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Evolutionary Biology for the 21st Century

We live in an exciting time for biology. Technological advances have made data collection easier and cheaper than we could ever have imagined just 10 years ago. We can now synthesize and analyze large data sets containing genomes, transcriptomes, proteomes, and multivariate phenotypes. At the same time, society's need for the results of biological research has never been greater. Solutions to many of the world's most pressing problems—feeding a global population, coping with climate change, preserving ecosystems and biodiversity, curing and preventing genetically based diseases—will rely heavily on biologists, collaborating across disciplines.

 

Losos JB, Arnold SJ, Bejerano G, Brodie ED III, Hibbett D, et al. (2013) Evolutionary Biology for the 21st Century. PLoS Biol 11(1): e1001466. http://dx.doi.org/10.1371/journal.pbio.1001466

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Corrupting cooperation and how anti-corruption strategies may backfire

Understanding how humans sustain cooperation in large, anonymous societies remains a central question of both theoretical and practical importance. In the laboratory, experimental behavioural research using tools like public goods games suggests that cooperation can be sustained by institutional punishment—analogous to governments, police forces and other institutions that sanction free-riders on behalf of individuals in large societies1,2,3. In the real world, however, corruption can undermine the effectiveness of these institutions4,5,6,7,8. Levels of corruption correlate with institutional, economic and cultural factors, but the causal directions of these relationships are difficult to determine5,6,8,​9,​10. Here, we experimentally model corruption by introducing the possibility of bribery. We investigate the effect of structural factors (a leader’s punitive power and economic potential), anti-corruption strategies (transparency and leader investment in the public good) and cultural background. The results reveal that (1) corruption possibilities cause a large (25%) decrease in public good provisioning, (2) empowering leaders decreases cooperative contributions (in direct opposition to typical institutional punishment results), (3) growing up in a more corrupt society predicts more acceptance of bribes and (4) anti-corruption strategies are effective under some conditions, but can further decrease public good provisioning when leaders are weak and the economic potential is poor. These results suggest that a more nuanced approach to corruption is needed and that proposed panaceas, such as transparency, may actually be harmful in some contexts.

 

Corrupting cooperation and how anti-corruption strategies may backfire
Michael Muthukrishna, Patrick Francois, Shayan Pourahmadi & Joseph Henrich
Nature Human Behaviour 1, Article number: 0138 (2017)
doi:10.1038/s41562-017-0138

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Quantifying Retail Agglomeration using Diverse Spatial Data

Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks an appropriate theoretical framework. The model we present here helps to address these issues. Based on random utility theory, we have built it around the idea of quantifying the role of floor-space and agglomeration in retail location choice. We test this model on the inner area of Greater London. The results are consistent with a super linear scaling of a retailer’s attractiveness with its floorspace, and with an agglomeration effect approximated as the total retail floorspace within a 300 m radius from each shop. Our model illustrates many of the issues involved in testing and validating urban simulation models involving spatial data and its aggregation to different spatial scales.

 

Quantifying Retail Agglomeration using Diverse Spatial Data
Duccio Piovani, Vassilis Zachariadis & Michael Batty
Scientific Reports 7, Article number: 5451 (2017)
doi:10.1038/s41598-017-05304-1

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Mapping spreading dynamics: From time respecting shortest paths to bond percolation

We propose a mapping of spreading dynamics to an ensemble of weighted networks, where edge weights represent propagation time delays. In this mapping, shortest paths in the weighted networks preserve the temporal causality of spreading. Our framework provides insights into the local and global spreading dynamics, enables efficient source detection, and helps to improve strategies for time-critical vaccination. Finally, we establish the connection of our mapping to bond percolation theory.

 

Mapping spreading dynamics: From time respecting shortest paths to bond percolation
Dijana Tolic, Kaj-Kolja Kleineberg, Nino Antulov-Fantulin

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Towards an integrated science of language

Towards an integrated science of language | Papers | Scoop.it

It has long been assumed that grammar is a system of abstract rules, that the world's languages follow universal patterns, and that we are born with a ‘language instinct’. But an alternative paradigm that focuses on how we learn and use language is emerging, overturning these assumptions and many more.

