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What Is Morphological Computation? On How the Body Contributes to Cognition and Control

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “offloading computation from the brain to the body,” where the body is said to perform “morphological computation.” Our investigation of four characteristic cases of morphological computation in animals and robots shows that the “offloading” perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control—how it contributes to the overall orchestration of intelligent behavior.

 

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

Vincent C. Müller, Matej Hoffmann

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 1-24
Posted Online February 27, 2017.
(doi:10.1162/ARTL_a_00219)

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Evolutionary Robotics: Taking a biologically inspired approach to the design of autonomous, adaptive machines.

Josh C. Bongard

Communications of the ACM

The automated design, construction, and deployment of autonomous and adaptive machines is an open problem. Industrial robots are an example of autonomous yet nonadaptive machines: they execute the same sequence of actions repeatedly. Conversely, unmanned drones are an example of adaptive yet non-autonomous machines: they exhibit the adaptive capabilities of their remote human operators. To date, the only force known to be capable of producing fully autonomous as well as adaptive machines is biological evolution. In the field of evolutionary robotics,9 one class of population-based metaheuristics—evolutionary algorithms—are used to optimize some or all aspects of an autonomous robot. The use of metaheuristics sets this subfield of robotics apart from the mainstream of robotics research, in which machine learning algorithms are used to optimize the control policya of a robot. As in other branches of computer science the use of a metaheuristic algorithm has a cost and a benefit. The cost is that it is not possible to guarantee if (or when) an optimal control policy will be found for a given robot. The benefit is few assumptions must be made about the problem: evolutionary algorithms can improve both the parameters and the architecture of the robot’s control policy, and even the shape of the robot itself.

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Optimization of nonequilibrium free energy harvesting illustrated on bacteriorhodopsin

Jordi Piñero, Ricard Solé, and Artemy Kolchinsky
Phys. Rev. Research 6, 013275

Harvesting free energy from the environment is essential for the operation of many biological and artificial systems. We use techniques from stochastic thermodynamics to investigate the maximum rate of harvesting achievable by optimizing a set of reactions in a Markovian system, possibly under various kinds of topological, kinetic, and thermodynamic constraints. This question is relevant for the optimal design of new harvesting devices as well as for quantifying the efficiency of existing systems. We first demonstrate that the maximum harvesting rate can be expressed as a constrained convex optimization problem. We illustrate it on bacteriorhodopsin, a light-driven proton pump from Archaea, which we find is close to optimal under realistic conditions. In our second result, we solve the optimization problem in closed-form in three physically meaningful limiting regimes. These closed-form solutions are illustrated on two idealized models of unicyclic harvesting systems.

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Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Wagner, N.

Systems 2024, 12(3), 96

Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.

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A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US

Matteo Chinazzi, Jessica T. Davis, Ana Pastore y Piontti, Kunpeng Mu, Nicolò Gozzi, Marco Ajelli, Nicola Perra, Alessandro Vespignani

Epidemics

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure underpinning our model, and present as a case study the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7). Our findings reveal considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the significant impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

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Discord in the voter model for complex networks

Discord in the voter model for complex networks | Papers | Scoop.it

Antoine Vendeville, Shi Zhou, and Benjamin Guedj
Phys. Rev. E 109, 024312

Online social networks have become primary means of communication. As they often exhibit undesirable effects such as hostility, polarization, or echo chambers, it is crucial to develop analytical tools that help us better understand them. In this paper we are interested in the evolution of discord in social networks. Formally, we introduce a method to calculate the probability of discord between any two agents in the multistate voter model with and without zealots. Our work applies to any directed, weighted graph with any finite number of possible opinions, allows for various update rates across agents, and does not imply any approximation. Under certain topological conditions, the opinions are independent and the joint distribution can be decoupled. Otherwise, the evolution of discord probabilities is described by a linear system of ordinary differential equations. We prove the existence of a unique equilibrium solution, which can be computed via an iterative algorithm. The classical definition of active links density is generalized to take into account long-range, weighted interactions. We illustrate our findings on real-life and synthetic networks. In particular, we investigate the impact of clustering on discord and uncover a rich landscape of varied behaviors in polarized networks. This sheds lights on the evolution of discord between, and within, antagonistic communities.

