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Morphology of travel routes and the organization of cities

Morphology of travel routes and the organization of cities | Papers | Scoop.it

The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities. Of particular interest is the interplay between street structure and its functional usage. Here, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world, finding that their collective morphology exhibits a directional bias influenced by the attractive (or repulsive) forces resulting from congestion, accessibility, and travel demand. To capture this, we develop a simple geometric measure, inness, that maps this force field. In particular, cities with common inness patterns cluster together in groups that are correlated with their putative stage of urban development as measured by a series of socio-economic and infrastructural indicators, suggesting a strong connection between urban development, increasing physical connectivity, and diversity of road hierarchies.

 

Morphology of travel routes and the organization of cities
Minjin Lee, Hugo Barbosa, Hyejin Youn, Petter Holme & Gourab Ghoshal
Nature Communications volume 8, Article number: 2229 (2017)
doi:10.1038/s41467-017-02374-7

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Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty

This work presents a theoretical and numerical analysis of the conditions under which distributed sequential consensus is possible when the state of a portion of nodes in a network is perturbed. Specifically, it examines the consensus level of partially connected blockchains under failure/attack events. To this end, we developed stochastic models for both verification probability once an error is detected and network breakdown when consensus is not possible. Through a mean field approximation for network degree we derive analytical solutions for the average network consensus in the large graph size thermodynamic limit. The resulting expressions allow us to derive connectivity thresholds above which networks can tolerate an attack.

 

Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty
Francisco Prieto-Castrillo, Sergii Kushch, and Juan Manuel Corchado

Complexity
Volume 2017 (2017), Article ID 4832740, 11 pages
https://doi.org/10.1155/2017/4832740 

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Evolutionary dynamics of incubation periods

The incubation period for typhoid, polio, measles, leukemia and many other diseases follows a right-skewed, approximately lognormal distribution. Although this pattern was discovered more than sixty years ago, it remains an open question to explain its ubiquity. Here, we propose an explanation based on evolutionary dynamics on graphs. For simple models of a mutant or pathogen invading a network-structured population of healthy cells, we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness, competition dynamics, and network structure. The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk. Unlike previous explanations that rely crucially on heterogeneity, our results hold even for homogeneous populations. Thus, we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may, by chance alone, show remarkably different time courses of disease.

 

Evolutionary dynamics of incubation periods
Bertrand Ottino-Loffler Jacob G Scott Steven H Strogatz

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Digital epidemiology: what is it, and where is it going?

Digital Epidemiology is a new field that has been growing rapidly in the past few years, fueled by the increasing availability of data and computing power, as well as by breakthroughs in data analytics methods. In this short piece, I provide an outlook of where I see the field heading, and offer a broad and a narrow definition of the term.

 

Digital epidemiology: what is it, and where is it going?
Marcel Salathé

Life Sciences, Society and Policy
December 2018, 14:1

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The role of gender in social network organization

The role of gender in social network organization | Papers | Scoop.it

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than 800 university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual’s characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?

 

Psylla I, Sapiezynski P, Mones E, Lehmann S (2017) The role of gender in social network organization. PLoS ONE 12(12): e0189873. https://doi.org/10.1371/journal.pone.0189873

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Thinking Fast and Slow on Networks: Co-evolution of Cognition and Cooperation in Structured Populations

Spatial structure is one of the most studied mechanisms in evolutionary game theory. Here, we explore the consequences of spatial structure for a question which has received considerable empirical and theoretical attention in recent years, but has not yet been studied from a network perspective: whether cooperation relies on intuitive predispositions or deliberative self-control. We examine this question using a model which integrates the “dual-process” framework from cognitive science with evolutionary game theory, and considers the evolution of agents who are embedded within a social network and only interact with their neighbors. In line with past work in well-mixed populations, we find that selection favors either the intuitive defector (ID) strategy which never deliberates, or the dual-process cooperator (DC) strategy which intuitively cooperates but uses deliberation to switch to defection in Prisoner’s Dilemma games. We find that sparser networks (i.e. smaller average degree) facilitate the success of DC over ID, while also reducing the level of deliberation that DC agents engage in; and that these results generalize across different kinds of networks. These observations demonstrate the important role that spatial structure can have not just on the evolution of cooperation, but on the co-evolution of cognition and cooperation.

