Complexity Thoughts: Issue #81
Unraveling complexity: building knowledge, one paper at a time
If you find value in #ComplexityThoughts, consider helping it grow by subscribing and sharing it with friends, colleagues or on social media. Your support makes a real difference.
→ Don’t miss the podcast version of this post: click on “Spotify/Apple Podcast” above!
Upcoming events
The 2026 Conference on Complex Systems will take place in Binghamton, New York (USA), from 9–16 October 2026.
This year’s theme, “Complex Systems for the Changing World”, highlights the role of interdisciplinary research in addressing global challenges. The program features an outstanding lineup of speakers across network science, nonlinear dynamics, and complex adaptive systems.
📅 Abstract submission deadline: May 1, 2026 – hurry up!
For those unable or hesitant (like me) to travel to the U.S. in this period, the conference will be fully hybrid, ensuring broad accessibility.
Ecosystems and evolution
A high-throughput laboratory experiment tracking the assembly of soil-derived communities shows that species-rich bacterial necromass supports increasingly diverse communities, with each additional dead species expanding opportunities for niche partitioning.
This is the referenced paper:
Understanding how high species diversity is maintained in natural bacterial communities is a central question in microbial ecology. Due to the versatile heterotrophic capacities of bacteria and the rich nutrients released by deceased bacterial cells, necromass recycling plays an important role in sustaining bacterial growth. Such nutrient cycling within communities can provide additional resource niches for bacteria, but its potential effects on bacterial diversity maintenance have been neglected. Here we conducted two independent experiments and studied the assembly of 276 soil-derived bacterial communities sustained by a wide range of bacterial necromass combinations, from single-species necromass to combinations of up to nearly 1,000 species. Our results highlight the existence of a species-rich bacterial necrobiome in soil. We found that the composition of necromass-decomposing communities was determined by the various organic compounds in the different necromass combinations, and the increases in necromass-producing species constantly promoted species diversity of necromass-decomposing communities. Moreover, the average niche breadth and overlap of coexisting necromass-decomposing species in utilizing distinct single-species necromass decreased with increases in necromass diversity, supporting the hypothesis of resource partitioning in utilizing different single-species necromass. Our study provides insights into diversity maintenance in bacterial communities from a perspective of internal nutrient cycling.
Functional motifs in food webs and networks
When studying a complex system, it is often useful to think of the system as a network of interacting units. One can then ask if some properties of the entire network are already explained by a small part of the network, a network motif. A famous example of an ecological motif is exploitative competition in food webs, where the presence of two species competing for a shared resource precludes the existence of a stable equilibrium for the whole system. However, other examples of motifs with such direct impacts on stability are not known. Here, we show why small motifs that allow conclusions on systemic stability are rare. More importantly, we show that another dynamical property, reactivity, is typically rooted in motifs. Computing the reactivity of motifs can reveal which parts of a network are prone to respond violently to perturbations. This highlights motif reactivity as a useful property to measure in real-world systems to understand likely modes of systemic failure in food webs or other networks in epidemics, supply chains, or power grids.
Trade-offs between quantity and quality are common in the organization and evolution of biological, technological, and economic systems. In social insects, shifts from solitary organisms to complex societies bring this dilemma to the colony scale: producing fewer robust units or many cheaper ones. We investigate how cuticle investment, a major nutritional cost, shaped the evolution of ant societies and diversification. Using a computer vision approach on three-dimensional x-ray microtomography scans of 880 specimens from 507 species, we show that larger colonies were facilitated by reducing exoskeleton investment rather than miniaturizing workers. Reduced cuticle investment was associated with accelerated diversification rates in ants, whereas other candidates—colony size and worker size—did not correlate with diversification. Diet and climate had measurable but secondary effects on cuticle investment. Our results support a hypothesis whereby evolving cheaper but more numerous units through reduced investment in structural tissues was a strategic trend in the evolution and diversification of complex insect societies.
I like this one, and I am wondering how it reconnects to my recent work on decoding the architecture of living systems via a multiscale/multilayer/eco-evolutionary model.
