A model is a simplified representation of a System.By defining models as being about Systems, we make a general but highly abstract Ontological Commitment to the actual existence of Systems.
Representation means that a model is “about” or denotes its target system; more specifically, it “stands in” for the system in some functional context in some useful way.
In other words, ==representation is a product of function and selection==, either by design or by evolution:There are two interpretations of this relationship: On the pragmatic account, models represent because of their function as functional devices, on the informational account it is the other way round. In an evolutionary account of implicit models that interprets natural selection as “Bayesian model selection over evolutionary time” (Kirchhoff et al. 2018, 4), the two accounts become complementary descriptions of the underlying evolutionary process.
- Explicit Models are consciously developed representations that can be objects of inspection, discussion and validation. They are built from Concepts and are thus systems of concepts.
- Implicit Models are evolved, embodied representations of a system’s Environment, or more precisely of “statistical regularities of its world in its physical and functional composition”Kirchhoff et al. (2018), 4 , and thus identical with the system itself (or part of it).
A model is ==useful== (and thus gets selected) if it successfully predicts behaviours, i.e. describes parts or approximations of attractors, of the target system. The more accurate the description of the attractors, the more useful the model.
Because An attractor defines a stable system and Systems live in state spaces, ==a model that describes attractors describes ipso facto actual features, i.e. system attributes==. This type of epistemological realism comes for free when we adopt state space realism.
Restated as a claim about knowledge, this can serve as a reconstruction of a concept of verisimilitude, i.e. closeness to truthPopper (1963) : The more accurate my description of the attractors of a system, the more actual features of it will I describe, and hence the closer I will be to the truth about it.
Another way of describing this is to say simplifications can be understood as Abstractions of the target system’s structure and components.
An especially interesting type of models are Causal Models that describe the Causal Structure of their target system.
If models accurately describe system boundaries, they represent real systems. If they don’t, they represent Missing Systems.
References
- Bailer-Jones (2009): Scientific Models in Philosophy of Science
- Frigg & Hartmann (2018): “Models in Science”
- Gelfert (2017): “The Ontology of Models”
- Kirchhoff et al. (2018): “The Markov blankets of life: autonomy, active inference and the free energy principle”
- Pezzulo & Sims (2021): “Modelling ourselves: what the free energy principle reveals about our implicit notions of representation”
- Popper (1963):Conjectures and Refutations: The Growth of Scientific Knowledge