(2018). “Multiple Realization and Robustness.” In Bertolaso, Caianiello, and Serrelli (eds.), Biological Robustness: Emerging perspectives from within the Life Sciences. Springer, pp. 75-94.
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Functional Robustness: A New Framework for Multiple Realization and its Epistemic Consequences
Brains exhibit remarkable capacities to maintain functions despite substantial variation in the component parts and processes that support those functions. This robustness of neural functions can be found at all levels of organization within the brain. For example, individual neurons show stable electrophysiological properties despite variation in the ion channels that determine those properties. Neural circuits produce stable outputs despite variation in the synaptic strengths between and intrinsic activity of the cells that make up those circuits. And neuroplasticity can enable recovery of function from macroscale damage to entire cortical areas. These different forms of neural robustness are imminently relevant to anyone interested in understanding the mind-brain relation, explanation in neuroscience, and the relationships between different levels of organization in complex systems.
Philosophical debates about the mind-brain relation have, however, failed to make substantial contact with this phenomenon of functional robustness. This is particularly puzzling given that the concept of multiple realization has been central to these debates since the 1970s. In broad terms, multiple realization is the claim that higher-level properties correspond to a number of distinct lower-level properties. And it is typically cited as a crucial premise in arguments against reductionism and in arguments looking to secure the autonomy of the so-called special sciences from the physical sciences. Functional robustness, at least on its face, would seem to be of patent relevance to multiple realization, as it demonstrates a clear case in which there is stability at the level of the function performed, despite variation in the causal structures that support performance of that function.
Philosophical accounts of multiple realization have, however, had a blind spot to the types of cases functional robustness presents. Particularly in the context of the mind-brain sciences, these accounts have tended to focus on the possibility of the same mental state arising in different organisms (e.g. animal pain vs. octopus pain) or in silica (i.e. the possibility of artificial intelligence), whereas functional robustness points toward a sort of causal heterogeneity underlying stable functions within a particular species or even within a particular organism.
Some reasons for the myopia of traditional accounts of multiple realization have to do with historical coincidence of the scientific state of the art at the time that early debates about multiple realization were taking place. For instance, advances in computer science teased the development of artificial intelligence that might bear similarities to human intelligence. And little was understood about the complexity underlying functions within particular neural systems, supporting the assumption that a mental state, like pain, is not multiply realized within a particular species (let alone within a particular organism). This meant looking to computers or other organisms for potential sources of multiple realization, rather than looking at how stable functions are performed within particular organisms.
In this dissertation, I provide a novel account of multiple realization. My account reframes the concept in terms of causal theories of explanation, in contrast to the original framing in terms of the deductive-nomological theory of explanation. I align my account of multiple realization with the phenomenon of functional robustness, particularly by examining a number of cases of robustness in neural systems. I then explore the epistemic consequences of functional robustness. In particular, I argue that systems that exhibit robustness will tend to violate causal faithfulness, thus posing challenges to causal hypothesis testing and causal discovery. I then consider the proposal that robustness undermines modularity – i.e. the ability of causal relationships within a system to be disrupted independently. I argue that it does not and instead that robustness often is due to feedback control driving systems toward particular outcomes. As a result, robustness will attend failures of acyclicity, not failures of modularity. I conclude by contrasting these epistemic consequences of functional robustness with those traditionally associated with multiple realization.