For a
System to have
an
Implicit
Model means embodying “statistical regularities of its world in
its physical and functional composition”Kirchhoff et al. (2018), 4
, which reduces surprise about how its world behaves and
helps the system stay in stable states.
In the case of biological systems, i.e. organisms or agents,
the agent’s phenotype embodies evidence of the very environmental dynamics that it and its progenitors over both phylogenetic and ontogenetic timescales have successfully adapted to. […] [B]eing a Model of the Environment does not imply any need to represent the environment […].Pezzulo & Sims (2021), 7817
[D]iverse phenotypes amount to multiple hypotheses about what might ‘work’; each individual is a hypothesis or model of what should occupy this ecological niche, and must compete for selection under pressure from the environment.Friston (2017)
In other words,
the morphology, bio-physical mechanics and neural architecture of the organism all constitute an agent’s model, and that these parameters (or parts) can be tuned and augmented by selection, learning and experience.Kirchhoff et al. (2018), 4
In this sense,
the body of a fish can be considered to be an implicit model of the fluid dynamics and other affordances of its watery environment.Seth (2015), 6
If we think about models in this way, it becomes clear that every
system is a model of its environment – at least every stable system
that survives in its environment.This insights was already expressed in the so called
“Good Regulator Theorem” from classical cybernetics (Conant & Ashby
1970): “Every good regulator of a system must be a model of that
system.”
References
- Conant & Ashby (1970): “Every good regulator of a system must be a model of that system”
- Friston (2017): “The mathematics of mind-time”
- Pezzulo & Sims (2021): “Modelling ourselves: what the free energy principle reveals about our implicit notions of representation”
- Kirchhoff et al. (2018): “The Markov blankets of life: autonomy, active inference and the free energy principle”
- Seth (2015): “The Cybernetic Bayesian Brain: From Interoceptive Inference to Sensorimotor Contingencies”