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Lethal weapons and the evolution of civilisation

IT'S about 2 metres long, made of tough spruce wood and carved into a sharp point at one end. The widest part, and hence its centre of gravity, is in the front third, suggesting it was thrown like a javelin. At 400,000 years old, this is the world's oldest spear. And, according to a provocative theory, on its carved length rests nothing less than the foundation of human civilisation as we know it, including democracy, class divisions and the modern nation state.

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Mixing and diffusion in a two-type population

The outbreak of epidemics, the rise of religious radicalization or the motivational influence of fellow students in classrooms are some of the issues that can be described as diffusion processes in heterogeneous groups. Understanding the role that interaction patterns between groups (e.g. homophily or segregation) play in the diffusion of certain traits or behaviours is a major challenge for contemporary societies. Here, we study the impact on diffusion processes of mixing (or, alternatively, segregating) two groups that present different sensitivities or propensities to contagion. We find non-monotonic effects of mixing and inefficient segregation levels, i.e. situations where a change in the mixing level can benefit both groups, e.g. where an increase in the mixing level can reduce the expected contagion levels in both groups. These findings can have fundamental consequences for the design of inclusion policies.

 

Mixing and diffusion in a two-type population
Segismundo S. Izquierdo, Luis R. Izquierdo, Dunia López-Pintado
Royal Society Open Science

Published 21 February 2018.DOI: 10.1098/rsos.172102

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Link transmission centrality in large-scale social networks

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.

 

Link transmission centrality in large-scale social networks
Qian Zhang, Márton Karsai, Alessandro Vespignani

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Entanglement, symmetry breaking and collapse: correspondences between quantum and self-organizing dynamics

Quantum phenomena are notoriously difficult to grasp. The present paper first reviews the most important quantum concepts in a non-technical matter: superposition, uncertainty, collapse of the wave function, entanglement and non-locality. It then tries to clarify these concepts by examining their analogues in complex, self-organizing systems. These include bifurcations, attractors, emergent constraints, order parameters and non-local correlations. They are illustrated with concrete examples that include Rayleigh-Bénard convection, social self-organization and Gestalt perception of ambiguous figures. In both cases, quantum and self-organizing, the core process appears to be a symmetry breaking that irreversibly and unpredictably "collapses" an ambiguous state into one of a number of initially equivalent "eigenstates" or "attractors". Some speculations are proposed about the non-linear amplification of quantum fluctuations of the vacuum being ultimately responsible for such symmetry breaking.

 

Entanglement, symmetry breaking and collapse: correspondences between quantum and self-organizing dynamics

Francis Heylighen

ECCO Working paper, 2018-03, draft submitted for: Foundations of Science

http://134.184.131.111/Papers/QM-Complexity.pdf

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Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels

The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

 

Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels
Chaos 27, 126601 (2017); https://doi.org/10.1063/1.5018728
Gregory S. Duane, Carsten Grabow, Frank Selten, and Michael Ghil

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Surprise finding points to DNA's role in shaping cells

Surprise finding points to DNA's role in shaping cells | Papers | Scoop.it

As a basic unit of life, the cell is one of the most carefully studied components of all living organisms. Yet details on basic processes such as how cells are shaped have remained a mystery. Working at the intersection of biology and physics, scientists at the University of California San Diego have made an unexpected discovery at the root of cell formation.


Via Integrated DNA Technologies
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A Review of Future and Ethical Perspectives of Robotics and AI

In recent years, there has been increased attention on the possible impact of future robotics and AI systems. Prominent thinkers have publicly warned about the risk of a dystopian future when the complexity of these systems progresses further. These warnings stand in contrast to the current state-of-the-art of the robotics and AI technology. This article reviews work considering both the future potential of robotics and AI systems, and ethical considerations that need to be taken in order to avoid a dystopian future. References to recent initiatives to outline ethical guidelines for both the design of systems and how they should operate are included.

