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The Hidden Danger of Big Data

The Hidden Danger of Big Data | Papers | Scoop.it
With big data, we can multiply our options and filter out things we don’t want to see. But there is much to be said for making discoveries through pure serendipity: contingency and randomness often furnish the transformational or counterintuitive ideas that propel humanity forward.

 

The Hidden Danger of Big Data

Carlo Ratti & Dirk Helbing

https://www.project-syndicate.org/commentary/data-optimization-danger-by-carlo-ratti-and-dirk-helbing-2016-08

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Extended Intelligence

We propose a kind of Extended Intelligence (EI), understanding intelligence as a fundamentally distributed phenomenon. As we develop increasingly powerful tools to process information and network that processing, aren’t we just adding new pieces to the EI that every actor in the network is a part of?

 

Extended Intelligence

Joichi Ito

Journal of Design and Science

http://jods.mitpress.mit.edu/pub/extended-intelligence

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Brazil asks whether Zika acts alone to cause birth defects

Brazil asks whether Zika acts alone to cause birth defects | Papers | Scoop.it
Government researchers in Brazil are set to explore the country's peculiar distribution of Zika-linked microcephaly — babies born with abnormally small heads.

Zika virus has spread throughout Brazil, but extremely high rates of microcephaly have been reported only in the country's northeast. Although evidence suggests that Zika can cause microcephaly, the clustering pattern hints that other environmental, socio-economic or biological factors could be at play.

“We suspect that something more than Zika virus is causing the high intensity and severity of cases,” says Fatima Marinho, director of information and health analysis at Brazil’s ministry of health. If that turns out to be true, it could change researchers' assessment of the risk that Zika poses to pregnant women and their children.
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Joys of Noise

Joys of Noise | Papers | Scoop.it

In engineering, uncertainty is usually as welcome as sand in a salad. The development of digital technologies, from the alphabet to the DVD, has been driven in large part by the desire to eliminate random fluctuations, or noise, inherent in analog systems like speech or VHS tapes. But randomness also has a special ability to make some systems work better. Here are five cases where a little chaos is a critical part of the plan:

  • Stochastic Resonance
  • Cryptography
  • Genetic Engineering
  • Gambling
  • Computer Simulations

 

http://nautil.us/issue/38/noise/joys-of-noise-rp 

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Interdisciplinary research has consistently lower funding success

Interdisciplinary research is widely considered a hothouse for innovation, and the only plausible approach to complex problems such as climate change1, 2. One barrier to interdisciplinary research is the widespread perception that interdisciplinary projects are less likely to be funded than those with a narrower focus3, 4. However, this commonly held belief has been difficult to evaluate objectively, partly because of lack of a comparable, quantitative measure of degree of interdisciplinarity that can be applied to funding application data1. Here we compare the degree to which research proposals span disparate fields by using a biodiversity metric that captures the relative representation of different fields (balance) and their degree of difference (disparity). The Australian Research Council’s Discovery Programme provides an ideal test case, because a single annual nationwide competitive grants scheme covers fundamental research in all disciplines, including arts, humanities and sciences. Using data on all 18,476 proposals submitted to the scheme over 5 consecutive years, including successful and unsuccessful applications, we show that the greater the degree of interdisciplinarity, the lower the probability of being funded. The negative impact of interdisciplinarity is significant even when number of collaborators, primary research field and type of institution are taken into account. This is the first broad-scale quantitative assessment of success rates of interdisciplinary research proposals. The interdisciplinary distance metric allows efficient evaluation of trends in research funding, and could be used to identify proposals that require assessment strategies appropriate to interdisciplinary research5.

 

Interdisciplinary research has consistently lower funding success
Lindell Bromham, Russell Dinnage & Xia Hua

Nature 534, 684–687 (30 June 2016) http://dx.doi.org/10.1038/nature18315

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WHY OUR INNOVATION SYSTEM IS FAILING - and How to Change This

Our innovation system has terribly failed. It is well designed to support gradual improvements of our knowledge and technologies. But it does not support disruptive innovations well, which would create new qualities and functionalities, or question the basis of our established knowledge and routines. Moreover, our knowledge does not keep up anymore with the pace at which our world changes, and solutions to new problems often come with serious delays. Therefore, we need to re-invent innovation. In particularly, we must learn to create systems embracing collective intelligence that surpasses the intelligence of even the brightest individual and of powerful supercomputing solutions. This cannot be based on top-down nor majority decisions. Diversity is absolutely crucial for collective intelligence to work…

