Top-down causation and emergence: some comments on mechanisms

Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) where the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.

The Emergence of Life as a First-Order Phase Transition

It is well known that life on Earth alters its environment over evolutionary and geological timescales. An important open question is whether this is a result of evolutionary optimization or a universal feature of life. In the latter case, the origin of life would be coincident with a shift in environmental conditions. Here we present a model for the emergence of life in which replicators are explicitly coupled to their environment through the recycling of a finite supply of resources. The model exhibits a dynamic, first-order phase transition from nonlife to life, where the life phase is distinguished by selection on replicators. We show that environmental coupling plays an important role in the dynamics of the transition. The transition corresponds to a redistribution of matter in replicators and their environment, driven by selection on replicators, exhibiting an explosive growth in diversity as replicators are selected. The transition is accurately tracked by the mutual information shared between replicators and their environment. In the absence of successfully repartitioning system resources, the transition fails to complete, leading to the possibility of many frustrated trials before life first emerges. Often, the replicators that initiate the transition are not those that are ultimately selected. The results are consistent with the view that life's propensity to shape its environment is indeed a universal feature of replicators, characteristic of the transition from nonlife to life. We discuss the implications of these results for understanding life's emergence and evolutionary transitions more broadly. Key Words: Origin of life-Prebiotic evolution-Astrobiology-Biopolymers-Life. Astrobiology 17, 266-276.

Profiling Project Complexity: Insights from Complexity Science and Project Management Literature

The current understanding of project complexity is limited in that there is neither a widely recognized conceptualization of project complexity nor a convergent view on how to deal with its effects. Drawing on the extant literature concerning project complexity and complexity science, this article develops a coherent and holistic profile of project complexity and provides reflections on its implications for project management theory and practice. This profile serves as a touchstone for practitioners to better understand, assess, and address complexity in their projects and as an aid to researchers in framing their research efforts.

Sequence Data, Phylogenetic Inference, and Implications of Downward Causation

Framing systematics as a field consistent with scientific inquiry entails that inferences of phylogenetic hypotheses have the goal of producing accounts of past causal events that explain differentially shared characters among organisms. Linking observations of characters to inferences occurs by way of why-questions implied by data matrices. Because of their form, why-questions require the use of common-cause theories. Such theories in phylogenetic inferences include natural selection and genetic drift. Selection or drift can explain 'morphological' characters but selection cannot be causally applied to sequences since fitness differences cannot be directly associated with individual nucleotides or amino acids. The relation of selection to sequence data is by way of downward or top-down causation from those phenotypes upon which selection occurs. The application of phylogenetic inference to explain sequence data is thus restricted to instances where drift is the relevant theory; those nucleotides or amino acids that can be explained via downward causation are precluded from inclusion in the data matrix. The restrictions on the inclusion of sequence data in phylogenetic inferences equally apply to species hypotheses, precluding the more restrictive approach known as DNA barcoding. Not being able to discern drift and selection as relevant causal mechanisms can severely constrain the inclusion and explanations of sequence data. Implications of such exclusion are discussed in relation to the requirement of total evidence.

Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

A major goal of regenerative medicine and bioengineering is the regeneration of complex organs, such as limbs, and the capability to create artificial constructs (so-called biobots) with defined morphologies and robust self-repair capabilities. Developmental biology presents remarkable examples of systems that self-assemble and regenerate complex structures toward their correct shape despite significant perturbations. A fundamental challenge is to translate progress in molecular genetics into control of large-scale organismal anatomy, and the field is still searching for an appropriate theoretical paradigm for facilitating control of pattern homeostasis. However, computational neuroscience provides many examples in which cell networks - brains - store memories (e.g., of geometric configurations, rules, and patterns) and coordinate their activity towards proximal and distant goals. In this Perspective, we propose that programming large-scale morphogenesis requires exploiting the information processing by which cellular structures work toward specific shapes. In non-neural cells, as in the brain, bioelectric signaling implements information processing, decision-making, and memory in regulating pattern and its remodeling. Thus, approaches used in computational neuroscience to understand goal-seeking neural systems offer a toolbox of techniques to model and control regenerative pattern formation. Here, we review recent data on developmental bioelectricity as a regulator of patterning, and propose that target morphology could be encoded within tissues as a kind of memory, using the same molecular mechanisms and algorithms so successfully exploited by the brain. We highlight the next steps of an unconventional research program, which may allow top-down control of growth and form for numerous applications in regenerative medicine and synthetic bioengineering.

Can subcellular organization be explained only by physical principles?

In a recent forum article, Dan Needleman and Jan Brugues argue that, despite the astonishing advances in cell biology, a fundamental understanding of even the most well-studied subcellular biological processes is lacking.¹ This lack of understanding is evidenced by our inability to make precise predictions of subcellular and cellular behaviors. They suggest that to achieve such an understanding, we need to apply a combination of quantitative experiments with new theoretical concepts and determine the physical principles of subcellular biological organization.¹ We discuss these issues and suggest that, besides biophysics, we need strong theoretical inputs from biocommunication theory in order to understand all the core agents of the cellular life and subcellular organization.

