system can newer capture the full detail of the system. Some models are however still useful.
