Papers
552.1K views | +10 today
Follow
Papers
Recent publications related to complex systems
Your new post is loading...
Your new post is loading...
Scooped by Complexity Digest
Scoop.it!

Extracting real social interactions from social media: a debate of COVID-19 policies in Mexico

Alberto García-Rodríguez, Tzipe Govezensky, Carlos Gershenson, Gerardo G. Naumis, Rafael A. Barrio
A study of the dynamical formation of networks of friends and enemies in social media, in this case Twitter, is presented. We characterise the single node properties of such networks, as the clustering coefficient and the degree, to investigate the structure of links. The results indicate that the network is made from three kinds of nodes: one with high clustering coefficient but very small degree, a second group has zero clustering coefficient with variable degree, and finally, a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents ∼2% of the nodes and is characteristic of dynamical networks with feedback. This part of the lattice seemingly represents strongly interacting friends in a real social network.

Read the full article at: arxiv.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Towards an engineering theory of evolution

Towards an engineering theory of evolution | Papers | Scoop.it

Simeon D. Castle, Claire S. Grierson & Thomas E. Gorochowski
Nature Communications volume 12, Article number: 3326 (2021)

Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution’s potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology. Effective biological engineering requires the acknowledgement of evolution and its consideration during the design process. In this perspective, the authors present the concept of the evotype to reason about and shape the evolutionary potential of natural and engineered biosystems.

Read the full article at: www.nature.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity | Papers | Scoop.it

Georgina Montserrat Reséndiz-Benhumea, Ekaterina Sangati, Federico Sangati, Soheil Keshmiri and Tom Froese

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial insects. Different hypotheses can be identified in the literature to explain this reduction in brain size. We evaluate some of them from the perspective of recent approaches to cognitive science, which support the idea that the basis of cognition can span over brain, body, and environment. Here we show through a minimal cognitive model using an evolutionary robotics methodology that the neural complexity, in terms of neural entropy and degrees of freedom of neural activity, of smaller-brained agents evolved in social interaction is comparable to the neural complexity of larger-brained agents evolved in solitary conditions. The nonlinear time series analysis of agents' neural activity reveals that the decoupled smaller neural network is intrinsically lower dimensional than the decoupled larger neural network. However, when smaller-brained agents are interacting, their actual neural complexity goes beyond its intrinsic limits achieving results comparable to those obtained by larger-brained solitary agents. This suggests that the smaller-brained agents are able to enhance their neural complexity through social interaction, thereby offsetting the reduced brain size.

Read the full article at: www.frontiersin.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Conspiracy of Corporate Networks in Corruption Scandals

Conspiracy of Corporate Networks in Corruption Scandals | Papers | Scoop.it

J. R. Nicolás-Carlock & I. Luna-Pla

Front. Phys

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.

Read the full article at: www.frontiersin.org

No comment yet.
Suggested by Fil Menczer
Scoop.it!

Uncovering Coordinated Networks on Social Media: Methods and Case Studies

Uncovering Coordinated Networks on Social Media: Methods and Case Studies | Papers | Scoop.it

Coordinated campaigns are used to manipulate social media platforms and influence their users, a critical challenge to the free exchange of information. Our paper introduces a general, unsupervised, network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.

By Diogo Pacheco, Pik-Mai Hui, Chris Torres, Bao Truong, Sandro Flammini & Fil Menczer

Read the full open-access article from the Proceedings ICWSM2021

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Bad machines corrupt good morals

Bad machines corrupt good morals | Papers | Scoop.it

Nils Köbis, Jean-François Bonnefon & Iyad Rahwan 
Nature Human Behaviour (2021)

As machines powered by artificial intelligence (AI) influence humans’ behaviour in ways that are both like and unlike the ways humans influence each other, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioural science, human–computer interaction and AI research. We propose four main social roles through which both humans and machines can influence ethical behaviour. These are: role model, advisor, partner and delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed the corrupting power of humans (yet). However, AI agents acting as enablers of unethical behaviour (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, a potentially perilous interaction. On the basis of these insights, we outline a research agenda to gain behavioural insights for better AI oversight.