 

Towards an integrated science of language
Morten H. Christiansen & Nick Chater

Nature Human Behaviour 1, Article number: 0163 (2017)DOIdoi:10.1038/s41562-017-0163

 

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Molecules react to their environment

Molecules react to their environment | Papers | Scoop.it
Le Chatelier’s principle is neither hard to state nor to understand. But it’s kind of hard to find the right words. It’s typically expressed as something like: ‘If a dynamic equilibrium is disturbed by changing the conditions, the position of equilibrium moves to counteract the change.’ But there is a clear implication of intentionality here: it’s as though the system is determined to keep its balance.

Sometimes, Le Chatelier’s principle is more or less equated with homeostasis in physiology – the maintenance of a steady state in a changing environment. Some homeostasis, such as pH regulation, does indeed involve the kind of shift in chemical equilibria described by Le Chatelier’s principle. The confusing thing is that biological homeostasis is also a survival mechanism and therefore connected to Darwinian adaptation. We have evolved sweat glands, yet the regulation of body temperature by sweating can be explained by purely physical laws.
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Sustainable development of smart cities: a systematic review of the literature

Sustainable development of smart cities: a systematic review of the literature | Papers | Scoop.it

This paper aims to analyse scientific studies focusing on both environmental sustainability and smart city concepts to understand the relationship between these two. In order to do so the study identifies information about researchers, models, frameworks and tools focused on the chosen themes. This research uses a qualitative methodology, through a systematic review of the literature, which examines the terms, ‘smart city’ and ‘sustainability’, aimed at sustainable development of smart cities. For this, three databases were used: Scopus, Science Direct, and Emerald Insight. This paper provides detailed information on the most recent scientific articles focusing on smart cities and sustainability issues. The paper can serve as a basis for researchers seeking background information for further investigations. The findings provide invaluable insights for scholars researching on the subject, and public managers considering applying those into practice in their cities.

 

Sustainable development of smart cities: a systematic review of the literature
Evelin Priscila Trindade, Marcus Phoebe Farias Hinnig, Eduardo Moreira da Costa, Jamile Sabatini Marques, Rogério Cid Bastos and Tan Yigitcanlar
Journal of Open Innovation: Technology, Market, and Complexity 2017 3:11
https://doi.org/10.1186/s40852-017-0063-2

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A network approach to topic models

One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success --- in particular of its most widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous applications in sociology, history, and linguistics, topic models are known to suffer from severe conceptual and practical problems, e.g. a lack of justification for the Bayesian priors, discrepancies with statistical properties of real texts, and the inability to properly choose the number of topics. Here, we approach the problem of identifying topical structures by representing text corpora as bipartite networks of documents and words and using methods from community detection in complex networks, in particular stochastic block models (SBM). We show that our SBM-based approach constitutes a more principled and versatile framework for topic modeling solving the intrinsic limitations of Dirichlet-based models through a more general choice of nonparametric priors. It automatically detects the number of topics and hierarchically clusters both the words and documents. In practice, we demonstrate through the analysis of artificial and real corpora that our approach outperforms LDA in terms of statistical model selection.

 

A network approach to topic models
Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann

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The Approach Towards Equilibrium in a Reversible Ising Dynamics Model: An Information-Theoretic Analysis Based on an Exact Solution

We study the approach towards equilibrium in a dynamic Ising model, the Q2R cellular automaton, with microscopic reversibility and conserved energy for an infinite one-dimensional system. Starting from a low-entropy state with positive magnetisation, we investigate how the system approaches equilibrium characteristics given by statistical mechanics. We show that the magnetisation converges to zero exponentially. The reversibility of the dynamics implies that the entropy density of the microstates is conserved in the time evolution. Still, it appears as if equilibrium, with a higher entropy density is approached. In order to understand this process, we solve the dynamics by formally proving how the information-theoretic characteristics of the microstates develop over time. With this approach we can show that an estimate of the entropy density based on finite length statistics within microstates converges to the equilibrium entropy density. The process behind this apparent entropy increase is a dissipation of correlation information over increasing distances. It is shown that the average information-theoretic correlation length increases linearly in time, being equivalent to a corresponding increase in excess entropy.

 

Kristian Lindgren and Eckehard Olbrich

Journal of Statistical Physics 168(4), 919-935 (2017).