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An "Opinion Reproduction Number" for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Heather Z. Brooks, Mason A. Porter

We study the spreading dynamics of content on networks. To do this, we use a model in which content spreads through a bounded-confidence mechanism. In a bounded-confidence model (BCM) of opinion dynamics, the agents of a network have continuous-valued opinions, which they adjust when they interact with agents whose opinions are sufficiently close to theirs. The employed content-spread model introduces a twist into BCMs by using bounded confidence for the content spread itself. To study the spread of content, we define an analogue of the basic reproduction number from disease dynamics that we call an \emph{opinion reproduction number}. A critical value of the opinion reproduction number indicates whether or not there is an ``infodemic'' (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion will die off or propagate widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination using a variety of summary statistics, and we illustrate how network structure and spreading model parameters affect these statistics. We find that content spreads most widely when the agents have large expected mean degree or large receptiveness to content. When the amount of content spread only slightly exceeds the critical opinion reproduction number (i.e., the infodemic threshold), there can be longer dissemination trees than when the expected mean degree or receptiveness is larger, even though the total number of content shares is smaller.

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Intercity connectivity and urban innovation

Xiaofan Liang, César A. Hidalgo, Pierre-Alexandre Balland, Siqi Zheng, Jianghao Wang

Computers, Environment and Urban Systems Volume 109, April 2024, 102092

Urban outputs, from economy to innovation, are known to grow as a power of a city's population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world's two largest economies (the United States and China) to explore whether a city's position in the networks of communication, human mobility, and scientific collaboration explains variance in a city's patenting activity that is unaccounted for by its population. We find evidence that models incorporating intercity connectivity outperform population-based models and exhibit stronger predictive power for patenting activity, particularly for technologies of more recent vintage (which we expect to be more complex or sophisticated). The effects of intercity connectivity are more robust in China, even after controlling for population, GDP, and education, but not in the United States once adjusted for GDP and education. This divergence suggests distinct urban network dynamics driving innovation in these regions. In China, models with social media and mobility networks explain more heterogeneity in the scaling of innovation, whereas in the United States, scientific collaboration plays a more significant role. These findings support the significance of a city's position within the intercity network in shaping its success in innovative activities.

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LLM Voting: Human Choices and AI Collective Decision Making

Joshua C. Yang, Marcin Korecki, Damian Dailisan, Carina I. Hausladen, Dirk Helbing

This paper investigates the voting behaviors of Large Language Models (LLMs), particularly OpenAI's GPT4 and LLaMA2, and their alignment with human voting patterns. Our approach included a human voting experiment to establish a baseline for human preferences and a parallel experiment with LLM agents. The study focused on both collective outcomes and individual preferences, revealing differences in decision-making and inherent biases between humans and LLMs. We observed a trade-off between preference diversity and alignment in LLMs, with a tendency towards more uniform choices as compared to the diverse preferences of human voters. This finding indicates that LLMs could lead to more homogenized collective outcomes when used in voting assistance, underscoring the need for cautious integration of LLMs into democratic processes.

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The 15-minute city quantified using human mobility data

The 15-minute city quantified using human mobility data | Papers | Scoop.it

Timur Abbiasov, Cate Heine, Sadegh Sabouri, Arianna Salazar-Miranda, Paolo Santi, Edward Glaeser & Carlo Ratti
Nature Human Behaviour (2024)

Amid rising congestion and transport emissions, policymakers are embracing the ‘15-minute city’ model, which envisions neighbourhoods where basic needs can be met within a short walk from home. Prior research has primarily examined amenity access without exploring its relationship to behaviour. We introduce a measure of local trip behaviour using GPS data from 40 million US mobile devices, defining ‘15-minute usage’ as the proportion of consumption-related trips made within a 15-minute walk from home. Our findings show that the median resident makes only 14% of daily consumption trips locally. Differences in access to local amenities can explain 84% and 74% of the variation in 15-minute usage across and within urban areas, respectively. Historical data from New York zoning policies suggest a causal relationship between local access and 15-minute usage. However, we find a trade-off: increased local usage correlates with higher experienced segregation for low-income residents, signalling potential socio-economic challenges in achieving local living.

