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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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Opinion Dynamics and Collective Decisions

We expect that democracy enables us to utilize collective intelligence such that our collective decisions build and enhance social welfare, and such that we accept their distributive and normative consequences. Collective decisions are produced by voting procedures which aggregate individual preferences and judgments. Before and after, individual preferences and judgments change as their underlying attitudes, values, and opinions change through discussion and deliberation. In large groups, these dynamics naturally go beyond the scope of the individual and consequently might show unexpected self-driven macroscopic systems dynamics following socio-physical laws. On the other hand, aggregated information and preferences as communicated through media, polls, political parties, or interest groups, also play a large role in the individual opinion formation process. Further on, actors are also capable of strategic opinion formation in the light of a pending referendum, election or other collective decision. Opinion dynamics and collective decision should thus not only be tackled by social choice, game theory, political and social psychology, but also from a systems dynamics and sociophysics perspective.

 

Advances in Complex SystemsVol. 21, No. 06n07, 1802002 (2018) Full Access
OPINION DYNAMICS AND COLLECTIVE DECISIONS
JAN LORENZ and MARTIN NEUMANN
https://doi.org/10.1142/S0219525918020022

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Where Does a Shark’s Skin Get Its Pattern?

Where Does a Shark’s Skin Get Its Pattern? | Papers | Scoop.it

In 1952, well before developmental biologists spoke in terms of Hoxgenes and transcription factors, or even understood DNA’s structure, Alan Turing had an idea. The famed mathematician who hastened the end of World War II by cracking the Enigma code turned his mind to the natural world and devised an elegant mathematical model of pattern formation. His theory outlined how endless varieties of stripes, spots, and scales could emerge from the interaction of two simple, hypothetical chemical agents, or “morphogens.”

Decades passed before biologists seriously considered that this mathematical theory could in fact explain myriad biological patterns. The development of mammalian hair, the feathers of birds, and even those ridges on the roof of your mouth all stem from Turing-like mechanisms.

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Causal deconvolution by algorithmic generative models

Causal deconvolution by algorithmic generative models | Papers | Scoop.it

New paper in Nature Machine Intelligence and a video produced by Nature shows how small programs can help deconvolve signals and data: https://www.nature.com/articles/s42256-018-0005-0 and https://www.youtube.com/watch?v=rkmz7DAA-t8

 

"Most machine learning approaches extract statistical features from data, rather than the underlying causal mechanisms. A different approach analyses information in a general way by extracting recursive patterns from data using generative models under the paradigm of computability and algorithmic information theory.

 

Complex behaviour emerges from interactions between objects produced by different generating mechanisms. Yet to decode their causal origin(s) from observations remains one of the most fundamental challenges in science. This paper introduces a universal, unsupervised and parameter-free model-oriented approach, based on the seminal concept and the first principles of algorithmic probability, to decompose an observation into its most likely algorithmic generative models."

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Forecasting innovations in science, technology, and education

Forecasting innovations in science, technology, and education | Papers | Scoop.it

Human survival depends on our ability to predict future outcomes so that we can make informed decisions. Human cognition and perception are optimized for local, short-term decision-making, such as deciding when to fight or flight, whom to mate, or what to eat. For more elaborate decisions (e.g., when to harvest, when to go to war or not, and whom to marry), people used to consult oracles—prophetic predictions of the future inspired by the gods. Over time, oracles were replaced by models of the structure and dynamics of natural, technological, and social systems. In the 21st century, computational models and visualizations of model results inform much of our decision-making: near real-time weather forecasts help us decide when to take an umbrella, plant, or harvest; where to ground airplanes; or when to evacuate inhabitants in the path of a hurricane, tornado, or flood. Long-term weather and climate forecasts predict a future with increasing torrential rains, stronger winds, and more frequent drought, landslides, and forest fires as well as rising sea levels, enabling decision makers to prepare for these changes by building dikes, moving cities and roads, and building larger water reservoirs and better storm sewers.

