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A causal test of the strength of weak ties

Complexity Digest - Sun, 09/25/2022 - 12:23

15 Sep 2022
Vol 377, Issue 6612
pp. 1304-1310

The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600,000 new jobs were created. The results provided experimental causal evidence supporting the strength of weak ties and suggested three revisions to the theory. First, the strength of weak ties was nonlinear. Statistical analysis found an inverted U-shaped relationship between tie strength and job transmission such that weaker ties increased job transmission but only to a point, after which there were diminishing marginal returns to tie weakness. Second, weak ties measured by interaction intensity and the number of mutual connections displayed varying effects. Moderately weak ties (measured by mutual connections) and the weakest ties (measured by interaction intensity) created the most job mobility. Third, the strength of weak ties varied by industry. Whereas weak ties increased job mobility in more digital industries, strong ties increased job mobility in less digital industries.

Read the full article at: www.science.org

How Do Fireflies Flash in Sync? Studies Suggest a New Answer.

Complexity Digest - Sat, 09/24/2022 - 10:09

Field research suggests a new explanation for the synchronized flashing in fireflies and confirms that a novel form of “chimeric” synchrony occurs naturally.

Read the full article at: www.quantamagazine.org

Multidimensional Economic Complexity: How the Geography of Trade, Technology, and Research Explain Inclusive Green Growth

Complexity Digest - Fri, 09/23/2022 - 14:01

Viktor Stojkoski, Philipp Koch, César A. Hidalgo
To achieve inclusive green growth, countries need to consider a multiplicity of economic, social, and environmental factors. These are often captured by metrics of economic complexity derived from the geography of trade, thus missing key information on innovative activities. To bridge this gap, we combine trade data with data on patent applications and research publications to build models that significantly and robustly improve the ability of economic complexity metrics to explain international variations in inclusive green growth. We show that measures of complexity built on trade and patent data combine to explain future economic growth and income inequality and that countries that score high in all three metrics tend to exhibit lower emission intensities. These findings illustrate how the geography of trade, technology, and research combine to explain inclusive green growth.

Read the full article at: arxiv.org

Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project

Complexity Digest - Fri, 09/23/2022 - 12:27

Editors: Fabio Crestani, David E. Losada, Javier Parapar
Presents techniques for the early Detection of Mental Health Disorders by Social Media Monitoring

Recent research on eRisk which stands for Early Risk Prediction on the Internet

Presents the best results of the first five years of the eRisk project

Read the full article at: link.springer.com

You are where you eat: Effect of mobile food environments on fast food visits

Complexity Digest - Fri, 09/23/2022 - 12:03

Bernardo Garcia Bulle Bueno, Abigail L Horn, Brooke M Bell, Mohsen Bahrami, Burcin Bozkaya, Alex Pentland, Kayla De la Haye, Esteban Moro Egido

Poor diets, including those high in fast food, are a leading cause of morbidity and mortality. Exposure to low-quality food environments, such as ‘food swamps’ saturated with fast food outlets (FFO), is hypothesized to negatively impact diet and related disease. However, research linking such exposure to diet and health outcomes has generated mixed findings and led to unsuccessful policy interventions. A major research limitation has been a predominant focus on static food environments around the home, such as food deserts and swamps, and sparse availability of information on mobile food environments people are exposed to and food outlets they visit as they move throughout the day. In this work, we leverage population-scale mobility data to examine peoples’ visits to food outlets and FFO in and beyond their home neighborhoods and to evaluate how food choice is influenced by features of food environments people are exposed to in their daily routines vs. individual preference. Using a semi-causal framework and various natural experiments, we find that 10\% more FFO in an area increases the odds of people visiting a FFO by approximately 20\%. This strong influence of the food environment happens similarly during weekends and weekdays, is largely independent of individual income. Using our results, we investigate multiple intervention strategies to food environments to promote reduced FFO visits. We find that optimal locations for intervention are a combination of where i) the prevalence of FFO is the highest, ii) most decisions about food outlet visits are made, and most importantly, iii) visitors’ food decisions are most susceptible to the environment. Multi-level interventions at the individual behavior- and food environment-level that target areas combining these features could have 1.7x to 4x larger effects than traditional interventions that alter food swamps or food deserts.

Read the full article at: www.medrxiv.org

Virtual Course in Complexity @NECSI

Complexity Digest - Thu, 09/22/2022 - 14:53

Dates: October 18 – November 18, 2022

Registration Opens: September 19, 2022

More at: necsi.edu

Provenance of life: Chemical autonomous agents surviving through associative learning

