Complexity Digest

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The unmapped chemical complexity of our diet

Sat, 12/14/2019 - 15:11

Albert-László Barabási, Giulia Menichetti & Joseph Loscalzo 
Nature Food (2019)


Our understanding of how diet affects health is limited to 150 key nutritional components that are tracked and catalogued by the United States Department of Agriculture and other national databases. Although this knowledge has been transformative for health sciences, helping unveil the role of calories, sugar, fat, vitamins and other nutritional factors in the emergence of common diseases, these nutritional components represent only a small fraction of the more than 26,000 distinct, definable biochemicals present in our food—many of which have documented effects on health but remain unquantified in any systematic fashion across different individual foods. Using new advances such as machine learning, a high-resolution library of these biochemicals could enable the systematic study of the full biochemical spectrum of our diets, opening new avenues for understanding the composition of what we eat, and how it affects health and disease.


Survey: AI adoption proves its worth, but few scale impact

Fri, 12/13/2019 - 13:07

Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers with AI capabilities show the way.


Editorial: Novel Technological and Methodological Tools for the Understanding of Collective Behaviors

Thu, 12/12/2019 - 06:24

Elio Tuci1, Vito Trianni, Andrew King and Simon Garnier

Front. Robot. AI, 10 December 2019


The social processes that give rise to coordinated actions of a group of agents and the emergence of global structures—referred to as collective behaviors—are observed in a range of biological and artificial systems. Collective behavior research, therefore, focuses upon a range of different phenomena with the common goal of understanding the dynamics of emergent group level responses, and has resulted in a burgeoning, diverse, and interdisciplinary research community.

Studying collective behaviors in biological and artificial systems is particularly challenging because of their intrinsic complexity, requiring novel approaches that can help unraveling these systems in order to explain how and why certain patterns are produced and maintained. This Research Topic brings together a collection of studies that focus on technological and methodological tools that can support the understanding of collective behaviors. The contributions included within the Research Topic can be broadly categorized as: (i) Review Articles, (ii) Tools and Technologies, and (iii) Empirical Studies.

Our goal is to facilitate the dissemination of ideas, theories, and methods among scientists that share an interest on the study of collective behavior in all its diverse manifestations. It is our hope that, together, this Research Topic and contributions may afford a more complete understanding of the nature of proximate and ultimate causes of collective behaviors in biological systems, and provide opportunity to generate a theoretical framework to engineer robust, resilient, and effective technologies, such as multi-robot systems, smart grids, and sensor networks.


On cycling risk and discomfort: urban safety mapping and bike route recommendations

Tue, 12/10/2019 - 15:34

David Castells-Graells, Christopher Salahub, Evangelos Pournaras



Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse spectrum of sources to characterise geolocated bike accident severity and, ultimately, study cycling risk and discomfort. Kernel density estimation generates a continuous, empirical, spatial risk estimate which is mapped in a case study of Zürich city. The roles of weather, time, accident type, and severity are illustrated. A predominance of self-caused accidents motivates an open-source software artifact for personalized route recommendations. This software is used to collect open baseline route data that are compared with alternative routes minimizing risk and discomfort. These contributions have the potential to provide invaluable infrastructure improvement insights to urban planners, and may also improve the awareness of risk in the urban environment among experienced and novice cyclists alike.


Helping machines to perceive laws of physics by themselves

Tue, 12/10/2019 - 11:27

ADEPT, an artificial intelligence model developed by MIT researchers, demonstrates an understanding of some basic “intuitive physics” by registering a surprise signal when objects in a scene violate assumed reality, similarly to how human infants and adults would register surprise.


We often think of artificial intelligence as a tool for automating certain tasks. But it turns out that the technology could also help give us a better understanding of ourselves. At least that’s what a team of researchers at the Massachusetts Institute of Technology (MIT) think they’ll be able to do with their new AI model.


Dubbed ADEPT, the system is able to, like a human being, understand some laws of physics intuitively. It can look at an object in a video, predict how it should act based on what it knows of the laws of physics and then register surprise if what it was looking at subsequently vanishes or teleports. The team behind ADEPT say their model will allow other researchers to create smarter AIs in the future, as well give us a better understanding of how infants understand the world around them.


