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Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter

Complexity Digest - Wed, 07/21/2021 - 09:29

Thayer Alshaabi, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth, and Peter Sheridan Dodds

Science Advances  16 Jul 2021:

Vol. 7, no. 29, eabe6534
DOI: 10.1126/sciadv.abe6534

In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021. For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, generating frequencies for words, hashtags, handles, numerals, symbols, and emojis. We make the dataset available through an interactive time series viewer and as downloadable time series and daily distributions. Although Storywrangler leverages Twitter data, our method of tracking dynamic changes in n-grams can be extended to any temporally evolving corpus. Illustrating the instrument’s potential, we present example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest.

Read the full article at: advances.sciencemag.org

Structure of the Region-Technology Network as a Driver for Technological Innovation

Complexity Digest - Tue, 07/20/2021 - 15:16

Dion R. J. O’Neale, Shaun C. Hendy, and Demival Vasques Filho

Front. Big Data, 14 July 2021

Agglomeration and spillovers are key phenomena of technological innovation, driving regional economic growth. Here, we investigate these phenomena through technological outputs of over 4,000 regions spanning 42 countries, by analyzing more than 30 years of patent data (approximately 2.7 million patents) from the European Patent Office. We construct a bipartite network—based on revealed comparative advantage—linking geographic regions with areas of technology and compare its properties to those of artificial networks using a series of randomization strategies, to uncover the patterns of regional diversity and technological ubiquity. Our results show that the technological outputs of regions create nested patterns similar to those of ecological networks. These patterns suggest that regions need to dominate various technologies first (those allegedly less sophisticated), creating a diverse knowledge base, before subsequently developing less ubiquitous (and perhaps more sophisticated) technologies as a consequence of complementary knowledge that facilitates innovation. Finally, we create a map—the Patent Space Network—showing the interactions between technologies according to their regional presence. This network reveals how technology across industries co-appear to form several explicit clusters, which may aid future works on predicting technological innovation due to agglomeration and spillovers.

Read the full article at: www.frontiersin.org

The Crisis of Democracy in the Age of Cities conference

Complexity Digest - Tue, 07/20/2021 - 10:29

Tel Aviv University’s City Center is proud to invite you to the Crisis of Democracy in the Age of Cities,
​an international online conference.
The conference will last 3 consecutive days, from August 31st to September 2nd.
The Aim of the conference is to examine the links between the crisis of democracy with its tension between “non-democratic liberalism’ vs “non-liberal democracy’ and, the 21st century as the age of cities, in which the various properties of cities and urbanism dominate life. This, at the background of Industry 4.0, the Anthropocene, globalization and the COVID-19 pandemic.

Details at: en-urban.tau.ac.il

Melanie Mitchell Trains AI to Think With Analogies

Complexity Digest - Mon, 07/19/2021 - 15:18

Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.

Read the full article at: www.quantamagazine.org

The fundamental theorem of natural selection

Complexity Digest - Mon, 07/19/2021 - 12:50

John Baez

Suppose we have n different types of self-replicating entity, with the population P_i of the ith type changing at a rate equal to P_i times the fitness f_i of that type. Suppose the fitness f_i is any continuous function of all the populations P_1, \dots, P_n. Let p_i be the fraction of replicators that are of the ith type. Then p = (p_1, \dots, p_n) is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fisher’s fundamental theorem of natural selection. We compare it to Fisher’s original result as interpreted by Price, Ewens and Edwards.

Read the full article at: johncarlosbaez.wordpress.com

A network model of labor market dynamics

Complexity Digest - Sat, 07/17/2021 - 20:14

Oxford Mathematicians and Economists Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse-Díaz, François Lafond, and J. Doyne Farmer discuss their network model of labor market dynamics.

“Mathematics has explained many physical, chemical, and biological phenomena, but can it explain how the economy works? It is challenging because the economy is highly diverse, and ever-changing, with both short term fluctuations – it goes through recession and recovery periods – and long-term structural change – innovation transforms the scope and diversity of what we do.

Take the labor market, for example. Figure 1 shows what we call the occupational mobility network (1) – each node is an occupation, and the links show how likely it is that a worker in an occupation moves to another occupation. Clearly, there are many different occupations, and some occupational transitions are more likely than others. How can we model the dynamics of the labor market while taking this into account (click figure to enlarge)?

Read the full article at: www.maths.ox.ac.uk

Collective decision-making in living and artificial systems: editorial

Complexity Digest - Sat, 07/17/2021 - 12:56

Special issue on “Collective decision-making in living and artificial systems”
Swarm Intelligence, volume 15, issue 1–2 (2021)
Edited by A. Reina, E. Ferrante & G. Valentini

Collective decision-making is a fundamental cognitive process required for group coordination. Typically, this process requires individuals in a group to either reach a consensus on one of several available options or to distribute their workforce over different tasks. Similar collective decision-making processes can be found in a large number of systems, motivating a vast modeling effort across scientific disciplines. It can be observed across scales in a variety of animal groups, from unicellular organisms, to social insects, fish schools, and groups of mammals. In the social sciences, scientific domains such as econophysics and sociophysics emerged to investigate collective decisions in humans, deepening our understanding of the dynamics of economies and social policies. Neuroscientists also look at brains as a collection of neurons that, through numerous interactions, lead to rational decisions. Studies of collective decision-making in nature inspired the engineering of decentralized cyber-physical systems such as robot swarms and wireless sensor networks with the potential to create new emerging and disruptive technologies. Collective decision-making, ubiquitous across living and artificial collectives, can benefit from an interdisciplinary approach as apparently different systems may share similar mechanisms. With this special issue, we aim to push forward such an interdisciplinary approach by providing perspectives and insights from biology, information science, and engineering.

