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The Origins of Scaling in Cities

Cities are perhaps the ultimate expression of human sociality displaying at once humanity’s greatest achievements and some of its most difficult challenges. Despite the increasing importance of cities in human societies our ability to understand them scientifically, and manage them in practice, has remained unsatisfactorily limited. The greatest difficulties to any scientific approach to cities have resulted from their many interdependent facets, as social, economic, infrastructural and spatial complex systems, which exist in similar but changing forms over a huge range of scales. Here, I show how cities may evolve following a small set of basic principles that operate locally and can explain how cities change gradually from the bottom-up. As a result I obtain a theoretical framework that derives the general open-ended properties of cities through the optimization of a set of local conditions. This framework is used to predict, in a unified and quantitative way, the average social, spatial and infrastructural properties of cities as a set of scaling relations that apply to all urban systems, many of which have been observed in nations around the world. Finally, I compare and contrast the structure and dynamics of cities to those of other complex systems that share some analogous properties.

 

The Origins of Scaling in Cities
Lúis M. A. Bettencourt

SFI-WP 12-09-014

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Control energy scaling in temporal networks

In practical terms, controlling a network requires manipulating a large number of nodes with a comparatively small number of external inputs, a process that is facilitated by paths that broadcast the influence of the (directly-controlled) driver nodes to the rest of the network. Recent work has shown that surprisingly, temporal networks can enjoy tremendous control advantages over their static counterparts despite the fact that in temporal networks such paths are seldom instantaneously available. To understand the underlying reasons, here we systematically analyze the scaling behavior of a key control cost for temporal networks--the control energy. We show that the energy costs of controlling temporal networks are determined solely by the spectral properties of an "effective" Gramian matrix, analogous to the static network case. Surprisingly, we find that this scaling is largely dictated by the first and the last network snapshot in the temporal sequence, independent of the number of intervening snapshots, the initial and final states, and the number of driver nodes. Our results uncover the intrinsic laws governing why and when temporal networks save considerable control energy over their static counterparts.

 

Control energy scaling in temporal networks
Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási

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Community energy storage: A smart choice for the smart grid?

•We compare batteries deployed in 4500 individual households with 200 communities.

•Using real demand, PV data and locations we form community microgrids.

•We find that community batteries are more effective for distributed PV integration.

•Internal rates of return depend on the number of PV households.

 

Community energy storage: A smart choice for the smart grid?
Edward Barbour, David Parra, Zeyad Awwad, Marta C.González

Applied Energy
Volume 212, 15 February 2018, Pages 489-497

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Scientists just uncovered the cause of a massive epidemic which killed the Aztecs, using 500-year-old teeth

Scientists just uncovered the cause of a massive epidemic which killed the Aztecs, using 500-year-old teeth | Papers | Scoop.it
Nearly 500 years ago, in what we know call Mexico, a disease started rippling through the population.

 

It bore the name cocoliztli, meaning ‘pestilence,’ and it killed between five and 15 million people in just three years. As many plagues were at the time, it proved deadly and mysterious, burning through entire populations. Occurring centuries before John Snow’s work on cholera gave rise to epidemiology, data on the disease’s devastation was sparse. Over the years, researchers and historians attempted to pin the blame for the illness on measles, plague, viral hemorrhagic fevers like Ebola, and typhoid fever—a disease caused by a variation of the bacteria Salmonella enterica.

 

In a paper published this week in Nature Ecology & Evolution, researchers present evidence that the latter was the most likely candidate in this cast of microbial miscreants. The study was pre-printed in biorxiv last year. The researchers detected the genome of a different variety of Salmonella enterica (the specific variety is Paratyphi C) in teeth of individuals buried in a cemetery historically linked to the deadly outbreak.

 

The researchers used a technique called MALT (MEGAN Alignment Tool) to analyze DNA left behind in the pulp of the teeth. MALT takes a sample of material, in this case from a tooth, and compares it to 6,247 known bacterial genomes. The results identified Salmonella enterica in 10 burials associated with the epidemic.


Via Dr. Stefan Gruenwald
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Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity

Multilayer networks describe well many real interconnected communication and transportation systems, ranging from computer networks to multimodal mobility infrastructures. Here, we introduce a model in which the nodes have a limited capacity of storing and processing the agents moving over a multilayer network, and their congestions trigger temporary faults which, in turn, dynamically affect the routing of agents seeking for uncongested paths. The study of the network performance under different layer velocities and node maximum capacities, reveals the existence of delicate trade-offs between the number of served agents and their time to travel to destination. We provide analytical estimates of the optimal buffer size at which the travel time is minimum and of its dependence on the velocity and number of links at the different layers. Phenomena reminiscent of the Slower Is Faster (SIF) effect and of the Braess' paradox are observed in our dynamical multilayer set-up.