Read the full article at: www.nature.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Revealing Consensus and Dissensus between Network Partitions

Revealing Consensus and Dissensus between Network Partitions | Papers | Scoop.it

Tiago P. Peixoto
Phys. Rev. X 11, 021003

Community detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. A major challenge when employing such methods is that they are often degenerate, typically yielding a complex landscape of competing answers. As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition “point estimate” that summarizes the whole distribution. Here, we show that it is, in general, not possible to obtain a consistent answer from such point estimates when the underlying distribution is too heterogeneous. As an alternative, we provide a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus but also the dissensus between elements of the population. Our approach is able to model mixed populations of partitions, where multiple consensuses can coexist, representing different competing hypotheses for the network structure. We also show how our methods can be used to compare pairs of partitions, how they can be generalized to hierarchical divisions, and how they can be used to perform statistical model selection between competing hypotheses.

Read the full article at: link.aps.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Artificial life: sustainable self-replicating systems

Carlos Gershenson, Jitka Cejkova
Nature has found one method of organizing living matter, but maybe there are also other options -- not yet discovered -- on how to create life. To study the life as it could be is the objective of an interdisciplinary field called Artificial Life (commonly abbreviated as ALife). The word "artificial" refers to the fact that humans are involved in the creation process. The results might be completely unlike natural forms of life, not only because of their chemical composition, but even some computer programs exhibiting life-like behaviours interest ALife researchers.

Read the full article at: arxiv.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Controlling COVID-19 via test-trace-quarantine

Controlling COVID-19 via test-trace-quarantine | Papers | Scoop.it

Cliff C. Kerr, Dina Mistry, Robyn M. Stuart, Katherine Rosenfeld, Gregory R. Hart, Rafael C. Núñez, Jamie A. Cohen, Prashanth Selvaraj, Romesh G. Abeysuriya, Michał Jastrzębski, Lauren George, Brittany Hagedorn, Jasmina Panovska-Griffiths, Meaghan Fagalde, Jeffrey Duchin, Michael Famulare & Daniel J. Klein
Nature Communications volume 12, Article number: 2993 (2021)

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required. Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures, with enormous societal and economic costs. Here, the authors demonstrate the feasibility of a test-trace-quarantine strategy using an agent-based model and detailed data on the Seattle region.

Read the full article at: www.nature.com

No comment yet.
Rescooped by Complexity Digest from Statistical Physics of Ecological Systems
Scoop.it!

The Widened Pipe Model of plant hydraulic evolution

The Widened Pipe Model of plant hydraulic evolution | Papers | Scoop.it

For most of its path through plant bodies, water moves in conduits in the wood. Plant water conduction is crucial for Earth’s biogeochemical cycles, making it important to understand how natural selection shapes conduit diameters along the entire lengths of plant stems. Can mathematical modeling and global sampling explain how wood conduits ought to widen from the tip of a plant to its trunk base? This question is evolutionarily important because xylem conduits should widen in a way that keeps water supply constant to the leaves as a plant grows taller. Moreover, selection should act on economy of construction costs of the conducting system. This issue is ecologically important because it helps suggest why climate change alters vegetation height worldwide.

Read the full article at: www.pnas.org


Via Samir
No comment yet.
Scooped by Complexity Digest
Scoop.it!

On the utility of dreaming: A general model for how learning in artificial agents can benefit from data hallucination

David Windridge, Henrik Svensson, Serge Thill

Adaptive Behavior


We consider the benefits of dream mechanisms – that is, the ability to simulate new experiences based on past ones – in a machine learning context. Specifically, we are interested in learning for artificial agents that act in the world, and operationalize “dreaming” as a mechanism by which such an agent can use its own model of the learning environment to generate new hypotheses and training data.

We first show that it is not necessarily a given that such a data-hallucination process is useful, since it can easily lead to a training set dominated by spurious imagined data until an ill-defined convergence point is reached. We then analyse a notably successful implementation of a machine learning-based dreaming mechanism by Ha and Schmidhuber (Ha, D., & Schmidhuber, J. (2018). World models. arXiv e-prints, arXiv:1803.10122). On that basis, we then develop a general framework by which an agent can generate simulated data to learn from in a manner that is beneficial to the agent. This, we argue, then forms a general method for an operationalized dream-like mechanism.