Read the full article at: link.springer.com

Mathematicians Prove Symmetry of Phase Transitions

Complexity Digest - Fri, 07/16/2021 - 20:09

A group of mathematicians has shown that at critical moments, a symmetry called rotational invariance is a universal property across many physical systems.

Read the full article at: www.quantamagazine.org

Topological synchronization: explosive transition and rhythmic phase

Complexity Digest - Tue, 07/13/2021 - 20:08

Lucille Calmon, Juan G. Restrepo, Joaquín J. Torres, Ginestra Bianconi
Topological signals defined on nodes, links and higher dimensional simplices define the dynamical state of a network or of a simplicial complex. As such, topological signals are attracting increasing attention in network theory, dynamical systems, signal processing and machine learning. Topological signals defined on the nodes are typically studied in network dynamics, while topological signals defined on links are much less explored. Here we investigate topological synchronization describing locally coupled topological signals defined on the nodes and on the links of a network. The dynamics of signals defined on the nodes is affected by a phase lag depending on the dynamical state of nearby links and vice versa, the dynamics of topological signals defined on the links is affected by a phase lag depending on the dynamical state of nearby nodes. We show that topological synchronization on a fully connected network is explosive and leads to a discontinuous forward transition and a continuous backward transition. The analytical investigation of the phase diagram provides an analytical expression for the critical threshold of the discontinuous explosive synchronization. The model also displays an exotic coherent synchronized phase, also called rhythmic phase, characterized by having non-stationary order parameters which can shed light on topological mechanisms for the emergence of brain rhythms.

Read the full article at: arxiv.org

‘Social’ Mitochondria, Whispering Between Cells, Influence Health

Complexity Digest - Tue, 07/13/2021 - 14:52

Mitochondria appear to communicate and cooperate with one another, both within and between cells. Biologists are only just beginning to understand how and why.

Read the full article at: www.quantamagazine.org

Scalability in Computing and Robotics

Complexity Digest - Tue, 07/13/2021 - 12:58

Heiko Hamann and Andreagiovanni Reina (2021) Scalability in Computing and Robotics. IEEE Transactions on Computers.
https://doi.org/10.1109/TC.2021.3089044
https://arxiv.org/abs/2006.04969

Efficient engineered systems require scalability. A scalable system has increasing performance with increasing system size. In an ideal situation, the increase in performance (e.g., speedup) corresponds to the number of units (e.g., processors, robots, users) that are added to the system (e.g., three times the number of processors in a computer would lead to three times faster computations). However, if multiple units work on the same task, then coordination among these units is required. This coordination can introduce overheads with an impact on system performance. The coordination costs can lead to sublinear improvement or even diminishing performance with increasing system size. However, there are also systems that implement efficient coordination and exploit collaboration of units to attain superlinear improvement. Modeling the scalability dynamics is key to understanding and engineering efficient systems. Known laws of scalability, such as Amdahl’s law, Gustafson’s law, and Gunther’s Universal Scalability Law, are minimalistic phenomenological models that explain a rich variety of system behaviors through concise equations. While useful to gain general insights, the phenomenological nature of these models may limit the understanding of the underlying dynamics, as they are detached from first principles that could explain coordination overheads or synergies among units. Through a decentralized system approach, we propose a general model based on generic interactions between units that is able to describe, as specific cases, any general pattern of scalability included by previously reported laws. The proposed general model of scalability has the advantage of being built on first principles, or at least on a microscopic description of interaction between units, and therefore has the potential to contribute to a better understanding of system behavior and scalability. We show that this generic model can be applied to a diverse set of systems, such as parallel supercomputers, robot swarms, or wireless sensor networks, therefore creating a unified view on interdisciplinary design for scalability.

Read the full article at: ieeexplore.ieee.org

Mindscape: Stephen Wolfram on Computation, Hypergraphs, and Fundamental Physics

Complexity Digest - Mon, 07/12/2021 - 20:11

It’s not easy, figuring out the fundamental laws of physics. It’s even harder when your chosen methodology is to essentially start from scratch, positing a simple underlying system and a simple set of rules for it, and hope that everything we know about the world somehow pops out. That’s the project being undertaken by Stephen Wolfram and his collaborators, who are working with a kind of discrete system called “hypergraphs.” We talk about what the basic ideas are, why one would choose this particular angle of attack on fundamental physics, and how ideas like quantum mechanics and general relativity might emerge from this simple framework.