 

Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity
Sabato Manfredi, Edmondo Di Tucci, Vito Latora

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Quantifying China’s regional economic complexity

Quantifying China’s regional economic complexity | Papers | Scoop.it

China’s regional economic complexity is quantified by modeling 25 years’ public firm data.
High positive correlation between economic complexity and macroeconomic indicators is shown.
Economic complexity has explanatory power for economic development and income inequality.
Multivariate regressions suggest the robustness of these results with controlling socioeconomic factors.

 

Quantifying China’s regional economic complexity
Jian Gao, Tao Zhou

Physica A: Statistical Mechanics and its Applications
Volume 492, 15 February 2018, Pages 1591-1603

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From Maps to Multi-dimensional Network Mechanisms of Mental Disorders

From Maps to Multi-dimensional Network Mechanisms of Mental Disorders | Papers | Scoop.it

The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.

 

From Maps to Multi-dimensional Network Mechanisms of Mental Disorders
Urs Braun, Axel Schaefer, Richard F. Betzel, Heike Tost, Andreas Meyer-Lindenberg, Danielle S. Bassett

Neuron
Volume 97, Issue 1, 3 January 2018, Pages 14-31

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Learning how to understand complexity and deal with sustainability challenges – A framework for a comprehensive approach and its application in university education

• Sustainability challenges require both specialized and integrative approaches.
• Domination of specialism and reductionism calls for emphasis on comprehensiveness.
• The GHH framework can be used as a tool to add comprehensiveness in education.
• The framework consists of three dimensions: generalism, holism, and holarchism.
• The dialectical approach combines comprehensive and differentiative approaches.

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A framework for designing compassionate and ethical artificial intelligence and artificial consciousness

Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from a fear of artificial intelligence towards designing machines that embed our cherished values in them. Consciousness, intelligence and empathy would be worthy design goals that can be engineered in machines.

 

Banerjee S. (2018) A framework for designing compassionate and ethical artificial intelligence and artificial consciousness. PeerJ Preprints 6:e3502v2 https://doi.org/10.7287/peerj.preprints.3502v2

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I Am A Science Lady's curator insight, January 10, 3:44 PM
If we're going to make AI more human, it's a good idea to make sure it's non-psychopathic.  I'm interested to see how this develops over the coming years; it seems there are still many issues with comprehension and how realistic the AI comes across as.
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A review of “Pathway Complexity”, a measure claimed to be able to distinguish life from non-life unlike no other measure before

A paper recently published in the Philosophical Transactions of the Royal Society A under the title “A probabilistic framework for identifying biosignatures using Pathway Complexity" claims to offer a revolutionary measure potentially capable of distinguishing life from non-life and even discerning life on other planets by finding biosignatures. The method proposed by its authors consists roughly in finding the generative grammar behind an object and then counting the number of steps needed to generate said object from its compressed form. Unfortunately, this does not amount to a new measure of complexity. The first part of the algorithm is mostly a description of Huffman's coding algorithm (see ref. below) and represents the way in which most popular lossless compression algorithms are implemented: finding the building blocks that can best compress a string by minimising redundancies, decomposing it into the statistically smallest collection of components that reproduce it without loss of information by traversing the string and finding repetitions.

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Cascading Failures as Continuous Phase-Space Transitions

In network systems, a local perturbation can amplify as it propagates, potentially leading to a large-scale cascading failure. Here we derive a continuous model to advance our understanding of cascading failures in power-grid networks. The model accounts for both the failure of transmission lines and the desynchronization of power generators and incorporates the transient dynamics between successive steps of the cascade. In this framework, we show that a cascade event is a phase-space transition from an equilibrium state with high energy to an equilibrium state with lower energy, which can be suitably described in a closed form using a global Hamiltonian-like function. From this function, we show that a perturbed system cannot always reach the equilibrium state predicted by quasi-steady-state cascade models, which would correspond to a reduced number of failures, and may instead undergo a larger cascade. We also show that, in the presence of two or more perturbations, the outcome depends strongly on the order and timing of the individual perturbations. These results offer new insights into the current understanding of cascading dynamics, with potential implications for control interventions.

