Complexity Thoughts: Issue #34
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
Foundations of network science and complex systems
Collective behavior from surprise minimization
A classical model to simulate and analyze the collective behavior of self-propelled particles (such as the fascinating flocks of birds or schools of fish) is due to Vicsek and collaborators in 1995, and had deep implications for our understanding of complex systems and emergent behavior. Roughly speaking, the model assumes that each particle is self-propelled and moves at a constant speed, with a direction that is influenced by the average direction of its neighbors within a certain radius. In practice, the particles use a form of local interaction for decision-making. Interestingly, a fluid network emerges and can be used to describe the collective behavior changing over time: I have prepared a simple animation where this network is drawn to facilitate the understanding of the underlying (local) interactions.
The Vicsek model is often used to study the phase transition from disordered motion to ordered motion as the density of particles or the noise level changes, demonstrating that even minimal rules and interactions can lead to complex, coordinated group behavior, making it a foundational model in the study of collective dynamics.
Since 1995, other models have been proposed (see here and here, for instance). In a new paper, Heins et al propose to go beyond the self-propelled-particles paradigm by considering active inferential agents.
The results are mesmerizing, and the paper is accompanied by several videos showing the collective behavior of particles within the new framework.
I hope to have time to write a dedicated short essay about this in the next future.
We introduce a model of collective behavior, proposing that individual members within a group, such as a school of fish or a flock of birds, act to minimize surprise. This active inference approach naturally generates well-known collective phenomena such as cohesion and directed movement without explicit behavioral rules. Our model reveals intricate relationships between individual beliefs and group properties, demonstrating that beliefs about uncertainty can shape collective decision-making accuracy. As agents update their generative model in real time, groups become more sensitive to external perturbations and more robust in encoding information. Our work provides fresh insights into understanding collective dynamics and could inspire strategies in the study of animal behavior, swarm robotics, and distributed systems.
Origin of Life and Evolution
To unravel the origin of life, treat findings as pieces of a bigger puzzle
Explaining isolated steps on the road from simple chemicals to complex living organisms is not enough
Bioenergetic costs and the evolution of noise regulation by microRNAs
MicroRNAs are short strands of genetic material that regulate cellular functions, including reducing noise in protein numbers. We argue that this regulation incurs a steep energetic price so that natural selection drives such systems toward greater energy efficiency. This involves tuning the interaction strength between microRNAs and their target messenger RNAs, which is controlled by the length of a microRNA seed region that pairs with a complementary region on the target. We show that 6 to 7 nucleotide seed lengths are optimal, which may help explain why seeds of this size are prevalent in animal microRNAs. Moreover, the behavior of the optimal microRNA network mimics the best possible linear noise filter, a classic concept in engineered communications systems.
Ecosystems
A taxonomy of multiple stable states in complex ecological communities
Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.
Structured community transitions explain the switching capacity of microbial systems
See also this nice Commentary for the paper:
Explaining and predicting qualitative changes in the taxon membership (community) of microbial systems is synonymous for understanding qualitative changes in the environment. Yet, it remains unclear the extent to which the capacity of going back and forth among few different communities is partially conditioned by the current taxon membership or is fully an outcome of random environmental changes. Here, we introduce a conceptual, mathematical, and practical framework to demonstrate that the relatively high switching capacity of microbial systems can be explained by structured community transitions that increase the dependency on the current community. We corroborate our theory using temporal data of human and ocean microbiota, opening the opportunity to enhance our understanding of a wide array of microbial systems.
Evidence of scale-free clusters of vegetation in tropical rainforests
I can’t summarize this paper and its background better than this great piece by Philip Ball, so I just refer to it (thanks Phil!)
Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's “green lungs.”
Neuroscience
Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
Understanding properties and connectivity of neurons in the human brain cortex is critical for understanding the mechanisms supporting cognitive functions. Most of our current knowledge of the cortical synaptic structure is derived from rodent studies. Peng et al. analyzed multineuron patch-clamp recordings obtained from human cortical brain slices to determine the principles governing synaptic connectivity and compared the results with rodent cortical activity. Unlike in rodents, connectivity in the human cortex is heterogeneous and exhibits a directed and mostly acyclic graph topology. Using these principles in neural network models increased dimensionality and improved performance in functionally relevant tasks, suggesting that these properties of cortical connectivity in humans facilitate high-order computation.
Climate change
The economic commitment of climate change
Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons1,2,3,4,5,6. Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes7,8. Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.
Emergent constraints on carbon budgets as a function of global warming
Earth System Models (ESMs) continue to diagnose a wide range of carbon budgets for each level of global warming. Here, we present emergent constraints on the carbon budget as a function of global warming, which combine the available ESM historical simulations and future projections for a range of scenarios, with observational estimates of global warming and anthropogenic CO2 emissions to the present day. We estimate mean and likely ranges for cumulative carbon budgets for the Paris targets of 1.5 °C and 2 °C of global warming of 812 [691, 933] PgC and 1048 [881, 1216] PgC, which are more than 10% larger than the ensemble mean values from the CMIP6 models. The linearity between cumulative emissions and global warming is found to be maintained at least until 4 °C, and is consistent with an effective Transient Climate Response to Emissions (eTCRE) of 2.1 [1.8, 2.6] °C/1000PgC, from a global warming of 1.2 °C onwards.