 

A Review of Future and Ethical Perspectives of Robotics and AI

Jim Torresen

Front. Robot. AI, 15 January 2018 | https://doi.org/10.3389/frobt.2017.00075

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Generating clustered scale-free networks using Poisson based localization of edges

Generating clustered scale-free networks using Poisson based localization of edges | Papers | Scoop.it
  • We introduce a variety of methods for generating clustered scale-free networks.
  • The Watts-Strogatz model is verified by reverse way.
  • Connections to spatially closer neighbors are promoted by Poisson based rewiring.
  • Barabasi-Albert model is rewired to have more local connections.
  • Growing clustered scale-free models with simple but efficient approaches are introduced.

 

Generating clustered scale-free networks using Poisson based localization of edges
İlker Türker

Physica A: Statistical Mechanics and its Applications

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Social Mixing and Home–Work Carpooling

Social Mixing and Home–Work Carpooling | Papers | Scoop.it
Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, the authors analyze the benefits of carpooling from a new angle, posing it as a possible means for favoring social integration in the city by matching carpoolers taking into account some of their social characteristics. Building upon a recently introduced network-based approach to model ride-sharing opportunities, the authors define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, the authors provide corresponding optimal and computationally efficient solutions. The authors then demonstrate their approach on two data sets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.
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Dynamic patterns of information flow in complex networks

Dynamic patterns of information flow in complex networks | Papers | Scoop.it

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit fundamentally different flow patterns, driven by different interaction mechanisms. To uncover a network’s actual flow patterns, here we use a perturbative formalism, analytically tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic information flow for a broad range of nonlinear systems. We find that the diversity of flow patterns can be mapped into a single universal function, characterizing the interplay between the system’s topology and its dynamics, ultimately allowing us to identify the network’s main arteries of information flow. Counter-intuitively, our formalism predicts a family of frequently encountered dynamics where the flow of information avoids the hubs, favoring the network’s peripheral pathways, a striking disparity between structure and dynamics.

 

Dynamic patterns of information flow in complex networks
Uzi Harush & Baruch Barzel
Nature Communicationsvolume 8, Article number: 2181 (2017)
doi:10.1038/s41467-017-01916-3

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ALife and Society: Editorial Introduction to the Artificial Life Conference 2016 Special Issue

ALife and Society: Editorial Introduction to the Artificial Life Conference 2016 Special Issue | Papers | Scoop.it

Artificial life (ALife) research is not only about the production of knowledge, but is also a source of compelling and meaningful stories and narratives, especially now when they are needed most. Such power, so to speak, emerges from its own foundations as a discipline. It was Chris Langton in 1987 who said that “By extending the horizons of empirical research in biology beyond the territory currently circumscribed by life-as-we-know-it, the study of Artificial Life gives us access to the do- main of life-as-it-could-be]. The very notion of life-as-it-could-be opened up many possibilities to explore, and released the study of life from its material and our cognitive constraints. The study of life did not have to be limited to carbon-based entities, DNA or proteins. It could also be about general and universal processes that could be implemented and realized in multiple forms. Moreover, while ALife was about biology at the beginning, its rationale and methods are now shared by many other domains, including chemistry, engineering, and the social sciences. In other words, the power to envision and synthesize “what is possible” beyond “what is” has transcended disciplinary boundaries. It also produces the material for the exploration of narratives about how things can be in principle and not only about their current state of being.

 

The Artificial Life Conference 2016 was dedicated to the special theme of ALife and Society. The guiding question for the conference was How can the synthetic study of living systems contribute to societies: scientifically, technically, and culturally?

 

Siqueiros-García, J. M., Froese, T., Gershenson, C., Aguilar, W., Sayama, H., and Izquierdo, E. (2018). ALife and society: Editorial introduction to the Artificial Life Conference 2016 Special Issue. Artificial Life, Early Access

Complexity Digest's insight:

See Also: Special Issue articles on Early Access: https://www.mitpressjournals.org/toc/artl/0/0 

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Why hiring the ‘best’ people produces the least creative results – Scott E Page

Why hiring the ‘best’ people produces the least creative results – Scott E Page | Papers | Scoop.it
Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. And when biases creep in, it results in people who look like those making the decisions. That’s not likely to lead to breakthroughs. As Astro Teller, CEO of X, the ‘moonshoot factory’ at Alphabet, Google’s parent company, has said: ‘Having people who have different mental perspectives is what’s important. If you want to explore things you haven’t explored, having people who look just like you and think just like you is not the best way.’ We must see the forest.
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Temporal patterns behind the strength of persistent ties

Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.