We finish by demonstrating the general conditions under which such mechanisms can be useful in machine learning, wherein the implicit simulator inference and extrapolation involved in dreaming act without reinforcing inference error even when inference is incomplete.

Read the full article at: journals.sagepub.com

No comment yet.
Suggested by Christoph Riedl
Scoop.it!

Quantifying collective intelligence in human groups

Christoph Riedl, Young Ji Kim, Pranav Gupta, Thomas W. Malone, and Anita Williams Woolley

PNAS May 25, 2021 118 (21) e2005737118

Collective intelligence (CI) is critical to solving many scientific, business, and other problems. We find strong support for a general factor of CI using meta-analytic methods in a dataset comprising 22 studies, including 5,279 individuals in 1,356 groups. CI can predict performance in a range of out-of-sample criterion tasks. CI, in turn, is most strongly predicted by group collaboration process, followed by individual skill and group composition. The proportion of women in a group is a significant predictor of group performance, mediated by social perceptiveness.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Emergence in artificial life

Carlos Gershenson
Concepts similar to emergence have been used since antiquity, but we lack an agreed definition of emergence. Still, emergence has been identified as one of the features of complex systems. Most would agree on the statement "life is complex". Thus, understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understand living systems? Artificial life (ALife) has been developed in recent decades to study life using a synthetic approach: build it to understand it. ALife systems are not so complex, be them soft (simulations), hard (robots), or wet (protocells). Then, we can aim at first understanding emergence in ALife, for then using this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, emergence can be defined as information that is not present at one scale but is present at another scale. This perspective avoids problems of studying emergence from a materialistic framework, and can be useful to study self-organization and complexity.

Read the full article at: arxiv.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Thermodynamic Efficiency of Interactions in Self-Organizing Systems

Thermodynamic Efficiency of Interactions in Self-Organizing Systems | Papers | Scoop.it

Ramil Nigmatullin and Mikhail Prokopenko

Entropy 2021, 23(6), 757

The emergence of global order in complex systems with locally interacting components is most striking at criticality, where small changes in control parameters result in a sudden global reorganization. We study the thermodynamic efficiency of interactions in self-organizing systems, which quantifies the change in the system’s order per unit of work carried out on (or extracted from) the system. We analytically derive the thermodynamic efficiency of interactions for the case of quasi-static variations of control parameters in the exactly solvable Curie–Weiss (fully connected) Ising model, and demonstrate that this quantity diverges at the critical point of a second-order phase transition. This divergence is shown for quasi-static perturbations in both control parameters—the external field and the coupling strength. Our analysis formalizes an intuitive understanding of thermodynamic efficiency across diverse self-organizing dynamics in physical, biological, and social domains.

Read the full article at: www.mdpi.com

No comment yet.
Suggested by Anastasio Giovanidis
Scoop.it!

Ranking online social users by their Influence

Ranking online social users by their Influence | Papers | Scoop.it

Anastasios Giovanidis, Bruno Baynat, Clémence Magnien & Antoine Vendeville
IEEE/ACM Transactions on Networking ( Early Access ) (2021)

Date of Publication: 08 June 2021

This work introduces an original mathematical model to analyze the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with his/her own Wall and Newsfeed, and has his/her own individual self-posting and re-posting activity. As a main result using the developed model, the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other can be derived in closed form. These are the solution of a linear system of equations, which can be resolved iteratively. In fact, the new model is very flexible with respect to the modeling assumptions. Using the probabilities derived from the solution, a new measure of per-user influence over the entire network is defined, named the Ψ-score, which combines the user position on the graph with user (re-)posting activity. In the homogeneous case where all users have the same activity rates, it is shown that a variant of the Ψ-score is equal to PageRank. Furthermore, the new model and its Ψ-score are compared against the empirical influence measured from very large data traces (Twitter, Weibo). The results illustrate that these new tools can accurately rank influencers with asymmetric (re-)posting activity for such real world applications.