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An ability to respond begins with inner alignment: How phase synchronisation effects transitions to higher levels of agency

Tazzio Tissot, Mike Levin, View Chris Buckley, Richard Watson

How do multiple active components at one level of organisation create agential wholes at higher levels of organisation? For example, in organismic development, how does the multi-scale autonomy of the organism arise from the interactions of the molecules, cells and tissues that an organism contains? And, in the major evolutionary transitions, how does a multicellular organism, for example, arise as an evolutionary unit from the selective interests of its unicellular ancestors? We utilise computational models as a way to think about this general question. We take a deliberately minimalistic notion of an agent: a competency to take one of two possible actions to minimise stress. Helping ourselves to this behaviour at the microscale, we focus on conditions where this same type of agency appears spontaneously at a higher level of organisation. We find that a simple process of positive feedback on the timing of individual responses, loosely analogous to the natural phase synchronisation of weakly coupled oscillators, causes such a transition in behaviour. The emergent collectives that arise become, quite suddenly, able to respond to their external stresses in the same (minimal) sense as the original microscale units. This effects a dramatic rescaling of the system behaviour, and a quantifiable increase in problem-solving competency, serving as a model of how higher-level agency emerges from a pool of lower-level agents or active matter. We discuss how this dynamical ‘waking-up’ of higher-level collectives, through the alignment of their internal dynamics, might relate to reproductive/cell-cycle synchronisation in evolutionary transitions and development.

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All Crises are Unhappy in their Own Way: The role of societal instability in shaping the past

Daniel Hoyer, Samantha Holder, James S Bennett, Pieter François, Harvey Whitehouse, Alan Covey, Gary Feinman, Andrey Korotayev, Vadim Vustiuzhanin, Johannes Preiser-Kapeller, Kathryn Bard, Jill Levine, Jenny Reddish, Georg Orlandi, Rachel Ainsworth, and Peter Turchin

Societal ‘crises’ are periods of turmoil and destabilization in socio-cultural, political, economic, and other systems, often accompanied by varying amounts of violence and sometimes significant changes in social structure. The extensive literature analyzing societal crises has concentrated on relatively few historical examples (large-scale events such the fall of the Roman Empire or the French and Russian Revolutions) emphasizing different aspects of these events as potential causes or consistent effects. To investigate crises and prior approaches to explaining them, and to avoid a potential small-sample size bias present in several previous studies, we sought to uniformly characterize a substantial collection of historical crises, spanning millennia, from the prehistoric to post-industrial, and afflicting a wide range of polities in diverse global regions; the Crisis Database (CrisisDB). Here, we describe this dataset which comprises 168 crises suggested by historians and characterized by a number of significant 'consequences' (such as civil war, epidemics, or loss of population) including also institutional and cultural reforms (for example improved sufferance or constitutional changes) that might occur during and immediately following the crisis period. Our analyses show that the consequences experienced by each crisis is highly variable. The outcomes themselves are uncorrelated with one another and, overall, the set of consequences is largely unpredictable when compared to other large-scale properties of society suggested by previous scholars such as its territorial size, religion, administrative size, or historical recency. We conclude that there is no ‘typical’ societal crisis of the past, but crisis situations can take a variety of different directions. We offer some suggestions on the forces that might drive these varying consequences for exploration in future work.