 

Temporal patterns behind the strength of persistent ties
Henry Navarro, Giovanna Miritello, Arturo Canales and Esteban Moro
EPJ Data Science 2017 6:31
https://doi.org/10.1140/epjds/s13688-017-0127-3

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How often does the best team win? A unified approach to understanding randomness in North American sport

Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport comparisons has been impeded by the difficulty of translating luck from one sport to another. In this manuscript, we develop Bayesian state-space models using betting market data that can be uniformly applied across sporting organizations to better understand the role of randomness in game outcomes. These models can be used to extract estimates of team strength, the between-season, within-season, and game-to-game variability of team strengths, as well each team's home advantage. We implement our approach across a decade of play in each of the National Football League (NFL), National Hockey League (NHL), National Basketball Association (NBA), and Major League Baseball (MLB), finding that the NBA demonstrates both the largest dispersion in talent and the largest home advantage, while the NHL and MLB stand out for their relative randomness in game outcomes. We conclude by proposing new metrics for judging competitiveness across sports leagues, both within the regular season and using traditional postseason tournament formats. Although we focus on sports, we discuss a number of other situations in which our generalizable models might be usefully applied.

 

How often does the best team win? A unified approach to understanding randomness in North American sport
Michael J. Lopez, Gregory J. Matthews, Benjamin S. Baumer

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Rank dynamics of word usage at multiple scales

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

 

Rank dynamics of word usage at multiple scales
José A. Morales, Ewan Colman, Sergio Sánchez, Fernanda Sánchez-Puig, Carlos Pineda, Gerardo Iñiguez, Germinal Cocho, Jorge Flores, Carlos Gershenson

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Robustness and Contingent History: From Prisoner's Dilemma to Gaia Theory

In both social systems and ecosystems there is a need to resolve potential conflicts between the interests of individuals and the collective interest of the community. The collective interests need to survive the turbulent dynamics of social and ecological interactions. To see how different systems with different sets of interactions have different degrees of robustness, we need to look at their different contingent histories. We analyze abstract artificial life models of such systems, and note that some prominent examples rely on explicitly ahistorical frameworks; we point out where analyses that ignore a contingent historical context can be fatally flawed. The mathematical foundations of Gaia theory are presented in a form whose very basic and general assumptions point to wide applicability across complex dynamical systems. This highlights surprising connections between robustness and accumulated contingent happenstance, regardless of whether Darwinian evolution is or is not implicated. Real-life studies highlight the role of history, and artificial life studies should do likewise.

 

Robustness and Contingent History: From Prisoner's Dilemma to Gaia Theory

Inman Harvey

Artificial Life
Volume 24 | Issue 1 | Winter 2018
p.29-48

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Multi-Armed Droplets as Shape-Changing Protocells

Protocells are objects that mimic one or several functions of biological cells and may be embodied as solid particles, lipid vesicles, or droplets. Our work is based on using decanol droplets in an aqueous solution of sodium decanoate in the presence of salt. A decanol droplet under such conditions bears many qualitative similarities with living cells, such as the ability to move chemotactically, divide and fuse, or change its shape. This article focuses on the description of a shape-changing process induced by the evaporation of water from the decanoate solution. Under these conditions, the droplets perform complex shape changes, whereby the originally round decanol droplets grow into branching patterns and mimic the growth of appendages in bacteria or axon growth of neuronal cells. We report two outcomes: (i) the morphological changes are reversible, and (ii) multiple protocells avoid contact between each other during the morphological transformation. The importance of these morphological changes in the context of artificial life are discussed.

