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Information arms race explains plant-herbivore chemical communication in ecological communities

Complexity Digest - Fri, 06/19/2020 - 18:57

Pengjuan Zu, Karina Boege, Ek del-Val, Meredith C. Schuman, Philip C. Stevenson, Alejandro Zaldivar-Riverón, Serguei Saavedra

Science  19 Jun 2020:
Vol. 368, Issue 6497, pp. 1377-1381
DOI: 10.1126/science.aba2965

 

Plants emit an extraordinary diversity of chemicals that provide information about their identity and mediate their interactions with insects. However, most studies of this have focused on a few model species in controlled environments, limiting our capacity to understand plant-insect chemical communication in ecological communities. Here, by integrating information theory with ecological and evolutionary theories, we show that a stable information structure of plant volatile organic compounds (VOCs) can emerge from a conflicting information process between plants and herbivores. We corroborate this information “arms race” theory with field data recording plant-VOC associations and plant-herbivore interactions in a tropical dry forest. We reveal that plant VOC redundancy and herbivore specialization can be explained by a conflicting information transfer. Information-based communication approaches can increase our understanding of species interactions across trophic levels.

Source: science.sciencemag.org

Globalization and the rise and fall of cognitive control

Complexity Digest - Fri, 06/19/2020 - 15:15

Mohsen Mosleh, Katelynn Kyker, Jonathan D. Cohen & David G. Rand

Nature Communications volume 11, Article number: 3099 (2020)

 

The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive) versus controlled (e.g., deliberative) cognition, interact with each other, and influence the environment (i.e., game payoffs). We find that globalized direct contact between agents can either favor or disfavor control, depending on whether controlled agents are harmed or helped by contact with automatic agents; globalized environment disfavors cognitive control, while also promoting strategic diversity and fostering mesoscale communities of more versus less controlled agents; and globalized learning destroys mesoscale communities and homogenizes the population. These results emphasize the importance of the scale of interaction for the evolution of cognition, and help shed light on modern challenges. Humankind is in a period of unprecedented cognitive sophistication as well as globalization. Here, using an evolutionary game theory model, the authors reveal ways in which the transition from local to global interaction can have both positive and potentially negative consequences for the prevalence of cognitive sophistication in the population.

Source: www.nature.com

Flow-Mediated Olfactory Communication in Honey Bee Swarms

Complexity Digest - Fri, 06/19/2020 - 15:11

Dieu My T. Nguyen, Michael L. Iuzzolino, Aaron Mankel, Katarzyna Bozek, Greg J. Stephens, Orit Peleg

 

Honey bee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones, but how can distant individuals exploit these chemical signals which decay rapidly in space and time? Here, we combine a novel behavioral assay with the machine vision detection of organism location and scenting behavior to track the search and aggregation dynamics of the honey bee Apis mellifera L. We find that bees collectively create a communication network to propagate pheromone signals, by arranging in a specific spatial distribution where there is a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signals. To better understand such a flow–mediated directional communication strategy, we connect our experimental results to an agent–based model where virtual bees with simple, local behavioral rules, exist in a flow environment. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic communication, such as small bee clusters that persist throughout the simulation. Our results highlight a novel example of extended classical stigmergy: rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Source: www.biorxiv.org

Neuroscience needs some new ideas

Complexity Digest - Fri, 06/19/2020 - 13:10

The Idea of the Brain: A History. Matthew Cobb. Profile (2020)

 

The Idea of the Brain puts our current predicament in context and synthesizes much that needs attention. It is a very good book. It could have done more in a time when science is coming to terms with the limitations of the straight, white, wealthy, Western, non-disabled, male perspective. But I hope it provokes contemplation about why certain metaphors linger, where they come from, how they persist, and in what ways they burden us with the invisible assumptions of past cultures.