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Cultural Evolution, Disinformation, and Social Division

R Alexander Bentley, Benjamin Horne, Joshua Borycz, Simon Carrignon, Garriy Shteynberg, Blai Vidiella, Sergi Valverde, and Michael J O’Brien

Adaptive Behavior Volume 32, Issue 2

Diversity of expertise is inherent to cultural evolution. When it is transparent, diversity of human knowledge is useful; when social conformity overcomes that transparency, “expertise” can lead to divisiveness. This is especially true today, where social media has increasingly allowed misinformation to spread by prioritizing what is recent and popular, regardless of validity or general benefit. Whereas in traditional societies there was diversity of expertise, contemporary social media facilitates homophily, which isolates true subject experts from each other and from the wider population. Diversity of knowledge thus becomes social division. Here, we discuss the potential of a cultural-evolutionary framework designed for the countless choices in contemporary media. Cultural-evolutionary theory identifies key factors that determine whether communication networks unify or fragment knowledge. Our approach highlights two parameters: transparency of information and social conformity. By identifying online spaces exhibiting aggregate patterns of high popularity bias and low transparency of information, we can help define the “safe limits” of social conformity and information overload in digital communications.

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Complex networks with complex weights

Complex networks with complex weights | Papers | Scoop.it

Lucas Böttcher and Mason A. Porter

Phys. Rev. E 109, 024314

In many studies, it is common to use binary (i.e., unweighted) edges to examine networks of entities that are either adjacent or not adjacent. Researchers have generalized such binary networks to incorporate edge weights, which allow one to encode node–node interactions with heterogeneous intensities or frequencies (e.g., in transportation networks, supply chains, and social networks). Most such studies have considered real-valued weights, despite the fact that networks with complex weights arise in fields as diverse as quantum information, quantum chemistry, electrodynamics, rheology, and machine learning. Many of the standard network-science approaches in the study of classical systems rely on the real-valued nature of edge weights, so it is necessary to generalize them if one seeks to use them to analyze networks with complex edge weights. In this paper, we examine how standard network-analysis methods fail to capture structural features of networks with complex edge weights. We then generalize several network measures to the complex domain and show that random-walk centralities provide a useful approach to examine node importances in networks with complex weights.

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Measuring Entanglement in Physical Networks

Cory Glover, Albert-László Barabási
The links of a physical network cannot cross, which often forces the network layout into non-optimal entangled states. Here we define a network fabric as a two-dimensional projection of a network and propose the average crossing number as a measure of network entanglement. We analytically derive the dependence of the crossing number on network density, average link length, degree heterogeneity, and community structure and show that the predictions accurately estimate the entanglement of both network models and of real physical networks.

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Disentangling the Timescales of a Complex System: A Bayesian Approach to Temporal Network Analysis

Giona Casiraghi, Georges Andres
Changes in the timescales at which complex systems evolve are essential to predicting critical transitions and catastrophic failures. Disentangling the timescales of the dynamics governing complex systems remains a key challenge. With this study, we introduce an integrated Bayesian framework based on temporal network models to address this challenge. We focus on two methodologies: change point detection for identifying shifts in system dynamics, and a spectrum analysis for inferring the distribution of timescales. Applied to synthetic and empirical datasets, these methologies robustly identify critical transitions and comprehensively map the dominant and subsidiaries timescales in complex systems. This dual approach offers a powerful tool for analyzing temporal networks, significantly enhancing our understanding of dynamic behaviors in complex systems.

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Tests for consciousness in humans and beyond

Tim Bayne, Anil K. Seth, Marcello Massimini, Joshua Shepherd, Axel Cleeremans, Stephen M. Fleming, Rafael Malach, Jason B. Mattingley, David K. Menon, Adrian M. Owen, Megan A.K. Peters, Adeel Razi, Liad Mudrik

Trends in Cognitive Science

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are
urgently needed. There is persisting uncertainty about when consciousness arises in
human development, when it is lost due to neurological disorders and brain injury,
and how it is distributed in nonhuman species. This need is amplified by recent and
rapid developments in artificial intelligence (AI), neural organoids, and xenobot
technology. Although a number of C-tests have been proposed in recent years, most
are of limited use, and currently we have no C-tests for many of the populations in
which they are most urgently needed. Here, we identify challenges facing any attempt
to develop C-tests, propose a multidimensional classification of such tests, and identify
strategies that might be used to validate them.