Stay tuned, since I will surely go back to this paper in my upcoming series of posts on living systems.
A complete explanation of evolutionary change requires reconciling processes that operate across multiple levels of spatial/temporal organization. Development, the processes by which traits are generated, unfolds over an individual’s lifetime; heredity, encompassing the diverse forms of information transmission, occurs across generations; and population and ecological change often take place across longer horizons. Capturing these layered dynamics in a formal mathematical framework is essential for advancing evolutionary theory in line with recent conceptual developments, such as extended evolutionary theory (EET) or the extended evolutionary synthesis (EES). In this work, we introduce the extended life cycle (ELC), a multilevel–multiscale mathematical modelling framework that centres the life cycle as the fundamental unit of evolutionary analysis. In contrast to traditional gene-centric approaches, the ELC formalism captures the multilevel causal architecture of biological systems by modelling development, heredity, population and ecological dynamics across distinct, but interacting ontological levels. Each level is expressed as a stochastic state-space model, with multiple scales within each level. We demonstrate the utility of the ELC through a Bayesian learning application to multiscale habitat selection and show how our framework can predict latent trajectories generated from complex multilayered dynamics.
The origin and evolution of cell types
Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the ‘core regulatory complex’ (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.
Biological Systems
A smartphone analogy to explore the origin of animals
How animals evolved from their unicellular ancestor is a fundamental biological question. The fact that all animals are monophyletic—sharing a single common ancestor—implies their origin from unicellular eukaryotes was likely driven by rare and highly advantageous innovations. While the fossil record and initial genomic comparisons suggested animals originated by the rapid acquisition of many novel genes, new research on animal’s closest unicellular relatives reveals most of those genes originated before animals evolved. Here we present a new model for animal origins, which shares similarities with the origin of one of the greatest technological innovations of our time: the smartphone. We show that the origin of both animals and smartphones was due to the integration and repurposing of pre-existing components driven by a novel “operating system”, rather than the sudden emergence of many new parts. This model offers testable predictions and a new theoretical framework for understanding complex biological innovation.
This work illustrates how a complex interplay between fungi and bacteria can result in detrimental consequences for the host.
A pathobiont is a member of the normal microbiota (often gut, oral, or skin) that is usually harmless or even beneficial, but can cause disease when conditions change (eg, if the host immune system is weakened, the microbial community is disrupted, or the organism gains access to normally sterile sites). Thus it’s not an “external pathogen” you acquire: it’s typically an endogenous resident with disease-causing potential under specific contexts.
The fungus Candida albicans is a pathobiont colonizing mucosal surfaces of healthy individuals. The interaction with bacteria is generally considered to be antagonistic, with bacteria preventing fungal infection by mediating colonization resistance. In contrast, our study shows that interaction with the bacterium Enterococcus faecalis can result in more severe infections. We identified a bacterial toxin mediating synergistic damage, which explains why synergism was observed for some but not all strains of E. faecalis. Our findings might have clinical implications as C. albicans and E. faecalis occur in the same mucosal niches, benefit from antibiotic treatment, and are coisolated in clinical samples.
Some infections may become hard to treat even without classic, gene-based antibiotic resistance. This paper shows that plasmids (mobile DNA that spreads between bacteria) can induce pili (ie, hair-like protein filaments that extend from the surface of many bacteria) that physically link cells into tight clusters, effectively creating a protective community in which antibiotics (and other treatments) are less effective. This matters because it reframes treatment failure as sometimes being driven by how bacteria are organized in space, not just which resistance genes they carry, meaning standard susceptibility readouts can be overly optimistic. We need to study and target the mechanics of plasmid transfer and biofilm-like structure (breaking up clustering or blocking pili) alongside developing new antibiotics.
