Complexity Digest

Brennan Klein, Harrison Hartle, Munik Shrestha, Ana Cecilia Zenteno, David Barros Sierra Cordera, José R. Nicolas-Carlock, Ana I. Bento, Benjamin M. Althouse, Bernardo Gutierrez, Marina Escalera-Zamudio, Arturo Reyes-Sandoval, Oliver G. Pybus, Alessandro Vespignani, Jose Alberto Diaz-Quiñonez, Samuel V. Scarpino, Moritz U.G. Kraemer

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases, deaths and hospitalizations at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March – June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide actionable and dynamic insights into how to use network science and epidemiological modelling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

Read the full article at: arxiv.org

João L. Cordovil, Gil C. Santos & John Symons 

Foundations of Science volume 28, pages1–20 (2023)

While ontic structural realism (OSR) has been a central topic in contemporary philosophy of science, the relation between OSR and the concept of emergence has received little attention. We will argue that OSR is fully compatible with emergentism. The denial of ontological emergence requires additional assumptions that, strictly speaking, go beyond OSR. We call these physicalist closure assumptions. We will explain these assumptions and show that they are independent of the central commitments of OSR and inconsistent with its core goals. Recognizing the compatibility of OSR and ontological emergence may contribute to the solution of ontological puzzles in physics while offering new ways to achieve the goals that advocates of OSR set for their view.

Read the full article at: link.springer.com

The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong.

Read the full article at: www.quantamagazine.org

Lukas Ambühl, Monica Menendez & Marta C. González
Communications Physics volume 6, Article number: 26 (2023)

The science of cities aims to model urban phenomena as aggregate properties that are functions of a system’s variables. Following this line of research, this study seeks to combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches, we find that the maximum number of congested clusters and the maximum MFD flow occur at the same moment, precluding network percolation (i.e. traffic collapse). These insights describe the transition of the average network flow from the uncongested phase to the congested phase in parallel with the percolation transition from sporadic congested links to a large, congested cluster of links. These results can help to better understand network resilience and the mechanisms behind the propagation of traffic congestion and the resulting traffic collapse.

Read the full article at: www.nature.com

One of the things that make complexity science so fascinating is the diversity of the systems that it applies to. In this series so far, you’ve learnt about everything from ecologies to economies, tipping points in ecologies and economies, to power and influence in the 1400s, and even the spread of coronavirus in the lungs and the thing that brings all of these different topics together is complexity. This means that we can study one system to help us understand other systems — including bees.

In today’s episode, Orit Peleg, Faculty at the University of Colorado, Boulder, and External Faculty at the Santa Fe Institute, explains how bees self-organise and produce sophisticated behaviour. In this case, you’ll hear how thousands of bees can work out where their queen is at any given point.

Listen at: omny.fm

Jan Korbel, Simon D. Lindner, Tuan Minh Pham, Rudolf Hanel, and Stefan Thurner
Phys. Rev. Lett. 130, 057401

Homophily, the tendency of humans to attract each other when sharing similar features, traits, or opinions, has been identified as one of the main driving forces behind the formation of structured societies. Here we ask to what extent homophily can explain the formation of social groups, particularly their size distribution. We propose a spin-glass-inspired framework of self-assembly, where opinions are represented as multidimensional spins that dynamically self-assemble into groups; individuals within a group tend to share similar opinions (intragroup homophily), and opinions between individuals belonging to different groups tend to be different (intergroup heterophily). We compute the associated nontrivial phase diagram by solving a self-consistency equation for “magnetization” (combined average opinion). Below a critical temperature, there exist two stable phases: one ordered with nonzero magnetization and large clusters, the other disordered with zero magnetization and no clusters. The system exhibits a first-order transition to the disordered phase. We analytically derive the group-size distribution that successfully matches empirical group-size distributions from online communities.

Read the full article at: link.aps.org

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

Read the full article at: www.quantamagazine.org

FBK-CHuB is seeking a Researcher in the field of the classification, analysis and modelling of online disinformation spreading behaviour.
In particular, the candidate will be involved in a large European research project focused on the development of a platform tackling misinformation and disinformation across the EU by empowering scientific researchers and media practitioners with advanced AI-based technologies that: 1) allow multichannel (distinct online social media and news feeds), multilingual and multimodal (textual, visual and audio content) monitoring, detection and recording of misinformation and disinformation on online social media and traditional media; 2) estimate the risk of unreliable information consumption; 3) create a trustworthy online environment involving researchers, media practitioners and policy makers to facilitate the creation and distribution of reliable information and counter-narratives, while labelling and countering mis/disinformation.

Read the full article at: jobs.fbk.eu

A sustainable future requires preservation of the world’s finite resources, which often means the waste from one process loops back and becomes the input for another. Advanced technologies and techniques are helping an array of industries to make reuse and recycling more central to their operations. 

Read the full Outlook at: www.nature.com

Sonia Kéfi, Camille Saade, Eric L. Berlow, Juliano S. Cabral and Emanuel A. Fronhofer

Philosophical Transactions of the Royal Society B-Biological Sciences; Vol.: 377; Issue: 1857; Article No.: 20210386

Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex—species rich and spatially structured—systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems.

Read the full article at: royalsocietypublishing.org

Kishore Vasan, Deisy Gysi, Albert-Laszlo Barabasi
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome.

Read the full article at: arxiv.org

Alice Patania, Antoine Allard, Jean-Gabriel Young
We study the problem of clustering networks whose nodes have imputed or physical positions in a single dimension, such as prestige hierarchies or the similarity dimension of hyperbolic embeddings. Existing algorithms, such as the critical gap method and other greedy strategies, only offer approximate solutions. Here, we introduce a dynamic programming approach that returns provably optimal solutions in polynomial time — O(n^2) steps — for a broad class of clustering objectives. We demonstrate the algorithm through applications to synthetic and empirical networks, and show that it outperforms existing heuristics by a significant margin, with a similar execution time.