 

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An Immune System Inspired Theory for Crime and Violence in Cities

Crime is ubiquitous and has been around for millennia. Crime is analogous to a pathogenic infection and police response to it is similar to an immune response. The biological immune system is also engaged in an arms race with pathogens. We propose an immune system inspired theory of crime and violence in human societies, especially in large agglomerations like cities. In this work we suggest that an immune system inspired theory of crime can provide a new perspective on the dynamics of violence in societies. The competitive dynamics between police and criminals has similarities to how the immune system is involved in an arms race with invading pathogens. Cities have properties similar to biological organisms and in this theory the police and military forces would be the immune system that protects against detrimental internal and external forces. Our theory has implications for public policy: ranging from how much financial resource to invest in crime fighting, to optimal policing strategies, pre-placement of police, and number of police to be allocated to different cities. Our work can also be applied to other forms of violence in human societies (like terrorism) and violence in other primate societies and eusocial insects. We hope this will be the first step towards a quantitative theory of violence and conflict in human societies. Ultimately we hope that this will help in designing smart and efficient cities that can scale and be sustainable despite population increase.

 

An Immune System Inspired Theory for Crime and Violence in Cities

Soumya Banerjee
INDECS 15(2), 133-143, 2017
DOI 10.7906/indecs.15.2.2

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The evolution of the host microbiome as an ecosystem on a leash

The evolution of the host microbiome as an ecosystem on a leash | Papers | Scoop.it

The human body carries vast communities of microbes that provide many benefits. Our microbiome is complex and challenging to understand, but evolutionary theory provides a universal framework with which to analyse its biology and health impacts. Here we argue that to understand a given microbiome feature, such as colonization resistance, host nutrition or immune development, we must consider how hosts and symbionts evolve. Symbionts commonly evolve to compete within the host ecosystem, while hosts evolve to keep the ecosystem on a leash. We suggest that the health benefits of the microbiome should be understood, and studied, as an interplay between microbial competition and host control.

 

The evolution of the host microbiome as an ecosystem on a leash
Kevin R. Foster, Jonas Schluter, Katharine Z. Coyte & Seth Rakoff-Nahoum
AffiliationsContributionsCorresponding authors
Nature 548, 43–51 (03 August 2017) doi:10.1038/nature23292

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Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta

Widespread use of single-occupancy cars often leads to traffic congestion. Using anonymized traffic speed data from Android phones collected through Google Maps, we investigated whether high-occupancy vehicle (HOV) policies can combat congestion. We studied Jakarta’s “three-in-one” policy, which required all private cars on two major roads to carry at least three passengers during peak hours. After the policy was abruptly abandoned in April 2016, delays rose from 2.1 to 3.1 minutes per kilometer (min/km) in the morning peak and from 2.8 to 5.3 min/km in the evening peak. The lifting of the policy led to worse traffic throughout the city, even on roads that had never been restricted or at times when restrictions had never been in place. In short, we find that HOV policies can greatly improve traffic conditions.

 

Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta
Rema Hanna, Gabriel Kreindler, Benjamin A. Olken
Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 89-93
DOI: 10.1126/science.aan2747

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To the Elites of the World

To the Elites of the World | Papers | Scoop.it
Faced with climate change, financial, economic and spending crisis, mass migration, terrorism, wars and cyber threats, it appears we are very close to global emergency. Given this state of affairs, we are running out of time to fix the problems of our planet. Here, we present what should be decided during the UN General Assembly on September 23 2017 and a reflexive preamble.
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Large-scale physical activity data reveal worldwide activity inequality

To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health
 
Large-scale physical activity data reveal worldwide activity inequality
Tim Althoff, Rok Sosič, Jennifer L. Hicks, Abby C. King, Scott L. Delp & Jure Leskovec
Nature 547, 336–339 (20 July 2017) doi:10.1038/nature23018

Via Samir
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Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome

Functional connectivity (FC) analysis has revealed stable and reproducible features of brain network organization, as well as their variations across individuals. Here, we localize network markers of individual variability in FC and track their dynamical expression across time. First, we determine the minimal set of network components required to identify individual subjects. Among specific resting-state networks, we find that the FC pattern of the frontoparietal network allows for the most reliable identification of individuals. Looking across the whole brain, an optimization approach designed to identify a minimal node set converges on distributed portions of the frontoparietal system. Second, we track the expression of these network markers across time. We find that the FC fingerprint is most clearly expressed at times when FC patterns exhibit low modularity. In summary, our study reveals distributed network markers of individual variability that are localized in both space and time.