Bacterial conjugation enables the horizontal transfer of plasmids that often carry genes influencing host physiology and behavior. In spatially structured biofilms, where many bacteria live in close proximity, conjugation can significantly alter both genetic and physical community composition. Here, we use a microfluidic system and fluorescence microscopy to track the transmission of the F-like plasmid pED208 within Escherichia coli biofilms, differentiating invading plasmid donors, transconjugants, and plasmid-free cells at high resolution. We find that conjugation within established resident biofilms is efficient until cell density reaches a threshold associated with high extracellular matrix secretion. Strikingly, plasmid-encoded conjugative pili also enable matrix-deficient cells to aggregate into dense biofilms, promoting the formation of multi-strain and multispecies cell clusters. This restoration of biofilm architecture increases antibiotic and phage tolerance but comes at the cost of altering dispersal dynamics: plasmid-bearing cells disperse less readily than plasmid-free cells, creating a trade-off between local advantage and distal spread. Our findings indicate that conjugative pilus-mediated adhesion incurs a fitness trade-off, compacting biofilm structure and thereby conferring enhanced antibiotic and phage tolerance while reducing the spread of plasmid carriers over larger spatial scales.
Human behavior and cognitive science
Classical anthropological and cognitive theories propose that supernatural healing practices emerge when ordinary causal reasoning fails, yet direct quantitative tests remain scarce. Using 3,655 “local cures,” collected as part of a national project to document folklore in Ireland in 1937–1938, we quantitatively test a range of theories about the appeal of supernatural cures. Preregistered mixed-effects models reveal that diseases whose causes or bodily mechanisms would have eluded lay observers were around 50% more likely to attract religious or magical treatments, whereas disease severity, pain, anxiety, and need for care showed no reliable relationship with supernatural or religious cure content. These findings suggest epistemic uncertainty may be a driver of supernatural thinking about health.
Why and when do people draw upon religious and supernatural solutions to problems? Cognitive scientists and anthropologists have proposed a range of answers, stressing religion and ritual’s capacity to alleviate anxiety, create a sense of order, or explain otherwise inexplicable events. Here, we leverage a unique dataset of 3,655 folk cures for 35 diseases, collected in 1937/8 from a mostly rural Irish sample born roughly between 1850 and 1925. Since the diseases vary in theory-relevant ways and the cures vary in the degree to which they include religious and supernatural elements, this dataset facilitates a unique test of these predictions in a premodern western population. In preregistered tests, we find that diseases judged by two doctors to have causes and mechanisms that would be unclear to the patients were more likely to have supernatural/religious treatments. Contra common predictions, severe and disabling diseases did not have more supernatural/religious cures and anxiety-provoking diseases did not have more ritualistic cures.
Collective intelligence as collective information processing
Collective intelligence (CI) inquiry lacks a multidisciplinary, unifying framework. We propose that Collective Information Processing (CIP) underlies CI. CIP distinguishes two forms of individual- and group-level processing. CIP enables identification and classification of phenomena labeled as CI. CIP advances debates about agency and causation within CI studies.
Collective intelligence research spans multiple disciplines and focuses on a broad range of collective behaviors, including group problem-solving, flocking in social animals, and the formation of social knowledge. It is not apparent what these different forms of collective intelligence have in common, apart from being instances of collective behavior. In this paper, we develop a framework that enables us to better classify different forms of collectively intelligent behavior in relation to one another based on the information processing mechanisms involved. We argue that these behaviors share a common foundation, which we call collective information processing, or CIP. CIP involves two key mechanisms: (1) individual processing of group information and (2) group processing, or group-level sensitivity to the arrangement of individual information. We operationalize the CIP framework to analyze different forms of collective intelligence, both classifying them in relation to one another and in alignment with generalized quantifiable measures of information processing. Our account of collective intelligence as CIP offers a novel framework for identifying and classifying forms of collective intelligence across a wide range of disciplinary contexts. This framework is meant to unify and subsume, rather than simply challenge, existing attempts to define collective intelligence.
→ Please, remind that if you find value in #ComplexityThoughts, you might consider helping it grow by subscribing, or by sharing it with friends, colleagues or on social media. See also this post to learn more about this space.