Read the full article at:  ieeexplore.ieee.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Designing temporal networks that synchronize under resource constraints

Designing temporal networks that synchronize under resource constraints | Papers | Scoop.it

Yuanzhao Zhang & Steven H. Strogatz 
Nature Communications volume 12, Article number: 3273 (2021)

Being fundamentally a non-equilibrium process, synchronization comes with unavoidable energy costs and has to be maintained under the constraint of limited resources. Such resource constraints are often reflected as a finite coupling budget available in a network to facilitate interaction and communication. Here, we show that introducing temporal variation in the network structure can lead to efficient synchronization even when stable synchrony is impossible in any static network under the given budget, thereby demonstrating a fundamental advantage of temporal networks. The temporal networks generated by our open-loop design are versatile in the sense of promoting synchronization for systems with vastly different dynamics, including periodic and chaotic dynamics in both discrete-time and continuous-time models. Furthermore, we link the dynamic stabilization effect of the changing topology to the curvature of the master stability function, which provides analytical insights into synchronization on temporal networks in general. In particular, our results shed light on the effect of network switching rate and explain why certain temporal networks synchronize only for intermediate switching rate.

Read the full article at: www.nature.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Is Green Development an Oxymoron?

Is Green Development an Oxymoron? | Papers | Scoop.it

Ricardo Hausmann

Decarbonization will transform global production and trade patterns so radically that new growth opportunities are bound to arise for the Global South. The goal for them should not be to stop global warming by restricting domestic emissions, but rather to carve out a role for themselves in a rapidly greening world economy.

Read the full article at: www.project-syndicate.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

The Impossibility of Automating Ambiguity

Abeba Birhane

Artificial Life

On the one hand, complexity science and enactive and embodied cognitive science approaches emphasize that people, as complex adaptive systems, are ambiguous, indeterminable, and inherently unpredictable. On the other, Machine Learning (ML) systems that claim to predict human behaviour are becoming ubiquitous in all spheres of social life. I contend that ubiquitous Artificial Intelligence (AI) and ML systems are close descendants of the Cartesian and Newtonian worldview in so far as they are tools that fundamentally sort, categorize, and classify the world, and forecast the future. Through the practice of clustering, sorting, and predicting human behaviour and action, these systems impose order, equilibrium, and stability to the active, fluid, messy, and unpredictable nature of human behaviour and the social world at large. Grounded in complexity science and enactive and embodied cognitive science approaches, this article emphasizes why people, embedded in social systems, are indeterminable and unpredictable. When ML systems “pick up” patterns and clusters, this often amounts to identifying historically and socially held norms, conventions, and stereotypes. Machine prediction of social behaviour, I argue, is not only erroneous but also presents real harm to those at the margins of society.

Read the full article at: direct.mit.edu

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Evolution of Autopoiesis and Multicellularity in the Game of Life

Peter D. Turney

Artificial Life

Recently we introduced a model of symbiosis, Model-S, based on the evolution of seed patterns in Conway's Game of Life. In the model, the fitness of a seed pattern is measured by one-on-one competitions in the Immigration Game, a two-player variation of the Game of Life. Our previous article showed that Model-S can serve as a highly abstract, simplified model of biological life: (1) The initial seed pattern is analogous to a genome. (2) The changes as the game runs are analogous to the development of the phenome. (3) Tournament selection in Model-S is analogous to natural selection in biology. (4) The Immigration Game in Model-S is analogous to competition in biology. (5) The first three layers in Model-S are analogous to biological reproduction. (6) The fusion of seed patterns in Model-S is analogous to symbiosis. The current article takes this analogy two steps further: (7) Autopoietic structures in the Game of Life (still lifes, oscillators, and spaceships—collectively known as ashes) are analogous to cells in biology. (8) The seed patterns in the Game of Life give rise to multiple, diverse, cooperating autopoietic structures, analogous to multicellular biological life. We use the apgsearch software (Ash Pattern Generator Search), developed by Adam Goucher for the study of ashes, to analyze autopoiesis and multicellularity in Model-S. We find that the fitness of evolved seed patterns in Model-S is highly correlated with the diversity and quantity of multicellular autopoietic structures.

Read the full article at: direct.mit.edu

No comment yet.
Scooped by Complexity Digest
Scoop.it!

The universal visitation law of human mobility

The universal visitation law of human mobility | Papers | Scoop.it

Markus Schläpfer, Lei Dong, Kevin O’Keeffe, Paolo Santi, Michael Szell, Hadrien Salat, Samuel Anklesaria, Mohammad Vazifeh, Carlo Ratti & Geoffrey B. West
Nature volume 593, pages 522–527 (2021)

Human mobility impacts many aspects of a city, from its spatial structure to its response to an epidemic. It is also ultimately key to social interactions, innovation and productivity. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models—such as the gravity law or the radiation model—concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf’s law. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory and Weber’s theory of emergent optimality) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.