 

WHY OUR INNOVATION SYSTEM IS FAILING - and How to Change This

by Dirk Helbing

http://futurict.blogspot.mx/2016/08/why-our-innovation-system-is-failing_87.html

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Open Issues in Evolutionary Robotics

Open Issues in Evolutionary Robotics | Papers | Scoop.it

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

 

Open Issues in Evolutionary Robotics
Fernando Silva, Miguel Duarte, Luís Correia, Sancho Moura Oliveira, Anders Lyhne Christensen

Evolutionary Computation

Summer 2016, Vol. 24, No. 2, Pages 205-236
Posted Online June 13, 2016.
http://dx.doi.org/10.1162/EVCO_a_00172

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From Modular to Distributed Open Architectures: A Unified Decision Framework

This paper introduces a conceptual, yet quantifiable, architecture framework by extending the notion of system modularity in its broadest sense. Acknowledging that modularity is not a binary feature and comes in various types and levels, the proposed framework introduces higher levels of modularity that naturally incorporate decentralized architecture on the one hand and autonomy in agents and subsystems on the other. This makes the framework suitable for modularity decisions in Systems of Systems and for analyzing the impact of modularity on broader surrounding ecosystems. The stages of modularity in the proposed framework are naturally aligned with the level of variations and uncertainty in the system and its environment, a relationship that is central to the benefits of modularity. The conceptual framework is complemented with a decision layer that makes it suitable to be used as a computational architecture decision tool to determine the appropriate stage and level of modularity of a system, for a given profile of variations and uncertainties in its environment. We further argue that the fundamental systemic driving forces and trade-offs of moving from monolithic to distributed architecture are essentially similar to those for moving from integral to modular architectures. The spectrum, in conjunction with the decision layer, could guide system architects when selecting appropriate parameters and building a system-specific computational tool from a combination of existing tools and techniques. To demonstrate the applicability of the framework, a case for fractionated satellite systems based on a simplified demo of the DARPA F6 program is presented where the value of transition from a monolithic architecture to a fractionated architecture, as two consecutive levels of modularity in the proposed spectrum, is calculated and ranges of parameters where fractionation increases systems value are determined.

 

Heydari, B., Mosleh, M. and Dalili, K. (2016), From Modular to Distributed Open Architectures: A Unified Decision Framework. Syst Eng. doi: http://dx.doi.org/10.1002/sys.21348

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Decision-making without a brain: how an amoeboid organism solves the two-armed bandit

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems.

 

Decision-making without a brain: how an amoeboid organism solves the two-armed bandit
Chris R. Reid, Hannelore MacDonald, Richard P. Mann, James A. R. Marshall, Tanya Latty, Simon Garnier

Interface

June 2016
Volume 13, issue 119

http://dx.doi.org/10.1098/rsif.2016.0030

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Adaptive Computation: The Multidisciplinary Legacy of John H. Holland

Adaptive Computation: The Multidisciplinary Legacy of John H. Holland | Papers | Scoop.it

John Holland was unusual in his ability to absorb the essence of other disciplines, articulate grand overarching principles, and then back them up with computational mechanisms and mathematics. Unlike most researchers, Holland moved seamlessly among these three modes of thinking, developing models that were years ahead of their time. A close reading of his work reveals the antecedents of many ideas prevalent in machine learning today (such as reinforcement learning in non-Markovian environments and active learning). His seminal genetic algorithm spawned the field of evolutionary computation, and his insights and wisdom helped define what are today referred to as the "sciences of complexity."