Source: www.nature.com

Planning within Complex Urban Systems – 1st Edition – Shih-Kung Lai –

Complexity Digest - Fri, 06/19/2020 - 12:16

Imagine living in a city where people could move freely and buildings could be replaced at minimal cost. Reality cannot be further from such. Despite this imperfect world in which we live, urban planning has become integral and critical especially in the face of rapid urbanization in many developing and developed countries. This book introduces the axiomatic/experimental approach to urban planning and addresses the criticism of the lack of a theoretical foundation in urban planning.

With the rise of the complexity movement, the book is timely in its depiction of cities as complex systems and explains why planning from within is useful in the face of urban complexity. It also includes policy implications for the Chinese cities in the context of axiomatic/experimental planning theory.

Source: www.routledge.com

Uncovering the internal structure of Boko Haram through its mobility patterns

Complexity Digest - Wed, 06/17/2020 - 13:40

Rafael Prieto Curiel, Olivier Walther & Neave O’Clery
Applied Network Science volume 5, Article number: 28 (2020)

 

Boko Haram has caused nearly 40,000 casualties in Nigeria, Niger, Cameroon and Chad, becoming one of the deadliest Jihadist organisations in recent history. At its current rate, Boko Haram takes part in more than two events each day, taking the lives of nearly 11 people daily. Yet, little is known concerning Boko Haram’s internal structure, organisation, and its mobility.

Here, we propose a novel technique to uncover the internal structure of Boko Haram based on the sequence of events in which the terrorist group takes part. Data from the Armed Conflict Location & Event Data Project (ACLED) gives the location and time of nearly 3,800 events in which Boko Haram has been involved since the organisation became violent 10 years ago. Using this dataset, we build an algorithm to detect the fragmentation of Boko Haram into multiple cells, assuming that travel costs and reduced familiarity with unknown locations limit the mobility of individual cells.

Our results suggest that the terrorist group has a very high level of fragmentation and consists of at least 50–60 separate cells. Our methodology enables us to detect periods of time during which Boko Haram exhibits exceptionally high levels of fragmentation, and identify a number of key routes frequently travelled by separate cells of Boko Haram where military interventions could be concentrated.

Source: appliednetsci.springeropen.com

Building the New Economy ·

Complexity Digest - Sun, 06/14/2020 - 08:31

Edited by Alex Pentland, Alexander Lipton, and Thomas Hardjono

With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and governments. Today’s pandemic, joined with the tsunami of data, crypto and AI technologies, is such a crisis. Consequently the critical question for today is: what sort institutions should we be creating both to help us past this crisis and to make us less vulnerable to the next crisis? This book lays out a vision of what we should build, covering not only how to reforge our societies’ social contract but also how institutions, systems, infrastructure, and law should change in support of this new order. We invite your comments and suggestions on both the ideas and the presentation, preferably by June 1, 2020 when we will move to make the book more widely available.

Source: wip.mitpress.mit.edu

The Exposome – 2nd Edition

Complexity Digest - Sat, 06/13/2020 - 08:29

Gary W. Miller

 

The Exposome: A New Paradigm for the Environment and Health, Second Edition, is a thoroughly expanded and updated edition of The Exposome: A Primer, the first book dedicated to the topic. This new release outlines the purpose and scope of this emerging field of study, its practical applications, and how it complements a broad range of disciplines. The book contains sections on -omics-based technologies, newer detection methods, managing and integrating exposome data (including maps, models, computation and systems biology), and more. Both students and scientists in toxicology, environmental health, epidemiology and public health will benefit from this rigorous, yet readable, overview.

This updated edition includes a more in-depth examination of the exposome, including full references, further reading and thought questions.

Source: www.elsevier.com

Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)

Complexity Digest - Thu, 06/11/2020 - 12:39

Jie Sun, Abd AlRahman AlMomani, Erik Bollt

 

Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field and in the finance industry. Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex. In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches. Building on the recently developed optimal causation entropy principle (oCSE), that we proved can correctly infer networks distinguishing between direct versus indirect connections, we develop here an efficient algorithm that furthermore infers a Boolean network (including both its structure and function) based on data observed from the evolving states at nodes. We call this new inference method, Boolean optimal causation entropy (BoCSE), which we will show that our method is both computationally efficient and also resilient to noise. Furthermore, it allows for selection of a set of features that best explains the process, a statement that can be described as a networked Boolean function reduced order model. We highlight our method to the feature selection in several real-world examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) risk causality analysis of loans in default status. Our proposed method is effective and efficient in all examples.