Complexity Digest - Mon, 09/19/2022 - 15:52

Stuart Bartlett and David Louapre

Phys. Rev. E 106, 034401

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

Read the full article at: link.aps.org

Sketch of a novel approach to a neural model

Complexity Digest - Mon, 09/19/2022 - 10:57

Gabriele Scheler
In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical integration model of neural processing. We believe a new approach to neural modeling will benefit the 3rd wave of AI. The horizontal plane consists of an adaptive network of neurons connected by transmission links which generates spatio-temporal spike patterns. This fits with standard computational neuroscience approaches. Additionally for each individual neuron there is a vertical part consisting of internal adaptive parameters steering the external membrane-expressed parameters which are involved in neural transmission. Each neuron has a vertical modular system of parameters corresponding to (a) external parameters at the membrane layer, divided into compartments (spines, boutons) (b) internal parameters in the submembrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In such models, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are systematically separated, an important conceptual advance over synaptic weight models. We discuss the membrane-based (external) filtering and selection of outside signals for processing vs. signal loss by fast fluctuations and the neuron-internal computing strategies from intracellular protein signaling to the nucleus as the core system. We want to show that the individual neuron has an important role in the computation of signals and that many assumptions derived from the synaptic weight adjustment hypothesis of memory may not hold in a real brain. Not every transmission event leaves a trace and the neuron is a self-programming device, rather than passively determined by current input. Ultimately we strive to build a flexible memory system that processes facts and events automatically.

Read the full article at: arxiv.org

AI Algorithm Foresees Chaotic Tipping Points

Complexity Digest - Sat, 09/17/2022 - 11:53

A custom-built machine learning algorithm excels at the formidable task of predicting when a complex system is about to switch to a wildly different mode of behavior.

Read the full article at: www.quantamagazine.org

Sequential motifs in observed walks

Complexity Digest - Fri, 09/16/2022 - 17:04

Timothy LaRock, Ingo Scholtes, Tina Eliassi-Rad
Journal of Complex Networks, Volume 10, Issue 5, October 2022, cnac036,

The structure of complex networks can be characterized by counting and analysing network motifs. Motifs are small graph structures that occur repeatedly in a network, such as triangles or chains. Recent work has generalized motifs to temporal and dynamic network data. However, existing techniques do not generalize to sequential or trajectory data, which represent entities moving through the nodes of a network, such as passengers moving through transportation networks. The unit of observation in these data is fundamentally different since we analyse observations of trajectories (e.g. a trip from airport A to airport C through airport B), rather than independent observations of edges or snapshots of graphs over time. In this work, we define sequential motifs in trajectory data, which are small, directed and sequence-ordered graphs corresponding to patterns in observed sequences. We draw a connection between the counting and analysis of sequential motifs and Higher-Order Network (HON) models. We show that by mapping edges of a HON, specifically a kth-order DeBruijn graph, to sequential motifs, we can count and evaluate their importance in observed data. We test our methodology with two datasets: (1) passengers navigating an airport network and (2) people navigating the Wikipedia article network. We find that the most prevalent and important sequential motifs correspond to intuitive patterns of traversal in the real systems and show empirically that the heterogeneity of edge weights in an observed higher-order DeBruijn graph has implications for the distributions of sequential motifs we expect to see across our null models.

Read the full article at: academic.oup.com

Untangling the network effects of productivity and prominence among scientists

Complexity Digest - Fri, 09/16/2022 - 15:02

Weihua Li, Sam Zhang, Zhiming Zheng, Skyler J. Cranmer & Aaron Clauset
Nature Communications volume 13, Article number: 4907 (2022)

While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries.

Read the full article at: www.nature.com

Temporal, structural, and functional heterogeneities extend criticality and antifragility in random Boolean networks

Complexity Digest - Fri, 09/16/2022 - 11:27

Amahury Jafet López-Díaz, Fernanda Sánchez-Puig, Carlos Gershenson
Most models of complex systems have been homogeneous, i.e., all elements have the same properties (spatial, temporal, structural, functional). However, most natural systems are heterogeneous: few elements are more relevant, larger, stronger, or faster than others. In homogeneous systems, criticality — a balance between change and stability, order and chaos — is usually found for a very narrow region in the parameter space, close to a phase transition. Using random Boolean networks — a general model of discrete dynamical systems — we show that heterogeneity — in time, structure, and function — can broaden additively the parameter region where criticality is found. Moreover, parameter regions where antifragility is found are also increased with heterogeneity. However, maximum antifragility is found for particular parameters in homogeneous networks. Our work suggests that the “optimal” balance between homogeneity and heterogeneity is non-trivial, context-dependent, and in some cases, dynamic.

Read the full article at: arxiv.org

How Claude Shannon’s Concept of Entropy Quantifies Information

Complexity Digest - Thu, 09/15/2022 - 15:29

What’s a message, really? Claude Shannon recognized that the elemental ingredient is surprise.

Read the full article at: www.quantamagazine.org

Complexity Weekend: September 23-25, 2022

Complexity Digest - Wed, 09/14/2022 - 15:43

Registration is open for the September 2022 Cohort!
“Learn Complexity by Doing” with a diverse cohort of global Participants. Guidance from world-class Facilitators, in a community of practice that meets monthly. Meet future collaborations from all countries, fields, domains, backgrounds, perspectives, and levels of familiarity with Complexity Science – everyone here shares a desire to learn about Complex System behavior by helping to solve the world’s toughest problems together. Your perspective is needed!

Read the full article at: www.complexityweekend.com

What can we know about that which we cannot even imagine?