 

Adaptive Computation: The Multidisciplinary Legacy of John H. Holland
By Stephanie Forrest, Melanie Mitchell
Communications of the ACM, Vol. 59 No. 8, Pages 58-63
http://dx.doi.org/10.1145/2964342 

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Models and people: An alternative view of the emergent properties of computational models

Computer models can help humans gain insight into the functioning of complex systems. Used for training, they can also help gain insight into the cognitive processes humans use to understand these systems. By influencing humans understanding (and consequent actions) computer models can thus generate an impact on both these actors and the very systems they are designed to simulate. When these systems also include humans, a number of self-referential relations thus emerge which can lead to very complex dynamics. This is particularly true when we explicitly acknowledge and model the existence of multiple conflicting representations of reality among different individuals. Given the increasing availability of computational devices, the use of computer models to support individual and shared decision making could potentially have implications far wider than the ones often discussed within the Information and Communication Technologies community in terms of computational power and network communication. We discuss some theoretical implications and describe some initial numerical simulations.

 

Models and people: An alternative view of the emergent properties of computational models
Fabio Boschetti

Complexity

Volume 21, Issue 6
July/August 2016
Pages 202–213

http://dx.doi.org/10.1002/cplx.21680

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Visualizing the “heartbeat” of a city with tweets

Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here, we describe the collective dynamics of New York City (NYC) and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of NYC, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal “heartbeat” of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to a central location one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis.

 

Visualizing the “heartbeat” of a city with tweets
Urbano França, 


Hiroki Sayama, 


Colin Mcswiggen, Roozbeh Daneshvar, 


Yaneer Bar-Yam

Complexity

Volume 21, Issue 6
July/August 2016
Pages 280–287

http://dx.doi.org/10.1002/cplx.21687

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Cultural evolution as a nonstationary stochastic process

We present an individual based model of cultural evolution, where interacting agents are coded by binary strings standing for strategies for action, blueprints for products or attitudes and beliefs. The model is patterned on an established model of biological evolution, the Tangled Nature Model (TNM), where a “tangle” of interactions between agents determines their reproductive success. In addition, our agents also have the ability to copy part of each other's strategy, a feature inspired by the Axelrod model of cultural diversity. Unlike the latter, but similarly to the TNM, the model dynamics goes through a series of metastable stages of increasing length, each characterized by mutually enforcing cultural patterns. These patterns are abruptly replaced by other patterns characteristic of the next metastable period. We analyze the time dependence of the population and diversity in the system, show how different cultures are formed and merge, and how their survival probability lacks, in the model, a finite average life-time. Finally, we use historical data on the number of car manufacturers after the introduction of the automobile to the market, to argue that our model can qualitatively reproduce the flurry of cultural activity which follows a disruptive innovation

 

Cultural evolution as a nonstationary stochastic process
Authors
Arwen E. Nicholson, 


Paolo Sibani

Complexity

Volume 21, Issue 6
July/August 2016
Pages 214–223

http://dx.doi.org/10.1002/cplx.21681 

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Higher-order organization of complex networks

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

 

Higher-order organization of complex networks
Austin R. Benson, David F. Gleich, Jure Leskovec

Science  08 Jul 2016:
Vol. 353, Issue 6295, pp. 163-166
http://dx.doi.org/10.1126/science.aad9029

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A multilayer approach to multiplexity and link prediction in online geo-social networks

Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.

 

A multilayer approach to multiplexity and link prediction in online geo-social networks
Hristova D, Noulas A, Brown C, Musolesi M, Mascolo C
EPJ Data Science 2016, 5 :24 (26 July 2016)

http://dx.doi.org/10.1140/epjds/s13688-016-0087-z

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The mature stage of capitalist development: Models, signs and policy implications

•We fit capitalist development into four stages of natural dissipative systems.
•We develop a system model of autocatalytic growth and development.
•We identify four endogenous and two exogenous negative feedbacks that constrain economic growth and contextualize them in a system model.
•We identify economic variables that would mark the transition to maturity in a selected group of economies that industrialized first and in OECD.
•Empirical findings suggest that observed groups of economies may have entered the mature stage of development.

 

The mature stage of capitalist development: Models, signs and policy implications
Igor Matutinović, Stanley N. Salthe, Robert E. Ulanowicz

Structural Change and Economic Dynamics
Volume 39, December 2016, Pages 17–30

http://dx.doi.org/10.1016/j.strueco.2016.06.001

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Open-Ended Evolution: Perspectives from the OEE Workshop in York

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1

 

Open-Ended Evolution: Perspectives from the OEE Workshop in York
Tim Taylor, Mark Bedau, Alastair Channon, et al.