 

Forecasting innovations in science, technology, and education
Katy Börner, William B. Rouse, Paul Trunfio, and H. Eugene Stanley
PNAS December 11, 2018 115 (50) 12573-12581; published ahead of print December 11, 2018 https://doi.org/10.1073/pnas.1818750115

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The strength of long-range ties in population-scale social networks

Long-range connections that span large social networks are widely assumed to be weak, composed of sporadic and emotionally distant relationships. However, researchers historically have lacked the population-scale network data needed to verify the predicted weakness. Using data from 11 culturally diverse population-scale networks on four continents—encompassing 56 million Twitter users and 58 million mobile phone subscribers—we find that long-range ties are nearly as strong as social ties embedded within a small circle of friends. These high-bandwidth connections have important implications for diffusion and social integration.

 

The strength of long-range ties in population-scale social networks
Patrick S. Park, Joshua E. Blumenstock, Michael W. Macy
Science  21 Dec 2018:
Vol. 362, Issue 6421, pp. 1410-1413
DOI: 10.1126/science.aau9735

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Cohesion, order and information flow in the collective motion of mixed-species shoals

Despite the frequency with which mixed-species groups are observed in nature, studies of collective behaviour typically focus on single-species groups. Here, we quantify and compare the patterns of interactions between three fish species, threespine sticklebacks (Gasterosteus aculeatus), ninespine sticklebacks (Pungitius pungitius) and roach (Rutilus rutilus) in both single- and mixed-species shoals in the laboratory. Pilot data confirmed that the three species form both single- and mixed-species shoals in the wild. In our laboratory study, we found that single-species groups were more polarized than mixed-species groups, while single-species groups of threespine sticklebacks and roach were more cohesive than mixed shoals of these species. Furthermore, while there was no difference between the inter-individual distances between threespine and ninespine sticklebacks within mixed-species groups, there was some evidence of segregation by species in mixed groups of threespine sticklebacks and roach. There were differences between treatments in mean pairwise transfer entropy, and in particular we identify species-differences in information use within the mixed-species groups, and, similarly, differences in responses to conspecifics and heterospecifics in mixed-species groups. We speculate that differences in the patterns of interactions between species in mixed-species groups may determine patterns of fission and fusion in such groups.

 

Cohesion, order and information flow in the collective motion of mixed-species shoals
Ashley J. W. Ward , T. M. Schaerf , A. L. J. Burns , J. T. Lizier , E. Crosato , M. Prokopenko and M. M. Webster

Royal Society Open Science

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Antifragility of Random Boolean Networks

Antifragility is a property that enhances the capability of a system in response to external perturbations. Although the concept has been applied in many areas, a practical measure of antifragility has not been developed yet. Here we propose a simply calculable measure of antifragility, based on the change of "satisfaction" before and after adding perturbations, and apply it to random Boolean networks (RBNs). Using the measure, we found that ordered RBNs are the most antifragile. Also, we demonstrate that seven biological systems are antifragile. Our measure and results can be used in various applications of Boolean networks (BNs) including creating antifragile engineering systems, identifying the genetic mechanism of antifragile biological systems, and developing new treatment strategies for various diseases.

 

Antifragility of Random Boolean Networks
Omar K. Pineda, Hyobin Kim, Carlos Gershenson

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The chaperone effect in scientific publishing

Experience plays a critical role in crafting high-impact scientific work. This is particularly evident in top multidisciplinary journals, where a scientist is unlikely to appear as senior author if he or she has not previously published within the same journal. Here, we develop a quantitative understanding of author order by quantifying this “chaperone effect,” capturing how scientists transition into senior status within a particular publication venue. We illustrate that the chaperone effect has a different magnitude for journals in different branches of science, being more pronounced in medical and biological sciences and weaker in natural sciences. Finally, we show that in the case of high-impact venues, the chaperone effect has significant implications, specifically resulting in a higher average impact relative to papers authored by new principal investigators (PIs). Our findings shed light on the role played by experience in publishing within specific scientific journals, on the paths toward acquiring the necessary experience and expertise, and on the skills required to publish in prestigious venues.