 

Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome
Cleofé Peña-Gómez Andrea Avena-Koenigsberger Jorge Sepulcre Olaf Sporns
Cerebral Cortex, https://doi.org/10.1093/cercor/bhx170

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Jury Rigging and Supply Network Design: Evolutionary “Tinkering” in the presence of Unknown‐Unknowns

Nobel laureate François Jacob wrote often about evolution as “tinkering” in which parts and processes alone or together in cells and organisms were co-opted for new functional purposes. Such behavior remains unexamined concerning how adaptive systems succeed in biology, supply networks, the economy, and beyond. In the presence of Unknown-Unknown events (Unk-Unks) that have no prior occurrences and are evident only in their realizations, the design of supply networks must allow for developing adaptive capabilities at the firm-level. When done right, such organic development in the supply network would mimic a biological phenomenon of tinkering and natural selection. We describe enabling such adaptive processes as jury rigging. We discuss how firms could design their supply networks and organize their supply network ex-ante that enables the network members to respond to Unk-Unks in an innovative way through jury rigging of their relationships. Development of such jury rigging capabilities requires integrative suppliers with deep embedded relationships, enabled through appropriate incentives that include incomplete contracts with the suppliers and sharing of unspecified decision rights.

 

Jury Rigging and Supply Network Design: Evolutionary “Tinkering” in the presence of Unknown-Unknowns
Stuart Kauffman, Surya D. Pathak, Pradyot K. Sen,
Thomas Choi

Journal of Supply Chain Management

doi: 10.1111/jscm.12146

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Logarithmic distributions prove that intrinsic learning is Hebbian.

In this paper, we document lognormal distributions for spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas.
The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears as a functional property that is present everywhere. 
Secondly, we created a generic neural model to show that Hebbian learning will create and maintain lognormal distributions.
We could prove with the model that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This settles a long-standing question about the type of plasticity exhibited by intrinsic excitability.

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The physics of data

The physics of data | Papers | Scoop.it

Physicists are accustomed to dealing with large datasets, yet they are fortunate in that the quality of their experimental data is very good. The onset of big data has led to an explosion of datasets with a far more complex structure — a development that requires new tools and a different mindset.

 

The physics of data
Jeff Byers
Nature Physics 13, 718–719 (2017) doi:10.1038/nphys4202

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Mapping the Curricular Structure and Contents of Network Science Courses

As network science has matured as a well-established field of research, there are already a number of courses on this topic developed and offered at various higher education institutions, often at postgraduate levels. In those courses, instructors have adopted different approaches with different focus areas and curricular designs. We collected information about 30 existing network science courses from various online sources, and analyzed the contents of their syllabi or course schedules. The topics and their curricular sequences were extracted from the course syllabi/schedules and represented as a directed weighted graph, which we call the topic network. Community detection in the topic network revealed seven topic clusters, which had a reasonable matching with the concept list previously generated by students and educators through the Network Literacy initiative. The minimum spanning tree of the topic network revealed typical flows of curricular contents, starting with examples of networks, moving onto random networks and small-world networks, then branching off to various subtopics from there. These results illustrate the current state of the consensus formed among the network science community on what should be taught about networks and how, which may also be informative for K-12 education and informal education.

 

Mapping the Curricular Structure and Contents of Network Science Courses
Hiroki Sayama

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An algorithm trained on emoji knows when you’re being sarcastic on Twitter

An algorithm trained on emoji knows when you’re being sarcastic on Twitter | Papers | Scoop.it

Detecting the sentiment of social-media posts is already useful for tracking attitudes toward brands and products, and for identifying signals that might indicate trends in the financial markets. But more accurately discerning the meaning of tweets and comments could help computers automatically spot and quash abuse and hate speech online. A deeper understanding of Twitter should also help academics understand how information and influence flows through the network. What’s more, as machines become smarter, the ability to sense emotion could become an important feature of human-to-machine communication.

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Diffusion Dynamics and Optimal Coupling in Directed Multiplex Networks

We study the dynamics of diffusion processes acting on directed multiplex networks, i.e., coupled multilayer networks where at least one layer consists of a directed graph. We reveal that directed multiplex networks may exhibit a faster diffusion at an intermediate degree of coupling than when the two layers are fully coupled. We use three simple multiplex examples and a real-world topology to illustrate the characteristics of the directed dynamics that give rise to a regime in which an optimal coupling exists. Given the ubiquity of both directed and multilayer networks in nature, our results could have important implications for the dynamics of multilevel complex systems towards optimality.

