Complexity Thoughts: Issue #63
Unraveling complexity: building knowledge, one paper at a time
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Foundations of network science and complex systems
Critical Demand in a Stochastic Model of Flows in Supply Networks
As a practical guideline, firms restructuring their supply chains should prioritize sourcing from nodes with broad but short supply networks rather than long, narrow ones
Supply networks are essential for modern production, yet their critical properties remain understudied. We present a stochastic model with random production capacities to analyze material flow to a root node, focusing on topology and buffer stocks. The critical demand, where unsatisfied demand diverges, is examined mostly through numerical simulations. Without stocks, minimal production dictates behavior, making topology irrelevant. With stocks, memory effects arise, making topology crucial. Increased local connectivity is beneficial: firms should favor broad, short supply chains over long, narrow ones.
Time-dependent influence metric for cascade dynamics on networks
An algorithm for efficiently calculating the expected size of single-seed cascade dynamics on networks is proposed and tested. The expected cascade size is a time-dependent quantity and so enables the identification of nodes that are the most influential early or late in the spreading process. The measure is accurate for both critical and subcritical dynamic regimes and so generalizes the nonbacktracking centrality that was previously shown to successfully identify the most influential single spreaders in a model of critical epidemics on networks.
Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to nontrivial steady states that generalize the notions of communities and structural balance in traditional networks.
Experimentally probing Landauer’s principle in the quantum many-body regime
Landauer’s principle bridges information theory and thermodynamics by linking the entropy change of a system during a process to the average energy dissipated to its environment. Although typically discussed in the context of erasing a single bit of information, Landauer’s principle can be generalized to characterize irreversibility in out-of-equilibrium processes, such as those involving complex quantum many-body systems. Specifically, the relation between the entropy change of a system and the energy dissipated to its environment can be decomposed into changes in quantum mutual information and a difference in the relative entropies of the environment. Here, we experimentally probe Landauer’s principle in the quantum many-body regime using a quantum field simulator of ultracold Bose gases. Employing a dynamical tomographic reconstruction scheme, we track the temporal evolution of the quantum field following a global mass quench from a massive to a massless Klein–Gordon model and analyse the thermodynamic and information-theoretic contributions to a generalized entropy production for various system–environment partitions of the composite system. Our results verify the quantum field theoretical calculations, interpreted using a semi-classical quasiparticle picture. Our work demonstrates the ability of ultracold atom-based quantum field simulators to experimentally investigate quantum thermodynamics.
[..] by extending beyond the case of bit erasure, recent influential work6 has generalized the link between information theory and thermodynamics. Using a quantum statistical mechanics framework, that work reinterprets Landauer’s principle as a means of relating the entropy change of a system to the energy dissipated to its environment in general out-of-equilibrium processes, not just erasure. This relation can be quantified by a measure of process irreversibility7. Such a broader formulation of Landauer’s principle not only deepens its physical importance but also makes this extension particularly relevant for quantum many-body systems, where contributions to irreversibility remain an area of active research, notably for phenomena such as equilibration and thermalization8,9,10,11.
Precision is not limited by the second law of thermodynamics
Measuring time is not that easy as one might expect.
As irreversible out-of-equilibrium systems, clocks come at a fundamental thermodynamic cost—entropy dissipation
Physical devices operating out of equilibrium are affected by thermal fluctuations, limiting their operational precision. This issue is particularly pronounced at microscopic and quantum scales, where its mitigation requires additional entropy dissipation. Understanding this constraint is important for both fundamental physics and technological design. Clocks, for example, need a thermodynamic flux towards equilibrium to measure time, resulting in a minimum entropy dissipation per clock tick. Although classical and quantum models often show a linear relationship between precision and dissipation, the ultimate bounds on this relationship remain unclear. Here we present an autonomous quantum many-body clock model that achieves clock precision that scales exponentially with entropy dissipation. This is enabled by coherent transport in a spin chain with tailored couplings, where dissipation is confined to a single link. The result demonstrates that coherent quantum dynamics can surpass the traditional thermodynamic precision limits, potentially guiding the development of future high-precision, low-dissipation quantum devices.
Ecosystems
Declining coral calcification to enhance twenty-first century ocean carbon uptake by gigatons
A bold proposal, I definitely need to read more about this. You can also check a coverage of the same study here.
