Abstract
The free energy principle (FEP) is sometimes put forward as accounting for biological self-organization and cognition. It states that for a system to maintain non-equilibrium steady-state with its environment it can be described as minimising its free energy. It is said to be entirely scale-free, applying to anything from particles to organisms, and interactive machines, spanning from the abiotic to the biotic. Because the FEP is so general in its application, one might wonder whether this framework can capture anything specific to biology. We take steps to correct for this here. We first explicate the worry, taking pebbles as examples of an abiotic system, and then discuss to what extent the FEP can distinguish its dynamics from an organism’s. We articulate the notion of ‘autonomy as precarious operational closure’ from the enactive literature, and investigate how it can be unpacked within the FEP. This enables the FEP to delineate between the abiotic and the biotic; avoiding the pebble worry that keeps it out of touch with the living systems we encounter in the world.
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Notes
The term ‘non-equilibrium steady-state’ refers to self-sustaining processes in a system requiring input and output to avoid relaxing into thermodynamic equilibrium (= systemic decay/death). It is important to mention here that the notion ‘steady-state’ in non-equilibrium systems is an approximation to some specified duration of time—e.g., circadian rhythms over a 24 h clock cycle or the homeostatic processes involved in maintaining on average and over time a specific body temperature. So strictly speaking, biological systems are not in steady states; rather, to say that a system is in a steady-state, X, at a particular time, is effectively to say that the probability density over the system’s states during some period of time was X.
See Colombo and Wright (2018) for criticism on the viability of this application onto an organismic system.
A functional is a function of a function.
The extent to which this story should be taken in a realist sense so that each biotic system literally performs advanced statistical operations, or in an instrumentalist sense so that each biotic system’s interactional dynamics merely correspond to (or ‘instantiate’) the dynamics described in Bayesian inference is still debated (van Es, 2020; see Ramstead et al., 2019b; Corcoran et al., 2020). A discussion of this debate is outside the scope of this paper, and it is unnecessary for our current purposes.
We could, for example, determine the surface molecules of the pebble to be sensory states, adjacent molecules to be active states, and the remainder of the pebble’s molecules to be internal states, with the environment cast as external states. The molecules we cast as active states are then shielded from influence of the external states, while still able to influence the external states, though vicariously through sensory states. Of course, a pebble is merely an example and this could apply to many abiotic systems. Thanks to an anonymous reviewer for pointing this out.
Autonomy is a central theoretical construct of the enactive approach to life and mind (Varela, 1979; Varela et al., 1991; Thompson 2007; Di Paolo & Thompson 2014; Di Paolo et al. 2017). Enactivism is a theoretical framework with roots in theoretical biology, dynamic systems theory, and phenomenology. In enactivism, the notion of autonomy as operational closure has received special attention in attempting to unearth the self-organisational dynamics essential to life. Yet the literature so far has fallen short of construing operational closure in terms of the FEP’s conceptual toolkit. Here we will make a first attempt at conceiving of an operationally closed system as being composed of a network of Markov blanketed systems that stand in a mutually enabling relation to one another.
Our treatment of the pebble case may seem disanalogous with our treatment of the cell case. The discussion of the cell case treated a few important internal processes such as metabolism and membrane-generation next to the external processes concerned with exchanges with the environment. Our take on the pebble case seems to lack in internal counterparts to the external processes. This speaks to what the operational closure formalism indicates, which is that the pebble simply is not an operationally closed system. This means that, in terms of this formalism, there is no ‘internal’ to speak of that could operate (semi-)independently of the external processes.
Thanks to an anonymous reviewer for pointing this out.
See Friston’s (2019) unpublished manuscript for a description in technical detail.
What this means is that the grey area is not inherently a fault, yet conceding this does not help us in distinguishing the pebble clearly from the organism.
Contrary to, say, Friston (2013), the condition of a system being at NESS with its environment seems to have replaced the initial clause of being locally ergodic (see Friston 2019; but also Hipólito, 2019; Ramstead et al., 2019a). Discussion of this change and its philosophical implications are outside of the scope of this paper, but see Bruineberg et al. (2020) for preliminary discussions.
Thanks to an anonymous reviewer for pointing this out, which inspired further considerations regarding operational closure as well.
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Acknowledgements
Kirchhoff’s work was supported by an Australian Research Council Discovery Project “Mind in Skilled Performance” (DP170102987). Van Es’s work was supported by the Research Foundation Flanders (Grant No. 1124818N). We would like to thank Mel Andrews, Mads Julian Dengsø and two anonymous reviewers for comments on a previous draft of this paper.
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van Es, T., Kirchhoff, M.D. Between pebbles and organisms: weaving autonomy into the Markov blanket. Synthese 199, 6623–6644 (2021). https://doi.org/10.1007/s11229-021-03084-w
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DOI: https://doi.org/10.1007/s11229-021-03084-w