"By the time infants are three months old, they have some notion that objects don’t wink in and out of existence, and can’t move through each other or teleport," said Kevin A. Smith, one of the researchers that created ADEPT. "We wanted to capture and formalize that knowledge to build infant cognition into artificial-intelligence agents. We’re now getting near human-like in the way models can pick apart basic implausible or plausible scenes."


ADEPT depends on two modules to do what it does. The first examines an object, determining its shape, pose and velocity. What’s interesting about this module is that it doesn’t get caught up in details. It only looks at the approximate geometry of something, rather than analyzing every facet of it, before it moves onto the next step. This was by design, according to the ADEPT team; it allows the system to predict the movement of a variety of different objects, not just ones it was trained to understand. Moreover, it’s an aspect of the system’s design that makes it similar to infants. Like ADEPT, it turns out that children don’t care much about the specific physical properties of something when they’re thinking about how it may move.


The second module is a physics system. It shares similarities with the software video game developers employ to replicate real-world physics in their games. It takes the data captured by the graphics module and simulates how an object should act based on the laws of physics. Once it has a couple of predicted outcomes, it will compare those against the next frames of a video. If it notices a discrepancy in what it thought would happen with what actually occurred, it will send out a signal. The stronger the signal, the more surprised it was by what just happened. What’s interesting about ADEPT is that its level of surprise matched those of humans who were shown the same set of videos.


Moving forward, the team says they want to further explore how young children see the world, and incorporate those findings into their model. "We want to see what else needs to be built in to understand the world more like infants, and formalize what we know about psychology to build better AI agents," Smith said.


Understanding and reducing the spread of misinformation online

Tue, 12/10/2019 - 10:20

Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio Arechar, Dean Eckles, David Rand


The spread of false and misleading news on social media is of great societal concern. Why do people share such content, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. We argue against a “post-truth” interpretation, whereby people deliberately share false content because it furthers their political agenda. Instead, we propose that the problem is simply distraction: most people do not want to spread misinformation, but are distracted from accuracy by other salient motives when choosing what to share. Indeed, when directly asked, most participants say it is important to only share accurate news. Accordingly, across three survey experiments (total N=2775) and an experiment on Twitter in which we messaged N=5,482 users who had previously shared news from misleading websites, we find that subtly inducing people to think about the concept of accuracy increases the quality of the news they share. Together, these results challenge the popular post-truth narrative. Instead, they suggest that many people are capable of detecting low-quality news content, but nonetheless share such content online because social media is not conducive to thinking analytically about truth and accuracy. Furthermore, our results translate directly into a scalable anti-misinformation intervention that is easily implementable by social media platforms.


Modeling somatic computation with non-neural bioelectric networks

Tue, 12/10/2019 - 08:20

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.


Dynamical Inference of Simple Heteroclinic Networks

Tue, 12/10/2019 - 06:17

Maximilian Voit and Hildegard Meyer-Ortmanns

Front. Appl. Math. Stat., 10 December 2019


Heteroclinic networks are structures in phase space that consist of multiple saddle fixed points as nodes, connected by heteroclinic orbits as edges. They provide a promising candidate attractor to generate reproducible sequential series of metastable states. While from an engineering point of view it is known how to construct heteroclinic networks to achieve certain dynamics, a data based approach for the inference of heteroclinic dynamics is still missing. Here, we present a method by which a template system dynamically learns to mimic an input sequence of metastable states. To this end, the template is unidirectionally, linearly coupled to the input in a master-slave fashion, so that it is forced to follow the same sequence. Simultaneously, its eigenvalues are adapted to minimize the difference of template dynamics and input sequence. Hence, after the learning procedure, the trained template constitutes a model with dynamics that are most similar to the training data. We demonstrate the performance of this method at various examples, including dynamics that differ from the template, as well as a regular and a random heteroclinic network. In all cases the topology of the heteroclinic network is recovered precisely, as are most eigenvalues. Our approach may thus be applied to infer the topology and the connection strength of a heteroclinic network from data in a dynamical fashion. Moreover, it may serve as a model for learning in systems of winnerless competition.


