Abstract
Richard Levins’s (Am Sci 54(4):421–431, 1966) paper sets a landmark for the significance of scientific model-making in biology. Colombo and Palacios (Biol Philos 36(5):1–26. 10.1007/S10539-021-09818-X, 2021) have recently built their critique of the explanatory power of the Free Energy Principle on Levins’s insight into the relationship between generality, realism, and precision. This paper addresses the issue of the plausibility of biological explanations that are grounded in the Free Energy Principle (FEP) and deals with the question of the realist fortitude of FEP’s theoretical framework. It indicates that what is required for establishing the plausibility of the explanation of a target system given a model of that system is the dosage or the harmony between the generality and accuracy of explanatory models. This would also provide a basis for seeing how scientific realism could be a viable option with respect to FEP.
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Notes
The long-term average of self-information or surprisal \(\mathcal{L}(x\left(t\right)\) of the system converges on the ergodic density modelled in terms of Shannon entropy:
$$p\left(x|m\right)={\mathop{\textit{lim}}\limits_{T\to \infty}}\frac{1}{T} \int_{0}^{T} \delta \left(x-x(t)\right)dt$$More generally, it is the attempt at modelling biotic systems as weakly mixing dynamical systems that is called into doubt in their paper. Weak mixing is a stronger statement of ergodicity. Thermodynamics can be developed to explains how the mixed substance would intermingle through the system in equal proportions. Theories of mixing are about measure-preserving dynamical systems that could show ergodic properties, and if a sub-set of such a system visits all parts of the total space in which the system moves, the system can be defined as ergodic.
Markov blankets are Bayesian netweorks that set conditional independence between internal states and external states (or active and sensory states).
HED hypothesizes the degraded capacity of joyous feeling.
IS hypothesis how VTA encodes incentive salient value to events.
RPE specifies computational patterns of encoding of reward prediction error in VTA.
It might be validly argued that because DCMs but not DAGs represent the causal physical underlying mechanisms, they are the right modelling tool in the context of FEP (Hipolito and Kirchhoff 2019). But to give the critics the benefit of doubt, let us grant that DCMs and DAGs are complementary modelling tools, assuming that they “ask and answer fundamentally different questions, so that choosing one or the other (or both) depends on whether one is interested in describing the data in terms of information flow (GCA [Granger causal analysis]) or exposing the underlying physical-causal mechanism (DCM)” (Seth et al. 2015, p. 3296).
This is the principle of least action:
$$\begin{aligned}r(t)&={\varphi }_{R}^{*}(t) ({x}_{0})=arg\,min_{r}\mathcal{L}(s(t))\iff {\partial }_{r}\mathcal{L}(s(t))=0\iff {\delta }_{r}\mathcal{S}=0 \\ \mathcal{S}&=\int_{0}^{T}dt\mathcal{L}(s(t))\end{aligned}$$And Friston (2012, p. 2110) articulates FEP in terms of the principle of least action thus:
$$2. r(t)={\varphi }_{R}^{*}(t) ({x}_{0})=arg\,min_{r}\mathcal{F}(x(t))\Rightarrow {\delta }_{r }\mathcal{S}=0$$In this context, action \(\mathcal{S}\) (or surprise) is the path integral of the Lagrangian, and \({\varphi *}_{R}(t) ({x}_{0})\) minimises the entropy of the ergodic density over external states (Friston 2012, p. 2108).
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This paper is dedicated, with respect and love, to the women of Iran.
I thank two anonymous referees of this journal for their constructive comments. I also thank Stephan Hartmann for a long discussion of realism about the free energy principle.
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Beni, M.D. Dosis sola facit venenum: reconceptualising biological realism. Biol Philos 37, 54 (2022). https://doi.org/10.1007/s10539-022-09884-9
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DOI: https://doi.org/10.1007/s10539-022-09884-9