Artificial Life

http://dx.doi.org/10.1162/ARTL_a_00210

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How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience

How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience | Papers | Scoop.it

Within an ecosystem, species interact with each other in many different ways, including predation, competition, and facilitation, and this can be modelled as a network of multiple interaction types. The variety of interaction types that link species to each other has long been recognized but has rarely been synthesized for entire multi-species ecosystems. Here, we leverage a unique marine ecological network that integrates thousands of trophic and non-trophic interactions. We show that, despite its multidimensional complexity, this ecological network collapses into a small set of “functional groups,” i.e., groups of species that resemble each other in the way they interact with others in their combined trophic and non-trophic interactions. These groups are taxonomically coherent and predictable by species attributes. Moreover, dynamic simulations suggest that the way the different interaction types relate to each other allows for higher species persistence and higher total biomass than is expected by chance alone, and that this tends to promote a higher robustness to extinctions. Our results will help to guide future empirical studies and to develop a more general theory of the dynamics of complex ecological systems.

 

Kéfi S, Miele V, Wieters EA, Navarrete SA, Berlow EL (2016) How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience. PLoS Biol 14(8): e1002527. http://dx.doi.org/10.1371/journal.pbio.1002527


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Eric L Berlow's curator insight, August 7, 2016 1:35 AM
Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities’ response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., “multiplex networks”), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full “entangled bank” of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
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Extracting Hidden Hierarchies in 3D Distribution Networks

Extracting Hidden Hierarchies in 3D Distribution Networks | Papers | Scoop.it

Complex networks like neural maps and the internet are characterized by a large number of connected nodes. A new algorithm shows how three-dimensional networks can be computationally simplified by tiling an abstract surface.

 

Extracting Hidden Hierarchies in 3D Distribution Networks
Carl D. Modes, Marcelo O. Magnasco, and Eleni Katifori
Phys. Rev. X 6, 031009 (2016)

http://dx.doi.org/10.1103/PhysRevX.6.031009

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Marcelo Errera's curator insight, August 16, 2016 9:44 AM
A great leap towards further understanding of complex networks. I see as next step the portrayal of the evolution of such networks in order to facilitate flow as Construal Law predicts.

Perhaps in this new framework things will be clearer.
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The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks

•Examines the effects of supply chains on productivity and innovation through knowledge diffusion.
•Ties with distant suppliers benefit more than ties with neighboring suppliers.
•Ties with neighboring clients benefit more than ties with distant clients.
•Density of a firm's ego network has a negative effect.
•These results suggest that access to diversified is important for knowledge diffusion.

 

The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks ☆
Yasuyuki Todoa, , , Petr Matousb, , Hiroyasu Inoue

Research Policy

http://dx.doi.org/10.1016/j.respol.2016.06.008

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Fractal dimension versus computational complexity

Complexity measures are designed to capture complex behavior and quantify *how* complex, according to that measure, that particular behavior is. It can be expected that different complexity measures from possibly entirely different fields are related to each other in a non-trivial fashion. Here we study small Turing machines (TMs) with two symbols, and two and three states. For any particular such machine τ and any particular input x we consider what we call the 'space-time' diagram which is the collection of consecutive tape configurations of the computation τ(x). In our setting, we define fractal dimension of a Turing machine as the limiting fractal dimension of the corresponding space-time diagram. It turns out that there is a very strong relation between the fractal dimension of a Turing machine of the above-specified type and its runtime complexity. In particular, a TM with three states and two colors runs in at most linear time iff its dimension is 2, and its dimension is 1 iff it runs in super-polynomial time and it uses polynomial space. If a TM runs in time O(x^n) we have empirically verified that the corresponding dimension is (n+1)/n, a result that we can only partially prove. We find the results presented here remarkable because they relate two completely different complexity measures: the geometrical fractal dimension on the one side versus the time complexity of a computation on the other side.

 

Fractal dimension versus computational complexity
Joost J. Joosten, Fernando Soler-Toscano, Hector Zenil

http://arxiv.org/abs/1309.1779

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Lucia Leao's curator insight, August 8, 2016 9:24 PM
fractal dimension

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On the Self-Organizing Origins of Agency

The question of agency and directedness in living systems has puzzled philosophers and scientists for centuries. What principles and mechanisms underlie the emergence of agency? Analysis and dynamical modeling of experiments on human infants suggest that the birth of agency is due to a eureka-like, pattern-forming phase transition in which the infant suddenly realizes it can make things happen in the world. The main mechanism involves positive feedback: when the baby's initially spontaneous movements cause the world to change, their perceived consequences have a sudden and sustained amplifying effect on the baby's further actions. The baby discovers itself as a causal agent. Some implications of this theory are discussed.