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Random walks and diffusion on networks

Random walks and diffusion on networks | Papers | Scoop.it

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

 

Random walks and diffusion on networks
Naoki Masuda, Mason A. Porter, Renaud Lambiotte

Physics Reports
Volumes 716–717, 22 November 2017, Pages 1-58

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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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Simpler grammar, larger vocabulary: How population size affects language

Languages with many speakers tend to be structurally simple while small communities sometimes develop languages with great structural complexity. Paradoxically, the opposite pattern appears to be observed for non-structural properties of language such as vocabulary size. These apparently opposite patterns pose a challenge for theories of language change and evolution. We use computational simulations to show that this inverse pattern can depend on a single factor: ease of diffusion through the population. A population of interacting agents was arranged on a network, passing linguistic conventions to one another along network links. Agents can invent new conventions, or replicate conventions that they have previously generated themselves or learned from other agents. Linguistic conventions are either Easy or Hard to diffuse, depending on how many times an agent needs to encounter a convention to learn it. In large groups, only linguistic conventions that are easy to learn, such as words, tend to proliferate, whereas small groups where everyone talks to everyone else allow for more complex conventions, like grammatical regularities, to be maintained. Our simulations thus suggest that language, and possibly other aspects of culture, may become simpler at the structural level as our world becomes increasingly interconnected.

 

Reali, F., Chater, N. & Christiansen, M.H. (2018). Simpler grammar, larger vocabulary: How population size affects language. Proceedings of the Royal Society B: Biological Sciences, 285, 20172586. http://rspb.royalsocietypublishing.org/content/285/1871/20172586 

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Thermodynamics and the dynamics of information in distributed computation

Information dynamics is an emerging description of information processing in complex systems. In this paper we make a formal analogy between information dynamics and stochastic thermodynamics. As stochastic dynamics increasingly concerns itself with the processing of information we suggest such an analogy is instructive in providing hitherto unexplored insights into the implicit information processing that occurs in physical systems. Information dynamics describes systems in terms of intrinsic computation, identifying computational primitives of information storage and transfer. We construct irreversibility measures in terms of these quantities are relate them to the physical entropy productions that govern the behaviour of single and composite systems in stochastic thermodynamics illustrating them with simple examples. Moreover, we can apply such a formalism to systems which do not have a bipartite structure. In particular we demonstrate that, given suitable non-bipartite processes, the heat flow in a subsystem can still be identified and one requires the present formalism to recover generalisations of the second law. This opens up the possibility of describing all physical systems in terms of computation allowing us to propose a framework for discussing the reversibility of systems traditionally out of scope of stochastic thermodynamics.

 

Thermodynamics and the dynamics of information in distributed computation
Richard E. Spinney, Joseph T. Lizier, Mikhail Prokopenko

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Scale-free networks are rare

A central claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree k follows a power law, decaying like k^−α, often with 2<α<3. However, empirical evidence for this belief derives from a relatively small number of real-world networks. We test the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from social, biological, technological, and informational sources. We fit the power-law model to each degree distribution, test its statistical plausibility, and compare it via a likelihood ratio test to alternative, non-scale-free models, e.g., the log-normal. Across domains, we find that scale-free networks are rare, with only 4% exhibiting the strongest-possible evidence of scale-free structure and 52% exhibiting the weakest-possible evidence. Furthermore, evidence of scale-free structure is not uniformly distributed across sources: social networks are at best weakly scale free, while a handful of technological and biological networks can be called strongly scale free. These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.

 

Scale-free networks are rare
Anna D. Broido, Aaron Clauset

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Twitter discussion, including Aaron Clauset, Laszlo Barabasi, Alex Vespignani, Duncan Watts, Stefano Zapperi, Petter Holme, Gabor Vattay, et al.

https://twitter.com/manlius84/timelines/952248309720211458 

Blog post by Petter Holme

https://petterhol.me/2018/01/12/me-and-power-laws/ 

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Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization

Do human societies from around the world exhibit similarities in the way that they are structured and show commonalities in the ways that they have evolved? To address these long-standing questions, we constructed a database of historical and archaeological information from 30 regions around the world over the last 10,000 years. Our analyses revealed that characteristics, such as social scale, economy, features of governance, and information systems, show strong evolutionary relationships with each other and that complexity of a society across different world regions can be meaningfully measured using a single principal component of variation. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

 

Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization
Peter Turchin, Thomas E. Currie, Harvey Whitehouse, Pieter François, Kevin Feeney, Daniel Mullins, Daniel Hoyer, Christina Collins, Stephanie Grohmann, Patrick Savage, Gavin Mendel-Gleason, Edward Turner, Agathe Dupeyron, Enrico Cioni, Jenny Reddish, Jill Levine, Greine Jordan, Eva Brandl, Alice Williams, Rudolf Cesaretti, Marta Krueger, Alessandro Ceccarelli, Joe Figliulo-Rosswurm, Po-Ju Tuan, Peter Peregrine, Arkadiusz Marciniak, Johannes Preiser-Kapeller, Nikolay Kradin, Andrey Korotayev, Alessio Palmisano, David Baker, Julye Bidmead, Peter Bol, David Christian, Connie Cook, Alan Covey, Gary Feinman, Árni Daníel Júlíusson, Axel Kristinsson, John Miksic, Ruth Mostern, Cameron Petrie, Peter Rudiak-Gould, Barend ter Haar, Vesna Wallace, Victor Mair, Liye Xie, John Baines, Elizabeth Bridges, Joseph Manning, Bruce Lockhart, Amy Bogaard and Charles Spencer
PNAS 2017; published ahead of print December 21, 2017, https://doi.org/10.1073/pnas.1708800115

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Carlos Garcia Pando's comment, January 27, 12:28 PM
Is there any way to predict "sustainability" of a given social organisation based on itself, its complexity, assuming there are no enemies, no wars?
Very interesting research topic, thanks for this
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Detecting reciprocity at a global scale

Reciprocity stabilizes cooperation from the level of microbes all the way up to humans interacting in small groups, but does reciprocity also underlie stable cooperation between larger human agglomerations, such as nation states? Famously, evolutionary models show that reciprocity could emerge as a widespread strategy for achieving international cooperation. However, existing studies have only detected reciprocity-driven cooperation in a small number of country pairs. We apply a new method for detecting mutual influence in dynamical systems to a new large-scale data set that records state interactions with high temporal resolution. Doing so, we detect reciprocity between many country pairs in the international system and find that these reciprocating country pairs exhibit qualitatively different cooperative dynamics when compared to nonreciprocating pairs. Consistent with evolutionary theories of cooperation, reciprocating country pairs exhibit higher levels of stable cooperation and are more likely to punish instances of noncooperation. However, countries in reciprocity-based relationships are also quicker to forgive single acts of noncooperation by eventually returning to previous levels of mutual cooperation. By contrast, nonreciprocating pairs are more likely to exploit each other’s cooperation via higher rates of defection. Together, these findings provide the strongest evidence to date that reciprocity is a widespread mechanism for achieving international cooperation.

 

Detecting reciprocity at a global scale
Morgan R. Frank, Nick Obradovich, Lijun Sun, Wei Lee Woon, Brad L. LeVeck and Iyad Rahwan,
Science Advances  03 Jan 2018:
Vol. 4, no. 1, eaao5348
DOI: 10.1126/sciadv.aao5348

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Neuromodulation Influences Synchronization and Intrinsic Read-out

The roles of neuromodulation in a neural network, such as in a cortical microcolumn, are still incompletely understood. Neuromodulation influences neural processing by presynaptic and postsynaptic regulation of synaptic efficacy. Synaptic efficacy modulation can be an effective way to rapidly alter network density and topology. We show that altering network topology, together with density, will affect its synchronization. Fast synaptic efficacy modulation may therefore influence the amount of correlated spiking in a network. Neuromodulation also affects ion channel regulation for intrinsic excitability, which alters the neuron's activation function. We show that synchronization in a network influences the read-out of these intrinsic properties. Highly synchronous input drives neurons, such that differences in intrinsic properties disappear, while asynchronous input lets intrinsic properties determine output behavior. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven network activity. We conclude that neuromodulation may allow a network to shift between a more synchronized transmission mode and a more asynchronous intrinsic read-out mode.

 

Neuromodulation Influences Synchronization and Intrinsic Read-out

Gabriele Scheler
doi: https://doi.org/10.1101/251801

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nukem777's curator insight, January 23, 7:30 PM
In english please!!
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Sensitive Dependence of Optimal Network Dynamics on Network Structure

Sensitive Dependence of Optimal Network Dynamics on Network Structure | Papers | Scoop.it
The relationship between the structure and dynamics of a network is key to understanding the behavior of complex systems. A new analysis shows how network optimization, whether designed or evolved, can lead to collective dynamics that depend sensitively on the structure of the network.

 

Sensitive Dependence of Optimal Network Dynamics on Network Structure

Takashi Nishikawa, Jie Sun, and Adilson E. Motter
Phys. Rev. X 7, 041044

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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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