 

Cascading Failures as Continuous Phase-Space Transitions
Yang Yang and Adilson E. Motter
Phys. Rev. Lett. 119, 248302

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nukem777's curator insight, December 18, 2017 4:14 AM
Good luck .... not too many folks will be able, much less take the time to read this....good, nonetheless
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Correlations between human mobility and social interaction reveal general activity patterns

A day in the life of a person involves a broad range of activities which are common across many people. Going beyond diurnal cycles, a central question is: to what extent do individuals act according to patterns shared across an entire population? Here we investigate the interplay between different activity types, namely communication, motion, and physical proximity by analyzing data collected from smartphones distributed among 638 individuals. We explore two central questions: Which underlying principles govern the formation of the activity patterns? Are the patterns specific to each individual or shared across the entire population? We find that statistics of the entire population allows us to successfully predict 71% of the activity and 85% of the inactivity involved in communication, mobility, and physical proximity. Surprisingly, individual level statistics only result in marginally better predictions, indicating that a majority of activity patterns are shared across our sample population. Finally, we predict short-term activity patterns using a generalized linear model, which suggests that a simple linear description might be sufficient to explain a wide range of actions, whether they be of social or of physical character.

 

Mollgaard A, Lehmann S, Mathiesen J (2017) Correlations between human mobility and social interaction reveal general activity patterns. PLoS ONE 12(12): e0188973. https://doi.org/10.1371/journal.pone.0188973

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Opinion Diffusion on Multilayer Social Networks

In this paper, to reveal the influence of multilayer network structure on opinion diffusion in social networks, we study an opinion dynamics model based on DeGroot model on multilayer networks. We find that if the influence matrix integrating the information of connectedness for each layer and correlation between layers is strongly connected and aperiodic, all agents’ opinions will reach a consensus. However, if there are stubborn agents in the networks, regular agents’ opinions will finally be confined to the convex combinations of the stubborn agents’. Specifically, if all stubborn agents hold the same opinion, even if the agents only exist on a certain layer, their opinions will diffuse to the entire multilayer networks. This paper not only characterizes the influence of multilayer network topology and agent attribute on opinion diffusion in a holistic way, but also demonstrates the importance of coupling agents which play an indispensable role in some social and economic situations.


Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219525917500151

 

HAI-BO HU, CANG-HAI LI, and QING-YING MIAO, Advs. Complex Syst. 20, 1750015 (2017) [25 pages]
https://doi.org/10.1142/S0219525917500151
OPINION DIFFUSION ON MULTILAYER SOCIAL NETWORKS

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The Emergence of Consensus: A Primer

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and scattered widely across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges 'spontaneously' in absence of centralised institutions and covers topic that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

Via Samir
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Ivon Prefontaine, PhD's curator insight, December 14, 2017 4:59 PM
There is a link to a PDF article.

The article explores how consensus arises when institutions are not involved.
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Understanding predictability and exploration in human mobility

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

 

Understanding predictability and exploration in human mobility
Andrea Cuttone, Sune Lehmann and Marta C. González
EPJ Data Science20187:2
https://doi.org/10.1140/epjds/s13688-017-0129-1

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Socioeconomic characterization of regions through the lens of individual financial transactions

Socioeconomic characterization of regions through the lens of individual financial transactions | Papers | Scoop.it

People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators.

 

Hashemian B, Massaro E, Bojic I, Murillo Arias J, Sobolevsky S, Ratti C (2017) Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE 12(11): e0187031. https://doi.org/10.1371/journal.pone.0187031

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Complexity, Development, and Evolution in Morphogenetic Collective Systems

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system's structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.

 

Complexity, Development, and Evolution in Morphogenetic Collective Systems
Hiroki Sayama

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Serendipity and strategy in rapid innovation

Serendipity and strategy in rapid innovation | Papers | Scoop.it

Innovation is to organizations what evolution is to organisms: it is how organizations adapt to environmental change and improve. Yet despite advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to accelerate innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role. To unify these perspectives, we analysed the mathematics of innovation as a search for designs across a universe of component building blocks. We tested our insights using data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire components, we observed that the relative usefulness of different components can cross over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers in advance, they offer opportunities to strategically increase the growth of the product space.

 

Serendipity and strategy in rapid innovation
T. M. A. Fink, M. Reeves, R. Palma & R. S. Farr
Nature Communications 8, Article number: 2002 (2017)
doi:10.1038/s41467-017-02042-w

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A Mathematician Who Decodes the Patterns Stamped Out by Life

A Mathematician Who Decodes the Patterns Stamped Out by Life | Papers | Scoop.it
Corina Tarnita deciphers bizarre patterns in the soil created by competing life-forms. She’s found that they can reveal whether an ecosystem is thriving or on the verge of collapse.
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Modelling indirect interactions during failure spreading in a project activity network

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect exposure remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and indirect exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that indirect exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of hidden influentials in large-scale spreading events, and evaluate the role of direct and indirect exposure in their emergence. Given the evidence of the importance of indirect exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.