Read the full article at: www.cell.com

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Cell reprogramming design by transfer learning of functional transcriptional networks

Thomas P. Wytock and Adilson E. Motter

PNAS 121 (11) e2312942121

The lack of genome-wide mathematical models for the gene regulatory network complicates the application of control theory to manipulate cell behavior in humans. We address this challenge by developing a transfer learning approach that leverages genome-wide transcriptomic profiles to characterize cell type attractors and perturbation responses. These responses are used to predict a combinatorial perturbation that minimizes the transcriptional difference between an initial and target cell type, bringing the regulatory network to the target cell type basin of attraction. We anticipate that this approach will enable the rapid identification of potential targets for treatment of complex diseases, while also providing insight into how the dynamics of gene regulatory networks affect phenotype.
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Bumblebees socially learn behaviour too complex to innovate alone

Alice D. Bridges, Amanda Royka, Tara Wilson, Charlotte Lockwood, Jasmin Richter, Mikko Juusola & Lars Chittka
Nature (2024)

Culture refers to behaviours that are socially learned and persist within a population over time. Increasing evidence suggests that animal culture can, like human culture, be cumulative: characterized by sequential innovations that build on previous ones1. However, human cumulative culture involves behaviours so complex that they lie beyond the capacity of any individual to independently discover during their lifetime1,2,3. To our knowledge, no study has so far demonstrated this phenomenon in an invertebrate. Here we show that bumblebees can learn from trained demonstrator bees to open a novel two-step puzzle box to obtain food rewards, even though they fail to do so independently. Experimenters were unable to train demonstrator bees to perform the unrewarded first step without providing a temporary reward linked to this action, which was removed during later stages of training. However, a third of naive observer bees learned to open the two-step box from these demonstrators, without ever being rewarded after the first step. This suggests that social learning might permit the acquisition of behaviours too complex to ‘re-innovate’ through individual learning. Furthermore, naive bees failed to open the box despite extended exposure for up to 24 days. This finding challenges a common opinion in the field: that the capacity to socially learn behaviours that cannot be innovated through individual trial and error is unique to humans.

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Emergence of innovations in networked populations with reputation-driven interactions

Emergence of innovations in networked populations with reputation-driven interactions | Papers | Scoop.it

Pablo Gallarta-Sáenz, Hugo Pérez-Martínez,  Jesús Gómez-Gardeñes

Chaos 34, 033106 (2024)

In this work, we analyze how reputation-based interactions influence the emergence of innovations. To do so, we make use of a dynamic model that mimics the discovery process by which, at each time step, a pair of individuals meet and merge their knowledge to eventually result in a novel technology of higher value. The way in which these pairs are brought together is found to be crucial for achieving the highest technological level. Our results show that when the influence of reputation is weak or moderate, it induces an acceleration of the discovery process with respect to the neutral case (purely random coupling). However, an excess of reputation is clearly detrimental, because it leads to an excessive concentration of knowledge in a small set of people, which prevents a diversification of the technologies discovered and, in addition, leads to societies in which a majority of individuals lack technical capabilities.

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Network topology mapping of chemical compounds space

Georgios Tsekenis, Giulio Cimini, Marinos Kalafatis, Achille Giacometti, Tommaso Gili & Guido Caldarelli
Scientific Reports volume 14, Article number: 5266 (2024)

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen . Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.

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A nonadaptive explanation for macroevolutionary patterns in the evolution of complex multicellularity

Emma P. Bingham and William C. Ratcliff

PNAS 121 (7) e2319840121

“Complex multicellularity,” conventionally defined as large organisms with many specialized cell types, has evolved five times independently in eukaryotes, but never within prokaryotes. A number of hypotheses have been proposed to explain this phenomenon, most of which posit that eukaryotes evolved key traits (e.g., dynamic cytoskeletons, alternative mechanisms of gene regulation, or subcellular compartments) which were a necessary prerequisite for the evolution of complex multicellularity. Here, we propose an alternative, nonadaptive hypothesis for this broad macroevolutionary pattern. By binning cells into groups with finite genetic bottlenecks between generations, the evolution of multicellularity greatly reduces the effective population size (Ne) of cellular populations, increasing the role of genetic drift in evolutionary change. While both prokaryotes and eukaryotes experience this phenomenon, they have opposite responses to drift: eukaryotes tend to undergo genomic expansion, providing additional raw genetic material for subsequent multicellular innovation, while prokaryotes generally face genomic erosion. Taken together, we hypothesize that these idiosyncratic lineage-specific evolutionary dynamics play a fundamental role in the long-term divergent evolution of complex multicellularity across the tree of life.