 

The chaperone effect in scientific publishing
Vedran Sekara, Pierre Deville, Sebastian E. Ahnert, Albert-László Barabási, Roberta Sinatra, and Sune Lehmann
PNAS December 11, 2018 115 (50) 12603-12607

https://doi.org/10.1073/pnas.1800471115

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ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.

 


ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Mayank Bansal, Alex Krizhevsky, Abhijit Ogale

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The chaperone effect in scientific publishing

Experience plays a critical role in crafting high-impact scientific work. This is particularly evident in top multidisciplinary journals, where a scientist is unlikely to appear as senior author if he or she has not previously published within the same journal. Here, we develop a quantitative understanding of author order by quantifying this “chaperone effect,” capturing how scientists transition into senior status within a particular publication venue. We illustrate that the chaperone effect has a different magnitude for journals in different branches of science, being more pronounced in medical and biological sciences and weaker in natural sciences. Finally, we show that in the case of high-impact venues, the chaperone effect has significant implications, specifically resulting in a higher average impact relative to papers authored by new principal investigators (PIs). Our findings shed light on the role played by experience in publishing within specific scientific journals, on the paths toward acquiring the necessary experience and expertise, and on the skills required to publish in prestigious venues.

 

The chaperone effect in scientific publishing
Vedran Sekara, Pierre Deville, Sebastian E. Ahnert, Albert-László Barabási, Roberta Sinatra, and Sune Lehmann
PNAS published ahead of print December 10, 2018 https://doi.org/10.1073/pnas.1800471115

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Stability of democracies: a complex systems perspective

The idea that democracy is under threat, after being largely dormant for at least 40 years, is looming increasingly large in public discourse. Complex systems theory offers a range of powerful new tools to analyse the stability of social institutions in general, and democracy in particular. What makes a democracy stable? And which processes potentially lead to instability of a democratic system? This paper offers a complex systems perspective on this question, informed by areas of the mathematical, natural, and social sciences. We explain the meaning of the term 'stability' in different disciplines and discuss how laws, rules, and regulations, but also norms, conventions, and expectations are decisive for the stability of a social institution such as democracy.

 

Stability of democracies: a complex systems perspective
K Wiesner, A Birdi3, T Eliassi-Rad, H Farrell, D Garcia, S Lewandowsky, P Palacios, D Ross, D Sornette and K Thébault
European Journal of Physics, Volume 40, Number 1

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Beyond Calories: A Holistic Assessment of the Global Food System

The global food system is failing to meet nutritional needs, with growing concerns for health related to both under-, over-consumption and severe micronutrient deficiency. The 2nd Sustainable Development Goal (SDG2) targets the end of malnutrition in all forms by 2030. To address this challenge, the focus around food security and malnutrition must be broadened beyond the scope of sufficient energy intake to take full account of total nutrient supply and requirements. Here, for the first time, we have quantitatively mapped the global food system in terms of energy, protein, fat, essential amino acids, and micronutrients from “field-to-fork,” normalized to an equitable per capita availability metric. This framework allows for the evaluation of the sufficiency of nutrient supply, identifies the key hotspots within the global food supply chain which could be targeted for improved efficiency, and highlights the trade-offs which may arise in delivering a balanced nutritional system.

 

Beyond Calories: A Holistic Assessment of the Global Food System

Hannah Ritchie, David S. Reay and Peter Higgins

Front. Sustain. Food Syst.

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The Impact of Potential Crowd Behaviours on Emergency Evacuation

Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks via studying the impact of crowd behavioural evolution towards evacuation simulation could mitigate the possibility of crowd disasters that may happen. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction of agents within their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviours towards the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game theory notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents get increased. Besides that, based on our simulation results, we can infer that crowd disaster could be prevented if the agent population are full of risk-averse and risk-neutral agents despite circumstances that lead to threat consequences.