Source: arxiv.org

Uncovering the social interaction network in swarm intelligence algorithms

Complexity Digest - Wed, 06/10/2020 - 14:37

Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020)

 

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.

Source: appliednetsci.springeropen.com

Living models or life modelled? On the use of models in the free energy principle

Complexity Digest - Wed, 06/10/2020 - 11:20

Thomas van Es
Adaptive Behavior

 

The free energy principle (FEP) is an information-theoretic approach to living systems. FEP characterizes life by living systems’ resistance to the second law of thermodynamics: living systems do not randomly visit the possible states, but actively work to remain within a set of viable states. In FEP, this is modelled mathematically. Yet, the status of these models is typically unclear: are these models employed by organisms or strictly scientific tools of understanding? In this article, I argue for an instrumentalist take on models in FEP. I shall argue that models used as instruments for knowledge by scientists and models as implemented by organisms to navigate the world are being conflated, which leads to erroneous conclusions. I further argue that a realist position is unwarranted. First, it overgenerates models and thus trivializes the notion of modelling. Second, even when the mathematical mechanisms described by FEP are implemented in an organism, they do not constitute a model. They are covariational, not representational in nature, and precede the social practices that have shaped our scientific modelling practice. I finally argue that the above arguments do not affect the instrumentalist position. An instrumentalist approach can further add to conceptual clarity in the FEP literature.

Source: journals.sagepub.com

Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks

Complexity Digest - Wed, 06/10/2020 - 09:17

Masaaki Inoue, Thong Pham, Hidetoshi Shimodaira

Journal of Informetrics
Volume 14, Issue 3, August 2020, 101042

 

• Transitivity and preferential attachment exist jointly in two co-authorship networks.

• Neither alone could describe the networks well.

• Their functional forms deviate substantially from the conventional power-law form.

• Transitivity greatly dominated preferential attachment in both networks.

Source: www.sciencedirect.com

Networks beyond pairwise interactions: structure and dynamics

Complexity Digest - Sun, 06/07/2020 - 12:02

Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri

 

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a great variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, in face-to-face human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes and cannot be simply described just in terms of simple dyads. Until recently, little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can greatly enhance our modeling capacities and help us to understand and predict their emerging dynamical behaviors. Here, we present a complete overview of the emerging field of networks beyond pairwise interactions. We first discuss the methods to represent higher-order interactions and give a unified presentation of the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed in the literature to generate synthetic structures, such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We introduce and discuss the rapidly growing research on higher-order dynamical systems and on dynamical topology. We focus on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond pairwise interactions. We elucidate the relations between higher-order topology and dynamical properties, and conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers.

Source: arxiv.org

Why Sleep Deprivation Kills

Complexity Digest - Sat, 06/06/2020 - 09:48

Going without sleep for too long kills animals but scientists haven’t known why. Newly published work suggests that the answer lies in an unexpected part of the body.

Source: www.quantamagazine.org

Universal evolution patterns of degree assortativity in social networks

Complexity Digest - Fri, 06/05/2020 - 14:45

Bin Zhou, Xin Lu, Petter Holme

Social Networks
Volume 63, October 2020, Pages 47-55

 

• A universal rise-and-fall pattern for assortativity is found in empirical networks
• The bidirectional selection model can re-construct the evolution of assortativity
• Heterogeneity of social status may drive the network evolution towards self-optimization
• The social status gap plays an important role for the evolution of network assortativity

Source: www.sciencedirect.com

On Assessing Control Actions for Epidemic Models on Temporal Networks

Complexity Digest - Thu, 06/04/2020 - 18:49

Lorenzo Zino ; Alessandro Rizzo ; Maurizio Porfiri

IEEE Control Systems Letters 4(4)