The evolving block universe and the meshing together of times

It has been proposed that spacetime should be regarded as an evolving block universe, bounded to the future by the present time, which continually extends to the future. This future boundary is defined at each time by measuring proper time along Ricci eigenlines from the start of the universe. A key point, then, is that physical reality can be represented at many different scales: hence, the passage of time may be seen as different at different scales, with quantum gravity determining the evolution of spacetime itself at the Planck scale, but quantum field theory and classical physics determining the evolution of events within spacetime at larger scales. The fundamental issue then arises as to how the effective times at different scales mesh together, leading to the concepts of global and local times.

Nursing and the new biology: towards a realist, anti-reductionist approach to nursing knowledge

As a system of knowledge, nursing has utilized a range of subjects and reconstituted them to reflect the thinking and practice of health care. Often drawn to a holistic model, nursing finds it difficult to resist the reductionist tendencies in biological and medical thinking. In this paper I will propose a relational approach to knowledge that is able to address this issue. The paper argues that biology is not characterized by one stable theory but is often a contentious topic and employs philosophically diverse models in its scientific research. Biology need not be seen as a reductionist science, but reductionism is nonetheless an important current within biological thinking. These reductionist currents can undermine nursing knowledge in four main ways. Firstly, that the conclusions drawn from reductionism go far beyond their data based on an approach that prioritizes biological explanations and eliminates others. Secondly, that the methods employed by biologists are sometimes weak, and the limitations are insufficiently acknowledged. Thirdly, that the assumptions that drive the research agenda are problematic, and finally that uncritical application of these ideas can be potentially disastrous for nursing practice. These issues are explored through an examination of the problems reductionism poses for the issue of gender, mental health, and altruism. I then propose an approach based on critical realism that adopts an anti-reductionist philosophy that utilizes the conceptual tools of emergence and a relational ontology.

Advancing the application of omics-based biomarkers in environmental epidemiology

The use of omics represents a shift in approach for environmental epidemiology and exposure science. In this article, the aspects of the use of omics that will require further development in the near future are discussed, including (a) the underlying causal interpretation and models; (b) the "meet-in-the-middle" concept, with examples; (c) the role of "calibration" of measurements; and (d) the role of life-course epidemiology and the related development of adequate biostatistical models.

The algorithmic origins of life

Although it has been notoriously difficult to pin down precisely what is it that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and, in particular, that top-down (or downward) causation-where higher levels influence and constrain the dynamics of lower levels in organizational hierarchies-may be a major contributor to the hierarchal structure of living systems. Here, we propose that the emergence of life may correspond to a physical transition associated with a shift in the causal structure, where information gains direct and context-dependent causal efficacy over the matter in which it is instantiated. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.

Top-down causation: an integrating theme within and across the sciences?

This issue of the journal is focused on ‘top-down (downward) causation'. The words in this title, however, already raise or beg many questions. Causation can be of many kinds. They form our ways of ordering our scientific understanding of the world, all the way from the reductive concept of cause as elementary objects exerting forces on each other, through to the more holistic concept of attractors towards which whole systems move, and to adaptive selection taking place in the context of an ecosystem. As for ‘top’ and ‘down’, in the present scientific context, these are clearly metaphorical, as some of the articles in this issue of the journal make clear. Do we therefore know what we are talking about? The meeting at the Royal Society on which this set of papers is based included philosophers as well as scientists, and some of those (Jeremy Butterfield, Barry Loewer, Alan Love, Samir Okasha and Eric Scerri) have contributed articles to this issue. We would like also to thank those (Claus Kiefer, Peter Menzies, Jerome Feldman and David Papineau) who contributed only to the discussion meeting. Their contributions were also valuable, both at the meeting and by influencing the articles that have been written by others. We include a glossary with this introduction, composed by one of us (O'Connor). The clarification of the use of words and their semantic frames is an important role of philosophy, and this was evident in the discussions at the meeting and is now evident in many of the articles published here. Moreover, philosophical analysis is not limited to the papers by the professional philosophers. The idea of top-down causation is intimately related to concepts of emergence; indeed, it is a key factor in strong theories of emergence.

Laws, causation and dynamics at different levels

I have two main aims. The first is general, and more philosophical (§2). The second is specific, and more closely related to physics (§§3 and 4). The first aim is to state my general views about laws and causation at different 'levels'. The main task is to understand how the higher levels sustain notions of law and causation that 'ride free' of reductions to the lower level or levels. I endeavour to relate my views to those of other symposiasts. The second aim is to give a framework for describing dynamics at different levels, emphasizing how the various levels' dynamics can mesh or fail to mesh. This framework is essentially that of elementary dynamical systems theory. The main idea will be, for simplicity, to work with just two levels, dubbed 'micro' and 'macro', which are related by coarse-graining. I use this framework to describe, in part, the first four of Ellis' five types of top-down causation.