Read the full article at: arxiv.org

Chris Kang, Madelynn McElroy, and Nikolaos K. Voulgarakis

Entropy 2023, 25(2), 235

Early embryonic development involves forming all specialized cells from a fluid-like mass of identical stem cells. The differentiation process consists of a series of symmetry-breaking events, starting from a high-symmetry state (stem cells) to a low-symmetry state (specialized cells). This scenario closely resembles phase transitions in statistical mechanics. To theoretically study this hypothesis, we model embryonic stem cell (ESC) populations through a coupled Boolean network (BN) model. The interaction is applied using a multilayer Ising model that considers paracrine and autocrine signaling, along with external interventions. It is demonstrated that cell-to-cell variability can be interpreted as a mixture of steady-state probability distributions. Simulations have revealed that such models can undergo a series of first- and second-order phase transitions as a function of the system parameters that describe gene expression noise and interaction strengths. These phase transitions result in spontaneous symmetry-breaking events that generate new types of cells characterized by various steady-state distributions. Coupled BNs have also been shown to self-organize in states that allow spontaneous cell differentiation.

Read the full article at: www.mdpi.com

Scientists and philosophers have been attempting to define life for ages. In biology class we were taught to define life through the set of features that we, and every other species on the planet share. Things like movement, respiration, growth, and reproduction. Life is made of cells and has DNA. But does biochemistry constitute the whole picture? As far back as 1970, Carl Sagan didn’t think so. Attempts at defining life, he and many others thought, were too constrained by the characteristics of life as we know it. A single example of extraterrestrial life could change everything.

Read the full article at: nautil.us

Theodor Cimpeanu, Francisco C Santos, The Anh Han
Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is imperative to optimise spending without sacrificing positive outcomes, a challenge which has often been approached within several areas of social, life and engineering sciences. These studies often neglect the availability of information, cost restraints, or the underlying complex network structures, which define real-world populations. Here, we have extended these models, including the aforementioned concerns, but also tested the robustness of their findings to stochastic social learning paradigms. Akin to real-world decisions on how best to distribute endowments, we study several incentive schemes, which consider information about the overall population, local neighbourhoods, or the level of influence which a cooperative node has in the network, selectively rewarding cooperative behaviour if certain criteria are met. Following a transition towards a more realistic network setting and stochastic behavioural update rule, we found that carelessly promoting cooperators can often lead to their downfall in socially diverse settings. These emergent cyclic patterns not only damage cooperation, but also decimate the budgets of external investors. Our findings highlight the complexity of designing effective and cogent investment policies in socially diverse populations.

Read the full article at: arxiv.org

The Nobel Laureate Giorgio Parisi is interviewed by JPhys Complexity Editor-in-Chief, Ginestra Bianconi, on themes related to the 2021 Nobel Prize in Physics awarded to him for research on complex systems.

Read the full article at: iopscience.iop.org

Ginestra Bianconi et al 2023 J. Phys. Complex. 4 010201

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.

Read the full article at: iopscience.iop.org

Thomas Schmickl

Complexity Volume 2022 | Article ID 9956885
Predictions of emergent phenomena, appearing on the macroscopic layer of a complex system, can fail if they are made by a microscopic model. This study demonstrates and analyses this claim on a well-known complex system, Conway’s Game of Life. Straightforward macroscopic mean-field models are easily capable of predicting such emergent properties after they have been fitted to simulation data in an after-the-fact way. Thus, these predictions are macro-to-macro only. However, a micro-to-macro model significantly fails to predict correctly, as does the obvious mesoscopic modeling approach. This suggests that some macroscopic system properties in a complex dynamic system should be interpreted as examples of phenomena (properties) arising from “strong emergence,” due to the lack of ability to build a consistent micro-to-macro model, that could explain these phenomena in a before-the-fact way. The root cause for this inability to predict this in a micro-to-macro way is identified as the pattern formation process, a phenomenon that is usually classified as being of “weak emergence.” Ultimately, this suggests that it may be in principle impossible to discriminate between such distinct categories of “weak” and “strong” emergence, as phenomena of both types can be part of the very same feedback loop that mainly governs the system’s dynamics.

Read the full article at: www.hindawi.com

Ric Sims  and Özlem Yilmaz

Adaptive Behavior

The tricky question in the plant cognition debate is what theory of cognition should be used to fix the reference of cognitive concepts without skewing the debate too much one way or the other. After all, plants are rather different to animals in many respects: they are not motile, do not possess central nervous systems or even neurons, do not exhibit an invariant morphology, interact with the world in a distributed multi-centred manner, and behave through changes in their physiology. Nonetheless, there is a significant strand in the debate that asserts that plants are indeed cognitive. But what theory of cognition makes sense of this claim without baking in prior zoological assumptions? The aim of this paper is to try out a theory of minimal cognition that makes the claim of plant cognition plausible. It is primarily inspired by the distributed cognition literature and the sensorimotor coordination theory of cognition proposed by van Duijn et al. (2006). We take a cognitive system to be a coordinated set of semi-autonomous processes running over the organism and items in its environment. Coordination is characterised in terms of two functional conditions that ensure that the system generates goal-directed action in the world. The system is stigmergic in the sense that the material results of its actions in the environment are a crucial part of the processes that coordinate further actions. The account possesses a degree of scale invariance and helps unify cognitive explanation across microorganisms, plants and animals.

Read the full article at: journals.sagepub.com

Arsham Ghavasieh, Manlio De Domenico
Complex network states are characterized by the interplay between system’s structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.

Read the full article at: arxiv.org