Abstract

(remote)

Biology’s roots in the description of diversity and function have led to a fact-rich empirical understanding of life, which unpacks to statistical reconstructions of Natural History and quite sophisticated mechanistic analyses of structure and function in living systems. The Darwinian pivot, from the previous pinnacle of Humboldtian naturalism and explicitly contra Lamarck, created, from whole cloth, a category of cause acting after the generation of change-events, and parallel to impetus-related notions, in which cause is the origin of change events, that had been the basis of physical theorizing from John of Alexandria through Galileo and then Newton. Darwinian ex-post causation through selection is a close cousin to notions of cause now extensively developed in information theory, which have also back-filled the modern physical theory of matter via thermodynamic ideas.

The foregoing facts, methods, and concepts, as they have been used so far, provide a partial window on the origin of life and its major evolutionary patterns, and an even less-complete framing of the nature of the living state. They have proved to be frustratingly limiting, however, to address questions of the likelihood of life’s emerging as a stage in planetary maturation, of the chance or necessity of any particular living feature, and of the appropriate notion of cause to account for life as a distinctive natural phenomenon. An argument can be made that some of the ideas we need to better address these questions have been developed outside biology, in areas of basic physics, computer science, control engineering, and information theory, where they could be discovered in simpler and more symmetric contexts (and even there, slowly and painfully over more than a century!) and that we can now bring them back into a unified science that includes questions of biological origin, evolution, and nature.

Much of my work has amounted to looking for small, concrete questions about chance, necessity, and cause for different features of life, in which I can give proofs-of-concept for framing the questions in terms of probability, hardness or cost of search, robustness, and related criteria, and use combinatorial tools to propose causal analyses. I have looked at universal core metabolism through this lens for some decades, and recently turned to the origins of macromolecules. Tools include the statistical mechanics of stochastic population processes with stoichiometry (e.g. Chemical Reaction Networks) or generative combinatorial chemistries (e.g. graph grammars). Other interests include basic maths of likelihood in spaces of histories and events, to understand how we should extend thermodynamic concepts, and approaches such as Large Deviation Theory, to the many contexts away from equilibrium where energy conservation may appear but need not be fundamental any longer. Here thermodynamics becomes recognizable as a theory about counting, measuring, and information—albeit discovered and named for the effort to understand heat-generated movement — but not fundamentally about, or limited to, that phenomenal domain.

An overarching question is how we should reason theoretically about complex phenomena in which causation is linked across many scales, in the early stages (where we still are) in which data and essential concepts and insights are still fragmentary and too incomplete to assemble into any coherent worldview.