Transitivity and degree assortativity explained: The bipartite structure of social networks

Mon, 12/09/2019 - 11:26

Demival Vasques Filho, Dion R. J. O’Neale


Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news", innovation, and others, are highly dependent on the structure of the social network on which they occur. However, questions on why most social networks present some particular structural features, namely high levels of
transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network — together with the presence of cycles of length four and six — explains the observed levels of transitivity and degree assortativity in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.


Digital Fingerprints of Cognitive Reflection

Mon, 12/09/2019 - 08:04

Mohsen Mosleh, Gordon Pennycook, Antonio Arechar, David Rand

Social media is playing an increasingly large role in everyday life. Thus, it is of both scientific and practical interest to understand behavior on social media platforms. Furthermore, social media provides a unique window for social scientists to deepen our understanding of the human mind. Here we investigate the relationship between individual differences in cognitive reflection and behavior on Twitter in a sample of large N = 1,953 users recruited via Prolific Academic. In doing so, we differentiate between two competing accounts of human information processing: an “intuitionist” account whereby reflection plays little role in daily life, and a “reflectionist” account whereby reflection (and, in particular, overriding intuitive responses) does play an important role. We found that people who score higher on the Cognitive Reflection Test (CRT) – a widely used measure of reflective thinking – were more discerning in their social media use: They followed more selectively, shared news content from more reliable sources, and tweeted about weightier subjects. Furthermore, a network analysis indicated that the phenomenon of echo chambers, in which discourse is more likely with like-minded others, is not limited to politics: we observe “cognitive echo chambers” in which people low on cognitive reflection tend to follow the same set of accounts. Our results help to illuminate the drivers of behavior on social media platforms, and challenge intuitionist notions that reflective thinking is unimportant for everyday judgment and decision-making.


A Genetic Model of the Connectome

Sun, 12/08/2019 - 23:02

The connectomes of organisms of the same species show remarkable architectural and often local wiring similarity, raising the question: where and how is neuronal connectivity encoded? Here, we start from the hypothesis that the genetic identity of neurons guides synapse and gap-junction formation and show that such genetically driven wiring predicts the existence of specific biclique motifs in the connectome. We identify a family of large, statistically significant biclique subgraphs in the connectomes of three species and show that within many of the observed bicliques the neurons share statistically significant expression patterns and morphological characteristics, supporting our expectation of common genetic factors that drive the synapse formation within these subgraphs. The proposed connectome model offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome, offering experimentally falsifiable predictions on the genetic factors that drive the formation of individual neuronal circuits.


A Genetic Model of the Connectome
Dániel L. Barabási, Albert-László Barabási



Machine learning and the physical sciences

Sun, 12/08/2019 - 16:45

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.


Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová
Rev. Mod. Phys. 91, 045002


International Journal of Complexity in Education

Fri, 12/06/2019 - 13:41

The International Journal of Complexity in Education, IJCE, is a new forum which publishes articles that are concerned with the application of complexity theory and related models in the field of education. The journal invites empirical papers, as well as theoretical and methodological contributions, literature reviews and short research reports. We also welcome book reviews. In each of these instances, however, the linkage between complexity theory and education needs to be made explicit, and it should be clear how the contribution adds to existing

knowledge in that area. IJCE is a peer reviewed open source journal. There are no publication charges.


The inaugural issue is planned for April 2020.
To be considered for inclusion in this first issue, send full papers by December 31, 2019.


How to Decide: Simple Tools for Making Better Choices: Annie Duke

Thu, 12/05/2019 - 18:14

Through a blend of compelling exercises, illustrations, and stories, the bestselling author of Thinking in Bets will train you to combat your own biases, address your weaknesses, and help you become a better and more confident decision-maker.


What do you do when you’re faced with a big decision? If you’re like most people, you probably make a pro and con list, spend a lot of time obsessing about decisions that didn’t work out, get caught in analysis paralysis, endlessly seek other people’s opinions to find just that little bit of extra information that might make you sure, and finally go with your gut.

What if there was a better way to make quality decisions so you can think clearly, feel more confident, second-guess yourself less, and ultimately be more decisive and be more productive?