Listen at: www.preposterousuniverse.com

Integrating explanation and prediction in computational social science

Complexity Digest - Sun, 07/11/2021 - 11:19

Jake M. Hofman, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani & Tal Yarkoni
Nature (2021)

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

Read the full article at: www.nature.com

Self-organization in natural swarms of Photinus carolinus synchronous fireflies

Complexity Digest - Sat, 07/10/2021 - 16:26

Raphaël Sarfati, Julie C. Hayes, and Orit Peleg

Science Advances 07 Jul 2021:
Vol. 7, no. 28, eabg9259

Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior.

Read the full article at: advances.sciencemag.org

Growing Urban Bicycle Networks

Complexity Digest - Fri, 07/09/2021 - 16:37

Michael Szell, Sayat Mimar, Tyler Perlman, Gourab Ghoshal, Roberta Sinatra
Cycling is a promising solution to unsustainable car-centric urban transport systems. However, prevailing bicycle network development follows a slow and piecewise process, without taking into account the structural complexity of transportation networks. Here we explore systematically the topological limitations of urban bicycle network development. For 62 cities we study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning: Cities must invest into bicycle networks with the right growth strategy, and persistently, to surpass a critical mass. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality. Growing networks from scratch makes our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal data requirements.

Read the full article at: arxiv.org

See also: http://growbike.net/ 

Cauliflower fractal forms arise from perturbations of floral gene networks

Complexity Digest - Thu, 07/08/2021 - 18:23

Eugenio Azpeitia, et al.

Science 09 Jul 2021:
Vol. 373, Issue 6551, pp. 192-197

Throughout development, plant meristems regularly produce organs in defined spiral, opposite, or whorl patterns. Cauliflowers present an unusual organ arrangement with a multitude of spirals nested over a wide range of scales. How such a fractal, self-similar organization emerges from developmental mechanisms has remained elusive. Combining experimental analyses in an Arabidopsis thaliana cauliflower-like mutant with modeling, we found that curd self-similarity arises because the meristems fail to form flowers but keep the “memory” of their transient passage in a floral state. Additional mutations affecting meristem growth can induce the production of conical structures reminiscent of the conspicuous fractal Romanesco shape. This study reveals how fractal-like forms may emerge from the combination of key, defined perturbations of floral developmental programs and growth dynamics.

Read the full article at: science.sciencemag.org

DESIGNING SYSTEMS BOTTOM UP: FACETS AND PROBLEMS

Complexity Digest - Tue, 07/06/2021 - 11:41

FRANK SCHWEITZER

Advances in Complex Systems Vol. 23, No. 07

Systems design utilizes top-down and bottom-up approaches to influence social or economic systems such that a desired outcome is obtained. We characterize different approaches like network controllability, network interventions, nudging and mechanism design and discuss the problems involved. We argue that systems design cannot be reduced to solving complex optimization problems.

Read the full article at: www.worldscientific.com

The design of self-organizing human–swarm intelligence

Complexity Digest - Mon, 07/05/2021 - 09:30

Jonas D Hasbach, Maren Bennewitz

Adaptive Behavior

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

Read the full article at: journals.sagepub.com

Measuring algorithmically infused societies

Complexity Digest - Sun, 07/04/2021 - 16:10

Claudia Wagner, Markus Strohmaier, Alexandra Olteanu, Emre Kıcıman, Noshir Contractor & Tina Eliassi-Rad 
Nature (2021)

It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of ‘algorithmically infused societies’—societies whose very fabric is co-shaped by algorithmic and human behaviour—raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories. Here we argue that tackling these challenges requires new social theories that account for the impact of algorithmic systems on social realities. To develop such theories, we need new methodologies for integrating data and measurements into theory construction. Given the scale at which measurements can be applied, we believe measurement models should be trustworthy, auditable and just. To achieve this, the development of measurements should be transparent and participatory, and include mechanisms to ensure measurement quality and identify possible harms. We argue that computational social scientists should rethink what aspects of algorithmically infused societies should be measured, how they should be measured, and the consequences of doing so.

Read the full article at: www.nature.com

High-frequency trading and networked markets

Complexity Digest - Sun, 07/04/2021 - 06:15

Federico Musciotto, Jyrki Piilo, and Rosario N. Mantegna

PNAS June 29, 2021 118 (26) e2015573118

Financial markets have undergone a deep reorganization during the last 20 y. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. The new stock market has changed the traditional ecology of market participants and market professionals, and financial markets have evolved into complex sociotechnical institutions characterized by a great heterogeneity in the time scales of market members’ interactions that cover more than eight orders of magnitude. We analyze three different datasets for two highly studied market venues recorded in 2004 to 2006, 2010 to 2011, and 2018. Using methods of complex network theory, we show that transactions between specific couples of market members are systematically and persistently overexpressed or underexpressed. Contemporary stock markets are therefore networked markets where liquidity provision of market members has statistically detectable preferences or avoidances with respect to some market members over time with a degree of persistence that can cover several months. We show a sizable increase in both the number and persistence of networked relationships between market members in most recent years and how technological and regulatory innovations affect the networked nature of the markets. Our study also shows that the portfolio of strategic trading decisions of high-frequency traders has evolved over the years, adding to the liquidity provision other market activities that consume market liquidity.

Read the full article at: www.pnas.org

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