 

On the Self-Organizing Origins of Agency
J.A. Scott Kelso

Trends in Cognitive Science

Volume 20, Issue 7, p490–499, July 2016

http://dx.doi.org/10.1016/j.tics.2016.04.004

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Diversity of immune strategies explained by adaptation to pathogen statistics

Organisms possess many mechanisms for protecting themselves from pathogens. In addition to the well-studied innate and adaptive immune systems of vertebrates, recently discovered mechanisms such as CRISPR immunity in prokaryotes diversify the known modes of information processing used in immune protection. By classifying these different systems in terms of their rules of heritability and response to the environment, we propose a mathematical framework that recapitulates the observed natural diversity of immune systems. We show how the basic modes of immunity emerge as optimal strategies of a population adapting to a changing pathogenic environment. The proposed framework offers a unified view of immunity across species and helps rationalize the diversity of observed mechanisms.

 

Diversity of immune strategies explained by adaptation to pathogen statistics
Andreas Mayera, Thierry Morab,1, Olivier Rivoirec, and Aleksandra M. Walczak

PNAS vol. 113 no. 31: 8630–8635

http://dx.doi.org/10.1073/pnas.1600663113 

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The Memory of Science: Inflation, Myopia, and the Knowledge Network

Science is a growing system, exhibiting ~4% annual growth in publications and ~1.8% annual growth in the number of references per publication. Combined these trends correspond to a 12-year doubling period in the total supply of references, thereby challenging traditional methods of evaluating scientific production, from researchers to institutions. Against this background, we analyzed a citation network comprised of 837 million references produced by 32.6 million publications over the period 1965-2012, allowing for a temporal analysis of the `attention economy' in science. Unlike previous studies, we analyzed the entire probability distribution of reference ages - the time difference between a citing and cited paper - thereby capturing previously overlooked trends. Over this half-century period we observe a narrowing range of attention - both classic and recent literature are being cited increasingly less, pointing to the important role of socio-technical processes. To better understand the impact of exponential growth on the underlying knowledge network we develop a network-based model, featuring the redirection of scientific attention via publications' reference lists, and validate the model against several empirical benchmarks. We then use the model to test the causal impact of real paradigm shifts, thereby providing guidance for science policy analysis. In particular, we show how perturbations to the growth rate of scientific output affects the reference age distribution and the functionality of the vast science citation network as an aid for the search & retrieval of knowledge. In order to account for the inflation of science, our study points to the need for a systemic overhaul of the counting methods used to evaluate citation impact - especially in the case of evaluating science careers, which can span several decades and thus several doubling periods.

 

The Memory of Science: Inflation, Myopia, and the Knowledge Network
Raj K. Pan, Alexander M. Petersen, Fabio Pammolli, Santo Fortunato

http://arxiv.org/abs/1607.05606

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One to Many: Opportunities to Understanding Collective Behaviors in Urban Environments Through Individual’s Passively-Collected Locative Data

Walkable cities are of increased interest for urban planners and active transportation professionals, where a greater understanding of pedestrian behaviors is needed. This presentation discusses an approach for measuring spatiotemporal macro-behaviors of walking activity in urban environments using anonymized, individual, locative, passively-collected data recorded by popular physical activity mobile applications. With this data, we explore the characteristics of aggregated pedestrian activity within the physical and social milieu of the city at scale, with temporal detail, and in consideration of the infrastructural and urban characteristics influencing individual activity.

 

One to Many: Opportunities to Understanding Collective Behaviors in Urban Environments Through Individual’s Passively-Collected Locative Data
Anthony Vanky, Theodore Courtney, Santosh Verma, Carlo Ratti

Distributed, Ambient and Pervasive Interactions
Volume 9749 of the series Lecture Notes in Computer Science pp 482-493

http://link.springer.com/chapter/10.1007/978-3-319-39862-4_44

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