Michael Levin and Richard Watson

Cell and developmental biology offer numerous remarkable examples of collective intelligence and adaptive plasticity to novel circumstances, as cells implement large-scale form and function. Many of these capabilities seem different from the behavior of machines or the results of computations. And yet, they are implemented by biochemical, biophysical, and bioelectrical events which are often interpreted with the machine metaphor that dominates molecular and cell biology. The seeming incongruity between molecular mechanisms and the emergence of self-constructing and goal-driven intentional living agents has driven a perennial debate between mechanist and organicist thinkers. Here, we discuss the inadequacies of, on the one hand, the (unminded) mechanist and computationalist frameworks, and on the other, dualistic conceptions of machine vs. mind. Both fail to provide an integration of agential and mechanistic aspects evident in biology. We propose that a new kind of cognitivism, cognition all the way down, provides the necessary unification of ‘bottom-up’ and ‘top-down’ causal flows evident in living systems. We illustrate how the organizational layers between genotype and phenotype provide problem-solving intelligence, not merely complexity, and discuss the benefits and inadequacies of specific machine metaphors in this context. By taking a pragmatist approach to the hypothesis that life and mind are fundamentally the same problem, formalisms are emerging that embrace the unique quality of the agential material of life while fully benefitting from the advances of modern machine science. New ways to map formal concepts of machine and data to biology provide a route toward unifying evolutionary and developmental biology, and rich substrates for the use of truly bio-inspired principles to advance engineering and computer science.

Read the full article at: osf.io

ARIEL FLINT ASHERY, LUCA MARIA AIELLO, AND ANDREA BARONCHELLI

SCIENCE ADVANCES 14 May 2025 Vol 11, Issue 20

Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong collective biases can emerge during this process, even when agents exhibit no bias individually. Last, we examine how committed minority groups of adversarial LLM agents can drive social change by imposing alternative social conventions on the larger population. Our results show that AI systems can autonomously develop social conventions without explicit programming and have implications for designing AI systems that align, and remain aligned, with human values and societal goals.

Read the full article at: www.science.org

Ahmed Allibhoy, Arthur N. Montanari, Fabio Pasqualetti, Adilson E. Motter

Oscillator Ising machines (OIMs) are networks of coupled oscillators that seek the minimum energy state of an Ising model. Since many NP-hard problems are equivalent to the minimization of an Ising Hamiltonian, OIMs have emerged as a promising computing paradigm for solving complex optimization problems that are intractable on existing digital computers. However, their performance is sensitive to the choice of tunable parameters, and convergence guarantees are mostly lacking. Here, we show that lower energy states are more likely to be stable, and that convergence to the global minimizer is often improved by introducing random heterogeneities in the regularization parameters. Our analysis relates the stability properties of Ising configurations to the spectral properties of a signed graph Laplacian. By examining the spectra of random ensembles of these graphs, we show that the probability of an equilibrium being asymptotically stable depends inversely on the value of the Ising Hamiltonian, biasing the system toward low-energy states. Our numerical results confirm our findings and demonstrate that heterogeneously designed OIMs efficiently converge to globally optimal solutions with high probability.

Read the full article at: arxiv.org

Lidia Maria de Oliveira Morais, et al.

Front. Public Health, 18 May 2025
Volume 13 – 2025

Introduction: Cities in the Global South face escalating climate change challenges, including extreme weather events that disproportionately affect marginalized populations and exacerbate health risks, such as non-communicable diseases (NCDs). Climate resilience, defined as the capacity to adapt and recover from climate-related events, requires intersectoral collaboration between governments and civil society.

Methods: This study employs a Community-based System Dynamics approach, leveraging shared learning across four cities—Belo Horizonte (BH, Brazil), Yaoundé (Cameroon), Kingston (Jamaica), and Kisumu (Kenya)—through the Global Diet and Activity Research Network (GDAR). An implementation of the method in BH is detailed, examining drivers and interdependencies shaping community-based climate resilience strategies against heavy rainfalls.

Results: In BH, findings highlight the interplay between urbanization risks, vulnerabilities, heavy rainfall, and NCDs, with visibility, resources, education, and training identified as critical intervention points.

Conclusion: This study underscores the importance of aligning community action with public policy and highlights opportunities for collective learning and resilience-building for climate change in Global South cities.

Read the full article at: www.frontiersin.org

Matthew Russell Barnes, Vincenzo Nicosia & Richard G. Clegg 

Scientific Reports volume 15, Article number: 5941 (2025)

In complex networks, the “rich-get-richer” effect (nodes with high degree at one point in time gain more degree in their future) is commonly observed. In practice this is often studied on a static network snapshot, for example, a preferential attachment model assumed to explain the more highly connected nodes or a rich-club effect that analyses the most highly connected nodes. In this paper, we consider temporal measures of how success (measured here as node degree) propagates across time. By analogy with social mobility (a measure of people moving within a social hierarchy through their life) we define hierarchical mobility to measure how a node’s propensity to gain degree changes over time. We introduce an associated taxonomy of temporal correlation statistics including mobility, philanthropy and community. Mobility measures the extent to which a node’s degree gain in one time period predicts its degree gain in the next. Philanthropy and community measure similar properties related to node neighbourhood. We apply these statistics both to artificial models and to 26 real temporal networks. We find that most of our networks show a tendency for individual nodes and their neighbourhoods to remain in similar hierarchical positions over time, while most networks show low correlative effects between individuals and their neighbourhoods. Moreover, we show that the mobility taxonomy can discriminate between networks from different fields. We also generate artificial network models to gain intuition about the behaviour and expected range of the statistics. The artificial models show that the opposite of the “rich-get-richer” effect requires the existence of inequality of degree in a network. Overall, we show that measuring the hierarchical mobility of a temporal network is an invaluable resource for discovering its underlying structural dynamics.

Read the full article at: www.nature.com