Making good decisions doesn’t have to be a series of endless guesswork. Rather, it’s a teachable skill that anyone can sharpen. In How to Decide, bestselling author Annie Duke and former professional poker player lays out a series of tools anyone can use to make better decisions. You’ll learn:

• To identify and dismantle hidden biases.
• To extract the highest quality feedback from those whose advice you seek.
• To more accurately identify the influence of luck in the outcome of your decisions.
• When to decide fast, when to decide slow, and when to decide in advance.
• To make decisions that more effectively help you to realize your goals and live your values.

Through practical exercises and engaging thought experiments, this book helps you analyze key decisions you’ve made in the past and troubleshoot those you’re making in the future. Whether you’re picking investments, evaluating a job offer, or trying to figure out your romantic life, this book is the key to happier outcomes and fewer regrets.


Cybernetics for the Newtonian diehard

Thu, 12/05/2019 - 16:10

This is a quick tour through Ashby’s Introduction to Cybernetics. Leads to the Cybernetic paradigm.


Guiding the Self-organization of Cyber-Physical Systems

Tue, 12/03/2019 - 18:57

Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.


Guiding the Self-organization of Cyber-Physical Systems
Carlos Gershenson


Complex Networks 2019 Conference Proceedings

Tue, 12/03/2019 - 15:41

The International Conference on Complex Networks and their Applications aims at bringing together researchers from different scientific communities working on areas related to complex networks.


Escaping optimization traps: the role of cultural adaptation and cultural exaptation in facilitating open-ended cumulative dynamics

Tue, 12/03/2019 - 14:13

Explaining the origins of cumulative culture, and how it is maintained over long timescales, constitutes a challenge for theories of cultural evolution. Previous theoretical work has emphasized two fundamental causal processes: cultural adaptation (where technologies are refined towards a functional objective) and cultural exaptation (the repurposing of existing technologies towards a new functional goal). Yet, despite the prominence of cultural exaptation in theoretical explanations, this process is often absent from models and experiments of cumulative culture. Using an agent-based model, where agents attempt to solve problems in a high-dimensional problem space, the current paper investigates the relationship between cultural adaptation and cultural exaptation and produces three major findings. First, cultural dynamics often end up in optimization traps: here, the process of optimization causes the dynamics of change to cease, with populations entering a state of equilibrium. Second, escaping these optimization traps requires cultural dynamics to explore the problem space rapidly enough to create a moving target for optimization. This results in a positive feedback loop of open-ended growth in both the diversity and complexity of cultural solutions. Finally, the results helped delineate the roles played by social and asocial mechanisms: asocial mechanisms of innovation drive the emergence of cumulative culture and social mechanisms of within-group transmission help maintain these dynamics over long timescales.


Escaping optimization traps: the role of cultural adaptation and cultural exaptation in facilitating open-ended cumulative dynamics

James Winters
Palgrave Communications volume 5, Article number: 149 (2019)


Climate tipping points — too risky to bet against

Sun, 12/01/2019 - 23:26

Politicians, economists and even some natural scientists have tended to assume that tipping points in the Earth system — such as the loss of the Amazon rainforest or the West Antarctic ice sheet — are of low probability and little understood. Yet evidence is mounting that these events could be more likely than was thought, have high impacts and are interconnected across different biophysical systems, potentially committing the world to long-term irreversible changes.

Here we summarize evidence on the threat of exceeding tipping points, identify knowledge gaps and suggest how these should be plugged. We explore the effects of such large-scale changes, how quickly they might unfold and whether we still have any control over them.

In our view, the consideration of tipping points helps to define that we are in a climate emergency and strengthens this year’s chorus of calls for urgent climate action — from schoolchildren to scientists, cities and countries.


Introduction to Artificial Life for People who Like AI

Sat, 11/30/2019 - 23:14

Artificial Life, often shortened as ALife. What is your first thought when reading those words? A brand of T-shirts? A Greg Egan novel?

For me and hundreds of ALifers, ALife is the bottom-up scientific study of the fundamental principles of life. Just as Artificial Intelligence researchers ponder the nature of intelligence by trying to build intelligent systems from scratch, ALife researchers investigate the nature of “life” by trying to build living systems from scratch.