 

Modelling indirect interactions during failure spreading in a project activity network
Christos Ellinas

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Using physics, math and models to fight cancer drug resistance

Using physics, math and models to fight cancer drug resistance | Papers | Scoop.it
Despite the increasing effectiveness of breast cancer treatments over the last 50 years, tumors often become resistent to the drugs used. While drug combinations could be part of the solution to this problem, their development is very challenging. In this blog post Jorge Zanudo explains how it is possible to combine physical and mathemathical models with clinical and biological data to determine which drug combinations would be most effective in breast cancer therapy.
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Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding

Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding | Papers | Scoop.it

The equal headway instability—the fact that a configuration with regular time intervals between vehicles tends to be volatile—is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system’s data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger’s inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur. The correlation between the simulation of the current model and empirical observations carried out in the Mexico City Metro provides a base to calculate the expected performance of the self-organizing method in case it is implemented in the real system. We also performed a pilot study at the Balderas station to regulate the alighting and boarding of passengers through guide signs on platforms. The analysis of empirical data shows a delay reduction of the waiting time of trains at stations. Finally, we provide recommendations to improve public transportation systems.

 

Carreón G, Gershenson C, Pineda LA (2017) Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding. PLoS ONE 12(12): e0190100. https://doi.org/10.1371/journal.pone.0190100

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Inequality in nature and society

Inequality is one of the main drivers of social tension. We show striking similarities between patterns of inequality between species abundances in nature and wealth in society. We demonstrate that in the absence of equalizing forces, such large inequality will arise from chance alone. While natural enemies have an equalizing effect in nature, inequality in societies can be suppressed by wealth-equalizing institutions. However, over the past millennium, such institutions have been weakened during periods of societal upscaling. Our analysis suggests that due to the very same mathematical principle that rules natural communities (indeed, a “law of nature”) extreme wealth inequality is inevitable in a globalizing world unless effective wealth-equalizing institutions are installed on a global scale.

 

Inequality in nature and society
Marten Scheffer, Bas van Bavel, Ingrid A. van de Leemput, and Egbert H. van Nes

PNAS

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Marcelo Errera's curator insight, December 17, 2017 9:32 PM
With a somewhat different approach, this article arrives to similar conclusions Prof. Bejan and I reached in our paper published last March.

We showed (and predicted) that the Lorenz distribution of income is a natural outcome of a flow system that evolves in time towards greater and greater access in the planet landscape: the movement of goods.

Inequality does not happen by evil, but for self-organization of flow systems (energy, mass flow, movement of goods) and the association with energy expenditure and the production of wealth.

Economies are highly complex (in the sense it is hard to describe), however it can be seen flow organization  plays the role of forcing factor.

Innovation and diversification of flow processes in the economies might provide better chances for less disparity.

The links below will direct to out paper In case the authors or PNAS want to cite it.


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Unveiling The Structure Of Information: The Fundamental Scale

Information has been a matter of intense study and discussion for the last century. The very nature of information, not a physical object, not an entirely abstract entity, has fostered this endless discussion which had some of its first episodes back in the 40’s with the arguments between Wiener and Shannon.

Whether information is the effect produced by the received pattern of signals or the pattern of signals itself, the truth is that these patterns exist and we are devoted to deep into them to find a model for their structures and to establish useful comparisons among them.
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Simple spatial scaling rules behind complex cities

Simple spatial scaling rules behind complex cities | Papers | Scoop.it

Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.

 

Simple spatial scaling rules behind complex cities
Ruiqi Li, Lei Dong, Jiang Zhang, Xinran Wang, Wen-Xu Wang, Zengru Di & H. Eugene Stanley
Nature Communications 8, Article number: 1841 (2017)
doi:10.1038/s41467-017-01882-w

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Slime mould: the fundamental mechanisms of cognition

The slime mould Physarum polycephalum has been used in developing unconventional computing devices for in which the slime mould played a role of a sensing, actuating, and computing device. These devices treated the slime mould rather as an active living substrate yet the slime mould is a self-consistent living creature which evolved for millions of years and occupied most part of the world, but in any case, that living entity did not own true cognition, just automated biochemical mechanisms. To "rehabilitate" the slime mould from the rank of a purely living electronics element to a "creature of thoughts" we are analyzing the cognitive potential of P. polycephalum. We base our theory of minimal cognition of the slime mould on a bottom-up approach, from the biological and biophysical nature of the slime mould and its regulatory systems using frameworks suh as Lyon's biogenic cognition, Muller, di Primio-Lengeler\'s modifiable pathways, Bateson's "patterns that connect" framework, Maturana's autopoetic network, or proto-consciousness and Morgan's Canon.

 

Slime mould: the fundamental mechanisms of cognition
Jordi Vallverdu, Oscar Castro, Richard Mayne, Max Talanov, Michael Levin, Frantisek Baluska, Yukio Gunji, Audrey Dussutour, Hector Zenil, Andrew Adamatzky

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