 

In this letter, we propose an epidemic model over temporal networks that explicitly encapsulates two different control actions. We develop our model within the theoretical framework of activity driven networks (ADNs), which have emerged as a valuable tool to capture the complexity of dynamical processes on networks, coevolving at a comparable time scale to the temporal network formation. Specifically, we complement a susceptible–infected–susceptible epidemic model with features that are typical of nonpharmaceutical interventions in public health policies: i) actions to promote awareness, which induce people to adopt self-protective behaviors, and ii) confinement policies to reduce the social activity of infected individuals. In the thermodynamic limit of large-scale populations, we use a mean-field approach to analytically derive the epidemic threshold, which offers viable insight to devise containment actions at the early stages of the outbreak. Through the proposed model, it is possible to devise an optimal epidemic control policy as the combination of the two strategies, arising from the solution of an optimization problem. Finally, the analytical computation of the epidemic prevalence in endemic diseases on homogeneous ADNs is used to optimally calibrate control actions toward mitigating an endemic disease. Simulations are provided to support our theoretical results.

 

Source: ieeexplore.ieee.org

Cities & Covid-19 Project | TAU Research Center for Cities and Urbanism | Tel Aviv University

Complexity Digest - Thu, 06/04/2020 - 17:02

During the transition from the year 2019 to the year 2020, the world was introduced to a new virus that commenced affecting its cities and the people residing in them.

March 11th 2020 marked the day that the WHO declared the world is coping with a pandemic.

Covid-19, the virus causing this pandemic, has spread extensively from Wuhan, China to cities like New York, Madrid, Moscow and Bergamo.

Cities are complex systems[1], and the entrance of an uninvited virus adds to their unpredictable, dynamic nature.

Some cities were put under lockdown, restricting the movement and economic activities of their citizens, while others did not wish interfering with the natural flow of urban life, imposing minimal limitations.

While cities around the world are adapting to the life alongside Covid-19, this unusual situation creates a fertile ground for contemplation about urban living during a pandemic and its aftermath.

TAU City Center invites students across the globe to share their thoughts and impressions on how their city has been affected by and coped with Covid-19.

This Project aims to display different perspectives reagrding urban living during the Covid-19 pandemic.

Source: en-urban.tau.ac.il

Networked Complexity: The Case of COVID-19. June 8-11, 2020

Complexity Digest - Wed, 06/03/2020 - 11:53

Close monitoring of the COVID-19 pandemic provides a blow by blow account of a spatio-temporal process percolating over complex (social)-networks. Efforts to contain the spread of the disease were and remain, for better or worse, explicitly informed by a rich tradition of mathematical models of such processes. This tradition was further enriched in the past couple of decades with the emergence of globally networked virtual societies, and the deployment of fine grained networks of sensors, both enabling the gathering of highly resolved data on the structure of complex networks, and flows over them.

Our online-conference is an occasion for expert reviews of this tradition, then presentations of work-in-progress on the gathering of epidemiological data (technical and ethical challenges), and its modeling (from the coarse grained compartmental, to the fine grained agent based models), with the urgency of COVID-19 mitigation in the air.

Taking place as it does at a cusp in a global pandemic, the meeting is for us at CAMS a timely intervention in a collaboration with the National Center for Remote Sensing (NCRS, CNRS-L) the principle aim of which is to harness big data analytics and complexity theory at the service of national and regional priorities. It draws on local expertise in concerned disciplines (in this case: physics, biology, epidemiology and sociology), and contributions by experts at leading international laboratories in data analytics, and complexity science (e.g. Multiscale and Quantum Physics, Aalto University, Finland; The Bartlett Center for Advanced Spatial Analysis, UCL, London; Center of Complexity Sciences (C3), UNAM, Mexico; The Alan Turing Institute, London; ICTP, Trieste, Italy; etc.).

Source: www.aub.edu.lb

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