Read the full article at: www.nature.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Identifying molecules as biosignatures with assembly theory and mass spectrometry

Identifying molecules as biosignatures with assembly theory and mass spectrometry | Papers | Scoop.it

Stuart M. Marshall, Cole Mathis, Emma Carrick, Graham Keenan, Geoffrey J. T. Cooper, Heather Graham, Matthew Craven, Piotr S. Gromski, Douglas G. Moore, Sara. I. Walker & Leroy Cronin 

Nature Communications volume 12, Article number: 3033 (2021)

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.

Read the full article at: www.nature.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Synthetic living machines: A new window on life

Synthetic living machines: A new window on life | Papers | Scoop.it


Mo R. Ebrahimkhani & Michael Levin

iScience Volume 24, Issue 5, 21 May 2021, 102505

Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.

Read the full article at: www.sciencedirect.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Random heterogeneity outperforms design in network synchronization

Yuanzhao Zhang, Jorge L. Ocampo-Espindola, István Z. Kiss, and Adilson E. Motter

PNAS May 25, 2021 118 (21) e2024299118

Synchronization among interacting entities is a process that underlies the function of numerous systems, including circadian clocks and laser arrays. It is generally believed that homogeneity among the entities is beneficial for synchronization. This work shows theoretically, numerically, and experimentally that the opposite is not only possible but also common in systems with interaction delays. In such systems, heterogeneity among the entities is shown to promote synchronization, even when the heterogeneity is completely random. This finding advances our understanding of the interplay between order and disorder in the collective behavior of complex systems. We suggest that the phenomenon can be observed for diverse coupling schemes and has implications for real-world systems, where heterogeneity and delays are common and often unavoidable.

Read the full article at: www.pnas.org

No comment yet.
Scooped by Complexity Digest
Scoop.it!

CIMAX: collective information maximization in robotic swarms using local communication

Hannes Hornischer, Joshua Cherian Varughese, Ronald Thenius, Franz Wotawa, Manfred Füllsack, Thomas Schmickl

Robotic swarms and mobile sensor networks are used for environmental monitoring in various domains and areas of operation. Especially in otherwise inaccessible environments, decentralized robotic swarms can be advantageous due to their high spatial resolution of measurements and resilience to failure of individuals in the swarm. However, such robotic swarms might need to be able to compensate misplacement during deployment or adapt to dynamical changes in the environment. Reaching a collective decision in a swarm with limited communication abilities without a central entity serving as decision-maker can be a challenging task. Here, we present the CIMAX algorithm for collective decision-making for maximizing the information gathered by the swarm as a whole. Agents negotiate based on their individual sensor readings and ultimately make a decision for collectively moving in a particular direction so that the swarm as a whole increases the amount of relevant measurements and thus accessible information. We use both simulation and real robotic experiments for presenting, testing, and validating our algorithm. CIMAX is designed to be used in underwater swarm robots for troubleshooting an oxygen depletion phenomenon known as “anoxia.”

Read the full article at: journals.sagepub.com

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Modern theories of human evolution foreshadowed by Darwin’s Descent of Man

Peter J. Richerson, Sergey Gavrilets, Frans B. M. de Waal
Science 21 May 2021:
Vol. 372, Issue 6544, eaba3776
Charles Darwin’s The Descent of Man, published 150 years ago, laid the grounds for scientific studies into human origins and evolution. Three of his insights have been reinforced by modern science. The first is that we share many characteristics (genetic, developmental, physiological, morphological, cognitive, and psychological) with our closest relatives, the anthropoid apes. The second is that humans have a talent for high-level cooperation reinforced by morality and social norms. The third is that we have greatly expanded the social learning capacity that we see already in other primates. Darwin’s emphasis on the role of culture deserves special attention because during an increasingly unstable Pleistocene environment, cultural accumulation allowed changes in life history; increased cognition; and the appearance of language, social norms, and institutions.

Read the full article at: science.sciencemag.org

No comment yet.