 

The Impact of Potential Crowd Behaviours on Emergency Evacuation: An Evolutionary Game Theoretic Approach
Azhar Mohd Ibrahim , Ibrahim Venkat and De Wilde Philippe
Journal of Artificial Societies and Social Simulation 22 (1) 3
<http://jasss.soc.surrey.ac.uk/22/1/3.html>
DOI: 10.18564/jasss.3837

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Taking census of physics

Taking census of physics | Papers | Scoop.it

Over the past decades, the diversity of areas explored by physicists has exploded, encompassing new topics from biophysics and chemical physics to network science. However, it is unclear how these new subfields emerged from the traditional subject areas and how physicists explore them. To map out the evolution of physics subfields, here, we take an intellectual census of physics by studying physicists’ careers. We use a large-scale publication data set, identify the subfields of 135,877 physicists and quantify their heterogeneous birth, growth and migration patterns among research areas. We find that the majority of physicists began their careers in only three subfields, branching out to other areas at later career stages, with different rates and transition times. Furthermore, we analyse the productivity, impact and team sizes across different subfields, finding drastic changes attributable to the recent rise in large-scale collaborations. This detailed, longitudinal census of physics can inform resource allocation policies and provide students, editors and scientists with a broader view of the field’s internal dynamics.

 

Taking census of physics
Federico Battiston, Federico Musciotto, Dashun Wang, Albert-László Barabási, Michael Szell & Roberta Sinatra 
Nature Reviews Physics volume 1, pages89–97 (2019)

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Pull out all the stops: Textual analysis via punctuation sequences

Whether enjoying the lucid prose of a favorite author or slogging through some other writer's cumbersome, heavy-set prattle (full of parentheses, em-dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in ``stylometry'', the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?

 

Pull out all the stops: Textual analysis via punctuation sequences
Alexandra N. M. Darmon Marya Bazzi Sam D. Howison Mason Porter

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Morphogenesis in robot swarms

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.

 

Morphogenesis in robot swarms
I. Slavkov, D. Carrillo-Zapata, N. Carranza, X. Diego, F. Jansson, J. Kaandorp, S. Hauert, and J. Sharpe

Science Robotics 19 Dec 2018:
Vol. 3, Issue 25, eaau9178
DOI: 10.1126/scirobotics.aau9178


Via june holley
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The role of industry-specific, occupation-specific, and location-specific knowledge in the growth and survival of new firms

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry-, occupation-, and location-specific knowledge carried by workers from one establishment to the next, using a dataset summarizing the individual work history for an entire country. We study pioneer firms—firms operating in an industry that was not present in a region—because the success of pioneers is the basic unit of regional economic diversification. We find that the growth and survival of pioneers increase significantly when their first hires are workers with experience in a related industry and with work experience in the same location, but not with past experience in a related occupation. We compare these results with new firms that are not pioneers and find that industry-specific knowledge is significantly more important for pioneer than for nonpioneer firms. To address endogeneity we use Bartik instruments, which leverage national fluctuations in the demand for an activity as shocks for local labor supply. The instrumental variable estimates support the finding that industry-specific knowledge is a predictor of the survival and growth of pioneer firms. These findings expand our understanding of the micromechanisms underlying regional economic diversification.

 

The role of industry-specific, occupation-specific, and location-specific knowledge in the growth and survival of new firms
C. Jara-Figueroa, Bogang Jun, Edward L. Glaeser, and Cesar A. Hidalgo
PNAS December 11, 2018 115 (50) 12646-12653; published ahead of print December 10, 2018 https://doi.org/10.1073/pnas.1800475115

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Modeling the Role of the Microbiome in Evolution

Modeling the Role of the Microbiome in Evolution | Papers | Scoop.it

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones.

