Serguei Saavedra, José Ignacio Arroyo, Pablo A. Marquet, Chris P. Kempes

Metabolic scaling and coexistence theories have been pivotal in mathematizing both the energy expenditures and transformations across individuals and populations. Yet, it is unclear how the sustainability of biodiversity depends on metabolic scaling relationships. Here, we provide a tractable and scalable perspective to study how the possibilities for multispecies coexistence (i.e., feasibility) change as a function of the dependence of a population’s metabolism on body size. This integration predicts that, regardless of body size distribution among populations, there is a reciprocal relationship between the scaling exponents defining the dependence of competitive effects and expected carrying capacities on body size. In line with empirical observations, our integration predicts that expected carrying capacities lead to equilibrium biomasses that are independent from body size, and consequently, to equilibrium abundances that are inversely related to body size. We show that this even distribution of equilibrium biomasses increases the possibilities for multispecies coexistence under random environmental perturbations. Additionally, this integration establishes that the possibilities for multispecies coexistence is maximized when no species has an energetic competitive advantage. This integration opens new opportunities to increase our understanding of how metabolic scaling relationships at the individual and population levels shape processes at the community level.

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Jesse S. Bettinger, Karl J. Friston

Neuroscience & Biobehavioral Reviews

Since the late nineteen-nineties, the concept of homeostasis has been contextualized within a broader class of “allostatic” dynamics characterized by a wider-berth of causal factors including social, psychological and environmental entailments; the fundamental nature of integrated brain-body dynamics; plus the role of anticipatory, top-down constraints supplied by intrinsic regulatory models. Many of these evidentiary factors are integral in original descriptions of homeostasis; subsequently integrated; and/or cite more-general operating principles of self-organization. As a result, the concept of allostasis may be generalized to a larger category of variational systems in biology, engineering and physics in terms of advances in complex systems, statistical mechanics and dynamics involving heterogenous (hierarchical/heterarchical, modular) systems like brain-networks and the internal milieu. This paper offers a three-part treatment. 1) interpret “allostasis” to emphasize a variational and relational foundation of physiological stability; 2) adapt the role of allostasis as “stability through change” to include a “return to stability” and 3) reframe the model of homeostasis with a conceptual model of criticality that licenses the upgrade to variational dynamics.

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Pietro Traversa, Guilherme Ferraz de Arruda, Alexei Vazquez, Yamir Moreno

Entropy 2023, 25(11), 1537

Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

Read the full article at: www.mdpi.com

MELANIE MITCHELL

SCIENCE 10 Nov 2023 Vol 382, Issue 6671

Current AI systems seem to be lacking a crucial aspect of human intelligence: rich internal models of the world. A tenet of modern cognitive science is that humans are not simply conditioned-reflex machines; instead, we have inside our heads abstracted models of the physical and social worlds that reflect the causes of events rather than merely correlations among them. We rely on these mental models to simulate and predict the likely results of possible actions, to reason and plan in unfamiliar situations, to imagine counterfactuals (“what would have happened if I hadn’t stopped the car in time?”), and to update our knowledge and beliefs on the basis of experiences. Moreover, we have mental models not only of the external world and other people, but of ourselves, enabling us to assess and explain our reasoning and decision-making processes. How such models are implemented in our brains is much debated, but there is little doubt that they are foundational to our intelligence.

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Marcin Korecki, Damian Dailisan, Joshua Yang, Dirk Helbing

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allowed agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.

Read the full article at: papers.ssrn.com