 

Multi-Armed Droplets as Shape-Changing Protocells

Jitka Čejková, Martin M. Hanczyc and František Štěpánek

Artificial Life
Volume 24 | Issue 1 | Winter 2018
p.71-79

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Undecidability and Irreducibility Conditions for Open-Ended Evolution and Emergence

Is undecidability a requirement for open-ended evolution (OEE)? Using methods derived from algorithmic complexity theory, we propose robust computational definitions of open-ended evolution and the adaptability of computable dynamical systems. Within this framework, we show that decidability imposes absolute limits on the stable growth of complexity in computable dynamical systems. Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems. Complexity is assessed in terms of three measures: sophistication, coarse sophistication, and busy beaver logical depth. These three complexity measures assign low complexity values to random (incompressible) objects. As time grows, the stated complexity measures allow for the existence of complex states during the evolution of a computable dynamical system. We show, however, that finding these states involves undecidable computations. We conjecture that for similar complexity measures that assign low complexity values, decidability imposes comparable limits on the stable growth of complexity, and that such behavior is necessary for nontrivial evolutionary systems. We show that the undecidability of adapted states imposes novel and unpredictable behavior on the individuals or populations being modeled. Such behavior is irreducible. Finally, we offer an example of a system, first proposed by Chaitin, that exhibits strong OEE.

 

Undecidability and Irreducibility Conditions for Open-Ended Evolution and Emergence

Santiago Hernández-Orozco, Francisco Hernández-Quiroz and Hector Zenil

Artificial Life
Volume 24 | Issue 1 | Winter 2018
p.56-70

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Evolving Ecosystems: Inheritance and Selection in the Light of the Microbiome

Evolving Ecosystems: Inheritance and Selection in the Light of the Microbiome | Papers | Scoop.it

The importance of microorganisms in human biology is undeniable. The amount of research that supports that microbes have a fundamental role in animal and plant physiology is substantial and increasing every year. Even though we are only beginning to comprehend the broadness and complexity of microbial communities, evolutionary theories need to be recast in the light of such discoveries to fully understand and incorporate the role of microbes in our evolution. Fundamental evolutionary concepts such as diversity, heredity, selection, speciation, etc., which constitute the modern synthesis, are now being challenged, or at least expanded, by the emerging notion of the holobiont, which defines the genetic and metabolic networks of the host and its microbes as a single evolutionary unit. Several concepts originally developed to study ecosystems, can be used to understand the physiology and evolution of such complex systems that constitute “individuals.” In this review, we discuss these ecological concepts and also provide examples that range from squids, insects and koalas to other mammals and humans, suggesting that microorganisms have a fundamental role not only in physiology but also in evolution. Current evolutionary theories need to take into account the dynamics and interconnectedness of the host-microbiome network, as animals and plants not only owe their symbiogenetic origin to microbes, but also share a long evolutionary history together.

 

Evolving Ecosystems: Inheritance and Selection in the Light of the Microbiome

Santiago Sandoval-Motta, Maximino Aldana, Alejandro Frank

Archives of Medical Research

https://doi.org/10.1016/j.arcmed.2018.01.002

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An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes.

 

J.T. Lizier, M.S. Harré, M. Mitchell, S. DeDeo, C. Finn, K. Lindgren, A.L. Lizier, H. Sayama

"An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education"

arXiv:1802.02707, 2018

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Editorial: Metastable Dynamics of Neural Ensembles

A classical view of neural computation is that it can be characterized in terms of convergence to fixed-point-type attractor states (representing for instance memory patterns in Hopfield, 1982) or limit-cycle-like sequential transitions among states (mapping e.g., motor or syntactical sequences in Elman, 1990). After over three decades, is this still a valid model of how brain dynamics implements cognition? The idea that neuro-computational dynamics is mainly deterministically driven by convergence to emergent stable states in a synaptic/network noisy background has been lively debated, and recently challenged both empirically and by computational work. This question touches on the very basics of our understanding of neural computation; and hence it is one of the most exciting topics currently in systems and computational neuroscience.

This e-book comprises a comprehensive collection of recent theoretical and experimental contributions addressing the question of stable versus transient neural population dynamics, and its implications for the observed variability in neural activity, from diverse, complementary angles.