 

Modeling the Role of the Microbiome in Evolution

Saúl Huitzil, Santiago Sandoval-Motta, Alejandro Frank and Maximino Aldana

Front. Physiol., 20 December 2018 | https://doi.org/10.3389/fphys.2018.01836 

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Connecting empirical phenomena and theoretical models of biological coordination across scales

Coordination is ubiquitous in living systems. Existing theoretical models of coordination -- from bacteria to brains -- focus on either gross statistics in large-scale systems (N) or detailed dynamics in small-scale systems (mostly N=2). Both approaches have proceeded largely independent of each other. The present work bridges this gap with a theoretical model of biological coordination that captures key experimental observations of mid-scale social coordination at multiple levels of description. It also reconciles in a single formulation two well-studied models of large- and small-scale biological coordination (Kuramoto and extended Haken-Kelso-Bunz). The model adds second-order coupling (from extended Haken-Kelso-Bunz) to the Kuramoto model. We show that second-order coupling is indispensable for reproducing empirically observed phenomena and gives rise to a phase transition from mono- to multi-stable coordination across scales. This mono-to-multistable transition connects the emergence and growth of behavioral complexity in small and large systems.

 

Connecting empirical phenomena and theoretical models of biological coordination across scales
Mengsen Zhang, Christopher Beetle, J. A. Scott Kelso, Emmanuelle Tognoli

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The universal decay of collective memory and attention

The universal decay of collective memory and attention | Papers | Scoop.it

Collective memory and attention are sustained by two channels: oral communication (communicative memory) and the physical recording of information (cultural memory). Here, we use data on the citation of academic articles and patents, and on the online attention received by songs, movies and biographies, to describe the temporal decay of the attention received by cultural products. We show that, once we isolate the temporal dimension of the decay, the attention received by cultural products decays following a universal biexponential function. We explain this universality by proposing a mathematical model based on communicative and cultural memory, which fits the data better than previously proposed log-normal and exponential models. Our results reveal that biographies remain in our communicative memory the longest (20–30 years) and music the shortest (about 5.6 years). These findings show that the average attention received by cultural products decays following a universal biexponential function.

 

The universal decay of collective memory and attention
Cristian Candia, C. Jara-Figueroa, Carlos Rodriguez-Sickert, Albert-László Barabási & César A. Hidalgo 
Nature Human Behaviour (2018)

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Macroscopic dynamics and the collapse of urban traffic

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.


Macroscopic dynamics and the collapse of urban traffic
Luis E. Olmos, Serdar Çolak, Sajjad Shafiei, Meead Saberi, and Marta C. González
PNAS December 11, 2018 115 (50) 12654-12661

https://doi.org/10.1073/pnas.1800474115

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Modeling Memory Effects in Activity-Driven Networks

Modeling Memory Effects in Activity-Driven Networks | Papers | Scoop.it

Activity-driven networks (ADNs) have recently emerged as a powerful paradigm to study the temporal evolution of stochastic networked systems. All the information on the time-varying nature of the system is encapsulated into a constant activity parameter, which represents the propensity to generate connections. This formulation has enabled the scientific community to perform effective analytical studies on temporal networks. However, the hypothesis that the whole dynamics of the system is summarized by constant parameters might be excessively restrictive. Empirical studies suggest that activity evolves in time, intertwined with the system evolution, causing burstiness and clustering phenomena. In this paper, we propose a novel model for temporal networks, in which a self-excitement mechanism governs the temporal evolution of the activity, linking it to the evolution of the networked system. We investigate the effect of self-excitement on the epidemic inception by comparing the epidemic threshold of a Susceptible--Infected--Susceptible model in the presence and in the absence of the self-excitement mechanism. Our results suggest that the temporal nature of the activity favors the epidemic inception. Hence, neglecting self-excitement mechanisms might lead to harmful underestimation of the risk of an epidemic outbreak. Extensive numerical simulations are presented to support and extend our analysis, exploring parameter heterogeneities and noise, transient dynamics, and immunization processes. Our results constitute a first, necessary step toward a theory of ADNs that accounts for memory effects in the network evolution.