[…] the result is a stark reminder that the climate and biodiversity crises overlap, but are not identical. Even if dead reefs would help a bit with climate change, losing them would destroy vital ecosystems that host about 25% of marine species. — Elise Cutts
As the oceans warm and acidify, the calcification of coral reefs declines, with net calcium carbonate dissolution projected even under moderate emissions scenarios. The impact of this on the global carbon cycle is however yet to be accounted for. We use a synthesis of the sensitivity of coral reef calcification to climate change, alongside reef distribution products to estimate alkalinity and dissolved inorganic carbon fluxes resulting from reductions in reef calcification. Using the global ocean biogeochemical model NEMO-PISCES, we simulate the impact of these fluxes on ocean carbon uptake under different emissions scenarios, accounting for uncertainty in present-day calcification rates.
Reductions in global coral reef carbonate production could enhance the ocean anthropogenic carbon sink by 0.34 PgC yr-1by mid-century (0.13 PgC yr-1 median estimate) with cumulative ocean carbon uptake up to 110 PgC greater by 2300 (46 PgC median estimate). Under medium to high emissions scenarios, two critical aspects emerge: (i) the full potential for coral reef degradation to affect carbon fluxes is reached within decades, and (ii) air-sea carbon fluxes remain substantial for centuries, due to the imbalance between carbon and alkalinity sinks/sources for the global ocean.
Accounting for the coral reef feedback into Earth system models could revise upward remaining carbon budget estimates, increasing the likelihood of achieving net-zero emissions without relying on negative emissions. The coral reef feedback could have a 21st-century impact comparable in magnitude to boreal forest dieback, though opposite in sign. This underscores a critical paradox: conserving calcifying organisms, such as coral reefs, may counteract a natural mechanism for mitigating climate change, but at the cost of protecting vital biodiversity. This challenges the "all-carbon" framework often used to address environmental issues, highlighting the complex trade-offs between carbon cycle regulation and biodiversity conservation.
Biological Systems
What is the current bottleneck in mapping molecular interaction networks?
Network biologists today have access to a rich assortment of interaction networks produced by assays such as affinity purification-mass spectrometry (AP-MS), yeast two-hybrid (Y2H) screening, co-fractionation mass spectrometry (CF-MS), or thermal proximity co-aggregation (TPCA), to name just a few. But high-throughput interaction data are notoriously noisy, such that similar experiments performed in different laboratories can produce very different networks. A long-standing challenge is distinguishing genuine interactions from experimental artifacts.
Cellular anatomy of arbuscular mycorrhizal fungi
Arbuscular mycorrhizal (AM) fungi are ancient plant mutualists that are ubiquitous across terrestrial ecosystems. These fungi are unique among most eukaryotes because they form multinucleate, open-pipe mycelial networks, where nutrients, organelles, and chemical signals move bidirectionally across a continuous cytoplasm. AM fungi play a crucial role in ecosystem functioning by supporting plant growth, mediating ecosystem diversity, and contributing to carbon cycling. It is estimated that plant communities allocate ∼3.93 Gt CO2e to AM fungi every year, much of which is stored as lipids inside the fungal network. Despite their ecological significance, the cellular biology of AM fungi remains underexplored. Here, we synthesise the current knowledge on AM fungal cellular structure and organisation. We examine AM fungal development at different biological levels — the hypha and its content, hyphal networks and AM fungal spores — and explore key cellular dynamics. This includes cell wall composition, cytoplasmic contents, nuclear and lipid organisation and dynamics, network architecture, and connectivity. We highlight how their unique cellular arrangement enables complex cytoplasmic flow and nutrient exchange processes across their open-pipe mycelial networks. We discuss how both established and novel techniques, including microscopy, culturing, and high-throughput image analysis, are helping to resolve previously unknown aspects of AM fungal biology. By comparing these insights with established knowledge in other, well-studied filamentous fungi, we identify critical knowledge gaps and propose questions for future research to further our understanding of fundamental AM fungal cell biology and its contributions to ecosystem health.
Earth and Global Systems
A foundation model for the Earth system
Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information.
Ecological scenarios: Embracing ecological uncertainty in an era of global change
Scenarios, or plausible characterizations of the future, can help natural resource stewards plan and act under uncertainty. Current methods for developing scenarios for climate change adaptation planning are often focused on exploring uncertainties in future climate, but new approaches are needed to better represent uncertainties in ecological responses. Scenarios that characterize how ecological changes may unfold in response to climate and describe divergent and surprising ecological outcomes can help natural resource stewards recognize signs of nascent ecological transformation and identify opportunities to intervene. Here, we offer principles and approaches for more fully integrating ecological uncertainties into the development of future scenarios. We provide examples of how specific qualitative and quantitative methods can be used to explore variation in ecological responses to a given climate future. We further highlight opportunities for ecological researchers to generate actionable projections that capture uncertainty in both climatic and ecological change in meaningful and manageable ways to support climate change adaptation decision making.