 

Diffusion Dynamics and Optimal Coupling in Directed Multiplex Networks
Alejandro Tejedor, Anthony Longjas, Efi Foufoula-Georgiou, Tryphon Georgiou, Yamir Moreno

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How Do People Differ? A Social Media Approach | NECSI

How Do People Differ? A Social Media Approach | NECSI | Papers | Scoop.it

Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract patterns in behavior and relate those patterns together in a high- dimensional picture. We use dimension reduction to analyze word usage in text data from the online discussion platform Reddit. We find that pronouns can be used to characterize the space of the two most prominent dimensions that capture the greatest differences in word usage, even though pronouns were not included in the determination of those dimensions. These patterns overlap with patterns of topics of discussion to reveal relationships between pronouns and topics that can describe the user population. This analysis corroborates findings from past research that have identified word use differences across populations and synthesizes them relative to one another. We believe this is a step toward understanding how differences between people are related to each other.

 

Vincent Wong, Yaneer Bar-Yam, How do people differ? A social media approach, New England Complex Systems Institute (July 1, 2017).

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Thinking in Eigenbehaviors as a Transdisciplinary Approach

Thinking in Eigenbehaviors as a Transdisciplinary Approach | Papers | Scoop.it

Context: By proposing to regard objects as “tokens for eigenbehavior,” von Foerster’s seminal paper opposes the intuitive subject-object dualism of traditional philosophy, which considers objects to be instances of an external world Problem: We argue that this proposal has two implications, one for epistemology and one for the demarcation between the natural sciences and the humanities. Method: Our arguments are based on insights gained in computational models and from reviewing the contributions to this special issue. Results: Epistemologically, von Foerster’s proposal suggests that what is called “reality” could be seen as an ensemble of eigenforms generated by the eigenbehavior that arises in the interaction of multiple dynamics. Regarding science, the contributions to this special issue demonstrate that the concept of eigenbehavior can be applied to a variety of disciplines from the formal and natural sciences to the humanities. Its universal applicability provides a strong argument for transdisciplinarity, and its emphasis on the observer points in the direction of an observer-inclusive science. Implications: Thinking in eigenbehavior may not only have implications for tearing down the barriers between sciences and humanities (although a common methodology based on von Foerster’s transdisciplinary approach is still to crystalize), a better understanding of eigenbehaviors may also have profound effects on our understanding of ourselves. This also opens the way to innovative behavior design/modification technologies.

 

Thinking in Eigenbehaviors as a Transdisciplinary Approach

Manfred Füllsack & Alexander Riegler

Constructivist Foundations 12(3): 239–245

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The scientists' apprentice (How AI is transforming science)

Big data has met its match. In field after field, the ability to collect data has exploded, overwhelming human insight and analysis. But the computing advances that helped deliver the data have also conjured powerful new tools for making sense of it all. In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on these mountains of data. Unlike earlier attempts at AI, such “deep learning” systems don’t need to be programmed with a human expert’s knowledge. Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets far larger and messier than human beings can cope with.

 

The scientists' apprentice
Tim Appenzeller
Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 16-17
DOI: 10.1126/science.357.6346.16

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The strange topology that is reshaping physics

The strange topology that is reshaping physics | Papers | Scoop.it
Topological effects might be hiding inside perfectly ordinary materials, waiting to reveal bizarre new particles or bolster quantum computing.
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When the Map Is Better Than the Territory

When the Map Is Better Than the Territory | Papers | Scoop.it

The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions may be useful to observers, they are at best a compressed description and at worse leave out critical information and causal relationships. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus and be more informative at a macroscale. That is, a macroscale description of a system (a map) can be more informative than a fully detailed microscale description of the system (the territory). This has been called “causal emergence.” While causal emergence may at first seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon’s discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different descriptions of those systems take advantage of that capacity to various degrees. For some systems, only macroscale descriptions use the full causal capacity. These macroscales can either be coarse-grains, or may leave variables and states out of the model (exogenous, or “black boxed”) in various ways, which can improve the efficacy and informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel’s capacity. The causal capacity of a system can approach the channel capacity as more and different kinds of macroscales are considered. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscale description

 

When the Map Is Better Than the Territory
Erik P. Hoel

Entropy 2017, 19(5), 188; doi:10.3390/e19050188

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