Michael Levin

The dominant paradigm in biomedicine focuses on the genetically-specified components of cells, and their biochemical dynamics. This perspective emphasizes bottom-up emergence of complexity, which constrains interventional approaches to micromanaging the living hardware. Here, I explore the implications for the applied life sciences of a complementary emerging field: diverse intelligence, which studies the capacity of a wide range of systems to reach specific goals in various problem spaces. Using tools from behavioral science and multiscale neuroscience, it is possible to address development, regenerative repair, and cancer as behaviors of a collective intelligence of cells as it navigates the space of possible morphologies and transcriptional and physiological states. This view emphasizes the competencies of living material – from the molecular to the organismal scales – that can be targeted by interventions. Top-down approaches take advantage of memories and homeodynamic goal-seeking behavior, offering the same massive advantages in biomedicine and bioengineering as the emphasis on reprogrammable hardware has had for information technologies. The bioelectric networks that bind individual cells toward large-scale anatomical goals are an especially tractable interface to organ-level plasticity. This suggests a research program to understand and tame the software of life by understanding the many examples of basal cognition that operate throughout living bodies. Tools are now in place to unify the organicist and mechanist perspectives on living systems toward a much-improved therapeutic landscape.

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Stefani A Crabtree, Colin D Wren, Avinash Dixit, Simon A Levin
PNAS Nexus, Volume 3, Issue 7, July 2024, pgae224,

In this paper, we examine how different governance types impact prosocial behaviors in a heterogenous society. We construct a general theoretical framework to examine a game-theoretic model to assess the ease of achieving a cooperative outcome. We then build a dynamic agent-based model to examine three distinct governance types in a heterogenous population: monitoring one’s neighbors, despotic leadership, and influencing one’s neighbors to adapt strategies that lead to better fitness. In our research, we find that while despotic leadership may lead towards high prosociality and high returns it does not exceed the effects of a local individual who can exert positive influence in the community. This may suggest that greater individual gains can be had by cooperating and that global hierarchical leadership may not be essential as long as influential individuals exert their influence for public good and not for public ill.

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Andy Clark

Entropy 2024, 26(8), 677

According to active inference, constantly running prediction engines in our brain play a large role in delivering all human experience. These predictions help deliver everything we see, hear, touch, and feel. In this paper, I pursue one apparent consequence of this increasingly well-supported view. Given the constant influence of hidden predictions on human experience, can we leverage the power of prediction in the service of human flourishing? Can we learn to hack our own predictive regimes in ways that better serve our needs and purposes? Asking this question rapidly reveals a landscape that is at once familiar and new. It is also challenging, suggesting important questions about scope and dangers while casting further doubt (as if any was needed) on old assumptions about a firm mind/body divide. I review a range of possible hacks, starting with the careful use of placebos, moving on to look at chronic pain and functional disorders, and ending with some speculations concerning the complex role of genetic influences on the predictive brain.

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Joanna K. Garner, Karen R. Harris

INTERNATIONAL JOURNAL OF COMPLEXITY IN EDUCATION Vol 5, No 1 (2024)

A primary goal of educational research is to improve understanding of the systems in
which students learn and educators teach. However, for much of the 20th and early 21st
century, researchers have relied upon models of individual, school, and district level change
that characterize educational processes and outcomes using linear, input-process-output
frameworks (Opfer & Pedder, 2011). These approaches conceal the complexity of
educational systems by using research designs and data collection methods that simplify
and decontextualize phenomena and the relations among them, and do not consider
influences across levels of the system (Kaplan & Garner, 2020). They have also not yielded
guidance for researchers and practitioners who work in contexts where multiple programs
and interventions overlap and interact with one another. In addition, our field faces
epistemological divisions that reflect varying emphases on context and the foregrounding
of different units of analysis. Some scholars advocate for large-scale, randomized control
trials as a “gold standard” for evaluating the implementation and outcome of interventions
(Lortie-Forgues & Inglis, 2019; Maxwell, 2004), while others value design-based, iterative
approaches aimed at addressing progressions of dilemmas of practice (Sandoval & Bell,
2004). Perhaps most importantly, many current approaches to research overlook the ways
in which teaching and learning are inherently interconnected with other sociocultural,
political, and historical phenomena such as economic development, mass migration, and
technological advances that manifest in individuals, families, and communities, and that
support or disrupt education (Harris, 2018).

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Jie Deng, Otto X. Cordero, Tadashi Fukami, Simon A. Levin, Robert M. Pringle, Ricard Solé, Serguei Saavedra

A long-standing question in biology is whether there are common principles that characterize the development of ecological systems (the appearance of a group of taxa), regardless of organismal diversity and environmental context. Classic ecological theory holds that these systems develop following a sequenced orderly process that generally proceeds from fast-growing to slow-growing taxa and depends on life-history trade-offs. However, it is also possible that this developmental order is simply the path with the least environmental resistance for survival of the component species and hence favored by probability alone. Here, we use theory and data to show that the order from fast-to slow-growing taxa is the most likely developmental path for diverse systems when local taxon interactions self-organize to minimize environmental resistance. First, we demonstrate theoretically that a sequenced development is more likely than a simultaneous one, at least until the number of iterations becomes so large as to be ecologically implausible. We then show that greater diversity of taxa and life histories improves the likelihood of a sequenced order from fast-to slow-growing taxa. Using data from bacterial and metazoan systems, we present empirical evidence that the developmental order of ecological systems moves along the paths of least environmental resistance. The capacity of simple principles to explain the trend in the developmental order of diverse ecological systems paves the way to an enhanced understanding of the collective features characterizing the diversity of life.

Read the full article at: www.biorxiv.org