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It is common in origins of life research to view the first stages of life as the passive result of particular environmental conditions. This paper considers the alternative possibility: that the antecedents of life were already actively regulating their environment to maintain the conditions necessary for their own persistence. In support of this proposal, we describe ‘viability-based behaviour’: a way that simple entities can adaptively regulate their environment in response to their health, and in so doing, increase the likelihood of their survival. Drawing on empirical investigations of simple self-preserving abiological systems, we argue that these viability-based behaviours are simple enough to precede neo-Darwinian evolution. We also explain how their operation can reduce the demanding requirements that mainstream theories place upon the environment(s) in which life emerged.

Under what circumstances can a system be said to have beliefs and goals, and how do such agency-related features relate to its physical state? Recent work has proposed a notion of interpretation map, a function that maps the state of a system to a probability distribution representing its beliefs about an external world. Such a map is not completely arbitrary, as the beliefs it attributes to the system must evolve over time in a manner that is consistent with Bayes' theorem, and consequently the dynamics of a system constrain its possible interpretations. Here we build on this approach, proposing a notion of interpretation not just in terms of beliefs but in terms of goals and actions. To do this we make use of the existing theory of partially observable Markov processes (POMDPs): we say that a system can be interpreted as a solution to a POMDP if it not only admits an interpretation map describing its beliefs about the hidden state of a POMDP but also takes actions that are optimal according to its belief state. An agent is then a system together with an interpretation of this system as a POMDP solution. Although POMDPs are not the only possible formulation of what it means to have a goal, this nevertheless represents a step towards a more general formal definition of what it means for a system to be an agent., Comment: 17 pages, no figures, to be presented at 3rd International Workshop on Active Inference 2022

A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world. These can be seen as an agent's beliefs, expressed as a Bayesian prior or posterior. Here we begin the development of a general theory that would tell us when it is appropriate to interpret states as representing beliefs in this way. We focus on the case in which a system can be interpreted as performing either Bayesian filtering or Bayesian inference. We provide formal definitions of what it means for such an interpretation to exist, using techniques from category theory., Comment: 11 pages + 26 pages appendix, to be published in the proceedings of the 2nd International Workshop on Active Inference 2021

We propose an alternative to the prevailing two origin of life narratives, one based on a replicator first hypothesis, and one based on a metabolism first hypothesis. Both hypotheses have known difficulties: All known evolvable molecular replicators such as RNA require complex chemical (enzymatic) machinery for the replication process. Likewise, contemporary cellular metabolisms require several enzymatically catalyzed steps, and it is difficult to identify a non-enzymatic path to their realization. We propose that there must have been precursors to both replication and metabolism that enable a form of selection to take place through action of simple chemical and physical processes. We model a concrete example of such a process, repeated sequestration of binary molecular combinations after exposure to an environment with a broad distribution of chemical components, as might be realized experimentally in in a repeated wet-dry cycle. We show that the repeated sequestration dynamics results in a selective amplification of a very small subset of molecular species present in the environment, thus providing a candidate primordial selection process., Comment: 7 pages, 6 figures

Are we evolving into a new Post-Industrial epoch driven by the emerging living and intelligent technologies, spearheaded by the digital revolution? Or are we just entering a new period in the Industrial epoch, where we don’t need to radically update our institutions, the market and our cultural narratives? Using an evolutionary perspective and time series analysis we seek to investigate these questions. We report a study of the long term dynamics of the economy across 28 countries and across different socio-technical epochs, some dating back to 1270, while others starting at the onset of the Industrial Revolution. Using standard time series analysis methods we report overall growth dynamics as well as rhythmic phenomena in the growth patterns after the onset of the Industrial Revolution. Our main data sources include the Maddison Project [4], Edvinson [1], Our Wold in Data [7] and updated data references in the cited literature. Assuming these evolutionary changes of our societies are driven by fundamental inventions that are capitalized through innovation when the socio-economic climate is appropriate (Mensch) [5], we can use a simple mathematical model (Mosekilde et al.) [6] to interpret and to better understand the underlying dynamics for the empirical data as well as provide informed, tentative forcasts for the future (Rosenlyst et al.) [9].