 

Editorial: Metastable Dynamics of Neural Ensembles
Emili Balaguer-Ballester, Ruben Moreno-Bote, Gustavo Deco, and Daniel Durstewitz

Front. Syst. Neurosci., 26 January 2018 | https://doi.org/10.3389/fnsys.2017.00099

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Dynamic Structure of Competition Networks in Affordable Care Act Insurance Market

 

"Stimulating competition is one of the main topics in most health care reform debates, and it has been a central issue in the Affordable Care Act in the United States since 2009. The goal of this paper is to use complex network methods to study dynamic and structure of competition under Affordable Care Act (ACA) and its evolution over time since its beginning until 2017. Using publicly available data, we construct a bipartite network of counties and insurance providers, create associated weighted single-mode networks, and analyze the evolution of network parameters that are related to competition and potential collusion in complex networks. These parameters have been previously tied to dynamics of collaboration and competition in earlier theoretical works. We argue that three parameters, namely network modularity, and eigenvector centrality mean and skewness are appropriate indicators of the overall competition in the insurance market. Based on these parameters, we show that the level of systemic competition among insurers as a function of time is an inverse U-shape trend, and that competition has returned back to what it was at the very beginning of ACA, indicating an undesirable resilience in the national health care system."

 

Dynamic Structure of Competition Networks in Affordable Care Act Insurance Market

David A Gianetto;  Mohsen Mosleh;  Babak Heydari

IEEE Access

DOI: 10.1109/ACCESS.2018.2800659

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Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

Interaction patterns in human communication networks are characterized by intermittency and unpredictable timing (burstiness). Simulated spreading dynamics through such networks are slower than expected. A technology for automated recording of social interactions of individual honeybees, developed by the authors, enables one to study these two phenomena in a nonhuman society. Specifically, by analyzing more than 1.2 million bee social interactions, we demonstrate that burstiness is not a human-specific interaction pattern. We furthermore show that spreading dynamics on bee social networks are faster than expected, confirming earlier theoretical predictions that burstiness and fast spreading can co-occur. We expect that these findings will inform future models of large-scale social organization, spread of disease, and information transmission.

 

Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks
Tim Gernat, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld and Gene E. Robinson
PNAS 2018; published ahead of print January 29, 2018, https://doi.org/10.1073/pnas.1713568115

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Psychological targeting as an effective approach to digital mass persuasion

Building on recent advancements in the assessment of psychological traits from digital footprints, this paper demonstrates the effectiveness of psychological mass persuasion—that is, the adaptation of persuasive appeals to the psychological characteristics of large groups of individuals with the goal of influencing their behavior. On the one hand, this form of psychological mass persuasion could be used to help people make better decisions and lead healthier and happier lives. On the other hand, it could be used to covertly exploit weaknesses in their character and persuade them to take action against their own best interest, highlighting the potential need for policy interventions.

 

Psychological targeting as an effective approach to digital mass persuasion
S. C. Matz, M. Kosinski, G. Nave and D. J. Stillwell
PNAS 2017 November, 114 (48) 12714-12719. https://doi.org/10.1073/pnas.1710966114

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Nonequilibrium entropic bounds for Darwinian replicators

Life evolved on our planet by means of a combination of Darwinian selection and innovations leading to higher levels of complexity. The emergence and selection of replicating entities is a central problem in prebiotic evolution. Theoretical models have shown how populations of different types of replicating entities exclude or coexist with other classes of replicators. Models are typically kinetic, based on standard replicator equations. On the other hand, the presence of thermodynamical constrains for these systems remain an open question. This is largely due to the lack of a general theory of out of statistical methods for systems far from equilibrium. Nonetheless, a first approach to this problem has been put forward in a series of novel developements in non-equilibrium physics, under the rubric of the extended second law of thermodynamics. The work presented here is twofold: firstly, we review this theoretical framework and provide a brief description of the three fundamental replicator types in prebiotic evolution: parabolic, malthusian and hyperbolic. Finally, we employ these previously mentioned techinques to explore how replicators are constrained by thermodynamics.

 

Nonequilibrium entropic bounds for Darwinian replicators
Jordi Pinero, Ricard Sole
doi: https://doi.org/10.1101/225011

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The human race has peaked

The human race has peaked | Papers | Scoop.it
Humans may have reached their maximum limits for height, lifespan and physical performance. A recent review suggests humans have biological limitations, and that anthropogenic impacts on the environment — including climate change — could have a deleterious effect on these limits. Published in Frontiers in Physiology, this review is the first of its kind spanning 120 years worth of historical information, while considering the effects of both genetic and environmental parameters.
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