Modeling Memory Effects in Activity-Driven Networks
Lorenzo Zino, Alessandro Rizzo, and Maurizio Porfiri

SIAM J. Appl. Dyn. Syst., 17(4), 2830–2854. (25 pages)

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The Inherent Instability of Disordered Systems

The Multiscale Law of Requisite Variety is a scientific law relating, at each scale, the variation in an environment to the variation in internal state that is necessary for effective response by a system. While this law has been used to describe the effectiveness of systems in self-regulation, the consequences for failure have not been formalized. Here we use this law to consider the internal dynamics of an unstructured system, and its response to a structured environment. We find that, due to its inability to respond, a completely unstructured system is inherently unstable to the formation of structure. And in general, any system without structure above a certain scale is unable to withstand structure arising above that scale. To describe complicated internal dynamics, we develop a characterization of multiscale changes in a system. This characterization is motivated by Shannon information theoretic ideas of noise, but considers structured information. We then relate our findings to political anarchism showing that society requires some organizing processes, even if there is no traditional government or hierarchies. We also formulate our findings as an inverse second law of thermodynamics; while closed systems collapse into disorder, systems open to a structured environment spontaneously generate order.

 

Taeer Bar-Yam, Owen Lynch, Yaneer Bar-Yam, The inherent instability of disordered systems, arXiv:1812.00450

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Consciousness: The last 50 years (and the next)

The mind and brain sciences began with consciousness as a central concern. But for much of the 20th century, ideological and methodological concerns relegated its empirical study to the margins. Since the 1990s, studying consciousness has regained a legitimacy and momentum befitting its status as the primary feature of our mental lives. Nowadays, consciousness science encompasses a rich interdisciplinary mixture drawing together philosophical, theoretical, computational, experimental, and clinical perspectives, with neuroscience its central discipline. Researchers have learned a great deal about the neural mechanisms underlying global states of consciousness, distinctions between conscious and unconscious perception, and self-consciousness. Further progress will depend on specifying closer explanatory mappings between (first-person subjective) phenomenological descriptions and (third-person objective) descriptions of (embodied and embedded) neuronal mechanisms. Such progress will help reframe our understanding of our place in nature and accelerate clinical approaches to a wide range of psychiatric and neurological disorders.

 

Consciousness: The last 50 years (and the next)
Anil K. Seth
Brain and Neuroscience Advances

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A structural transition in physical networks

A structural transition in physical networks | Papers | Scoop.it

In many physical networks, including neurons in the brain1,2, three-dimensional integrated circuits3 and underground hyphal networks4, the nodes and links are physical objects that cannot intersect or overlap with each other. To take this into account, non-crossing conditions can be imposed to constrain the geometry of networks, which consequently affects how they form, evolve and function. However, these constraints are not included in the theoretical frameworks that are currently used to characterize real networks5,6,7. Most tools for laying out networks are variants of the force-directed layout algorithm8,9—which assumes dimensionless nodes and links—and are therefore unable to reveal the geometry of densely packed physical networks. Here we develop a modelling framework that accounts for the physical sizes of nodes and links, allowing us to explore how non-crossing conditions affect the geometry of a network. For small link thicknesses, we observe a weakly interacting regime in which link crossings are avoided via local link rearrangements, without altering the overall geometry of the layout compared to the force-directed layout. Once the link thickness exceeds a threshold, a strongly interacting regime emerges in which multiple geometric quantities, such as the total link length and the link curvature, scale with the link thickness. We show that the crossover between the two regimes is driven by the non-crossing condition, which allows us to derive the transition point analytically and show that networks with large numbers of nodes will ultimately exist in the strongly interacting regime. We also find that networks in the weakly interacting regime display a solid-like response to stress, whereas in the strongly interacting regime they behave in a gel-like fashion. Networks in the weakly interacting regime are amenable to 3D printing and so can be used to visualize network geometry, and the strongly interacting regime provides insights into the scaling of the sizes of densely packed mammalian brains.

 

A structural transition in physical networks
Nima Dehmamy, Soodabeh Milanlouei & Albert-László Barabási 
Nature volume 563, pages 676–680 (2018)

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