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Modelling the creation of friends and foes groups in small real social networks

Modelling the creation of friends and foes groups in small real social networks | Papers | Scoop.it

García-Rodríguez A, Govezensky T, Naumis GG, Barrio RA

PLoS ONE 19(2): e0298791

Although friendship networks have been extensively studied, few models and studies are available to understand the reciprocity of friendship and foes. Here a model is presented to explain the directed friendship and foes network formation observed in experiments of Mexican and Hungarian schools. Within the presented model, each agent has a private opinion and a public one that shares to the group. There are two kinds of interactions between agents. The first kind represent interactions with the neighbors while the other represents the attitude of an agent to the overall public available information. Links between agents evolve as a combination of the public and private information available. Friendship is defined using a fitness function according to the strength of the agent’s bonds, clustering coefficient, betweenness centrality and degree. Enmity is defined as very negative links. The model allows us to reproduce the distribution of mentions for friends and foes observed in the experiments, as well as the topology of the directed networks.

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Stochastic regimes can hide the attractors in data, reconstruction algorithms can reveal them

Babak M. S. Arani, Stephen R. Carpenter, Egbert H. van Nes, Ingrid A. van de Leemput, Chi Xu, Pedro G. Lind, Marten Scheffer

Tipping points and alternative attractors have become an important focus of research and public discussions about the future of climate, ecosystems and societies. However, empirical evidence for the existence of alternative attractors remains scarce. For example, bimodal frequency distributions of state variables may suggest bistability, but can also be due to bimodality in external conditions. Here, we bring a new dimension to the classical arguments on alternative stable states and their resilience showing that the stochastic regime can distort the relationship between the probability distribution of states and the underlying attractors. Simple additive Gaussian white noise produces a one-to-one correspondence between the modes of frequency distributions and alternative stable states. However, for more realistic types of noise, the number and position of modes of the frequency distribution do not necessarily match the equilibria of the underlying deterministic system. We show that data must represent the stochastic regime as thoroughly as possible. When data are adequate then existing methods can be used to determine the nature of the underlying deterministic system and noise simultaneously. This may help resolve the question of whether there are tipping points, but also how realized states of a system are shaped by stochastic forcing vs internal stability properties.

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Software in the natural world: A computational approach to emergence in complex multi-level systems

Fernando E. Rosas, Bernhard C. Geiger, Andrea I Luppi, Anil K. Seth, Daniel Polani, Michael Gastpar, Pedro A.M. Mediano

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalism that articulates how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.

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A synthetic microbial Daisyworld: planetary regulation in the test tube

Victor Maull , Jordi Pla Mauri , Nuria Conde Pueyo and Ricard Solé

JRS Interface February 2024 Volume 21 Issue 211

The idea that the Earth system self-regulates in a habitable state was proposed in the 1970s by James Lovelock, who conjectured that life plays a self-regulatory role on a planetary-level scale. A formal approach to such hypothesis was presented afterwards under a toy model known as the Daisyworld. The model showed how such life-geosphere homeostasis was an emergent property of the system, where two species with different properties adjusted their populations to the changing external environment. So far, this ideal world exists only as a mathematical or computational construct, but it would be desirable to have a real, biological implementation of Lovelock’s picture beyond our one biosphere. Inspired by the exploration of synthetic ecosystems using genetic engineering and recent cell factory designs, here we propose a possible implementation for a microbial Daisyworld. This includes: (i) an explicit proposal for an engineered design of a two-strain consortia, using pH as the external, abiotic control parameter and (ii) several theoretical and computational case studies including two, three and multiple species assemblies. The special alternative implementations and their implications in other synthetic biology scenarios, including ecosystem engineering, are outlined.

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