Complexity Digest - Wed, 09/14/2022 - 14:15

David H. Wolpert
In this essay I will consider a sequence of questions, ending with one about the breadth and depth of the epistemic limitations of our our science and mathematics. I will then suggest a possible way to circumvent such limitations. I begin by considering questions about the biological function of intelligence. This will lead into questions concerning human language, perhaps the most important cognitive prosthesis we have ever developed. While it is traditional to rhapsodize about the perceptual power provided by human language, I will emphasize how horribly limited – and therefore limiting – it is. This will lead to questions of whether human mathematics, being so deeply grounded in our language, is also deeply limited. I will then combine all of this into a partial, sort-of, sideways answer to the guiding question of this essay: what we can ever discern about all that we cannot even conceive of?

Read the full article at: arxiv.org

AMS :: Mathematic Research Communities: Complex Social Systems

Complexity Digest - Mon, 09/12/2022 - 14:47

The field of complex systems, which is mathematically broad and interdisciplinary, concerns the study of individual entities that interact to produce collective dynamics.

Complex social systems include the spread of memes on Twitter, the adoption and evolution of opinions during political discourse, and the formation of social movements that can affect both norms and policy. In all of these examples, the interactions of individuals, as well as how they react to external forces and shape their environment, lead to emergent features. Uncovering these interactions and determining how behaviors affect group-level dynamics has important societal implications. Complex social systems also inspire the development of new methods and draw on many different areas, including computational social science, political science, economics, legal scholarship, mathematical and statistical modeling, data and network analysis, dynamical systems, probability, and scientific computation.

The intersection of society, data, and computation in complex social systems creates an inherently interdisciplinary problem space, with a need for community-building between experts from a variety of backgrounds (including many who may not traditionally participate in mathematics research). This MRC aims to introduce early-career researchers to complex social systems and to foster new collaborations among mathematical, computational, and social scientists. MRC participants will come from a wide variety of mathematical and computational subfields and disciplinary traditions. We will explore some of the key methods and applications in complex systems, and we will engage in interdisciplinary research to attack open questions ranging from theoretical problems that are inspired by complex systems to data-analysis projects in social justice.

Please note that you may apply to more than one MRC conference if they match your research interests. A separate application is needed for each one. However, you can only be selected as a participant in one conference.

Applications are being accepted on MathPrograms, with a deadline of February 15, 2023.

More at: www.ams.org

Winter Workshop on Complex Systems 2023

Complexity Digest - Mon, 09/12/2022 - 12:53

The Winter Workshop on Complex Systems is a one-week workshop where young researchers from all over the world gather together for discussing complex systems.
The primary focus of the workshop is for participants to engage into novel research projects.
This is the 8th edition of the WWCS and it will be held in Amsterdam from January 30th to Feb 3rd 2023.

More at: wwcs2023.github.io


Complexity Digest - Mon, 09/12/2022 - 10:17

This book propagates a new way of thinking about managing our resources by integrating the perspectives of complex systems theory and social psychology. By resources, the authors mean objects, such as cell phones and cars, and human resources, such as family members, friends, and the small and large communities they belong to. As we all face the “replace or repair” dichotomy, readers will understand how to repair themselves, their relationships, and communities, accept the “new normal,” and contribute to repairing the world. The book is offered to Zoomers, growing up in a world where it seems everything is falling apart; people in their 30s and 40s, who are thinking about how to live a fulfilling life; people from the Boomers generation, who are thinking back on life and how to repair relationships. The Reader will enjoy the intellectual adventure of connecting the natural and social worlds and understanding the transition’s pathways from a “throwaway society” to a “repair society.

More at: link.springer.com

The big idea: why relationships are the key to existence

Complexity Digest - Sun, 09/11/2022 - 17:22

from Helgoland: Making Sense of the Quantum Revolution by Carlo Rovelli

From subatomic particles to human beings, interaction is what shapes reality (…)

Perhaps this is precisely what “properties” are: the effects of interactions. A good scientific theory, then, should not be about how things “are”, or what they “do”: it should be about how they affect one another. (…)

Reality is not a collection of things, it’s a network of processes. If this is correct, I think it comes with a lesson. We understand reality better if we think of it in terms of interactions, not individuals. 

Read the full article at: www.theguardian.com

Optimal transport and control of active drops

Complexity Digest - Fri, 09/09/2022 - 17:31

Suraj Shankar, Vidya Raju, and L. Mahadevan


Transportation, in its broadest sense, is an important task in many fields, including engineering, physics, biology, and economics, and a great deal is known about optimal and efficient strategies to move matter, energy, and information around. But can we craft similar optimal protocols to transport autonomously moving (active) matter, such as self-propelled drops or migrating cells? We develop an optimal control framework to transport active fluid drops with the least amount of energy dissipated, by manipulating the spatio-temporal profile of its internal active stresses. By combining numerical solutions and analytical insight, we uncover simple principles and characteristic trade-offs that govern the optimal policies, suggesting general strategies for optimal transportation in a wide variety of synthetic and biological active systems.

Read the full article at: www.pnas.org


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