Complexity Digest

Subscribe to Complexity Digest feed Complexity Digest
Networking the complexity community since 1999
Updated: 2 hours 51 min ago

Crowdsourcing Moral Machines

Sat, 03/07/2020 - 14:16

Edmond Awad, Sohan Dsouza, Jean-François Bonnefon, Azim Shariff, Iyad Rahwan
Communications of the ACM, March 2020, Vol. 63 No. 3, Pages 48-55


Robots and other artificial intelligence (AI) systems are transitioning from performing well-defined tasks in closed environments to becoming significant physical actors in the real world. No longer confined within the walls of factories, robots will permeate the urban environment, moving people and goods around, and performing tasks alongside humans. Perhaps the most striking example of this transition is the imminent rise of automated vehicles (AVs). AVs promise numerous social and economic advantages. They are expected to increase the efficiency of transportation, and free up millions of person-hours of productivity. Even more importantly, they promise to drastically reduce the number of deaths and injuries from traffic accidents.12,30 Indeed, AVs are arguably the first human-made artifact to make autonomous decisions with potential life-and-death consequences on a broad scale. This marks a qualitative shift in the consequences of design choices made by engineers.


Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications, by Nassim Nicholas Taleb

Sat, 03/07/2020 - 14:08

The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible.
Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under of the "laws of the medium numbers" –which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence.
A few examples:
+ The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods.
+ The "empirical distribution" is rarely empirical.
+ Parameter uncertainty has compounding effects on statistical metrics.
+ Dimension reduction (principal components) fails.
+ Inequality estimators (GINI or quantile contributions) are not additive and produce wrong results.
+ Many "biases" found in psychology become entirely rational under more sophisticated probability distributions
+ Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions.
This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.


Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems

Sat, 03/07/2020 - 12:13

P. S. Dodds, J. R. Minot, M. V. Arnold, T. Alshaabi, J. L. Adams, D. R. Dewhurst, T. J. Gray, M. R. Frank, A. J. Reagan, C. M. Danforth

Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Comparisons of component size distributions for two complex systems—or a system with itself at two different time points—generally employ information-theoretic instruments, such as Jensen-Shannon divergence. We argue that these methods lack transparency and adjustability, and should not be applied when component probabilities are non-sensible or are problematic to estimate. Here, we introduce `allotaxonometry’ along with `rank-turbulence divergence’, a tunable instrument for comparing any two (Zipfian) ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.


How Computation Is Helping Unravel the Dynamics of Morphogenesis

Fri, 03/06/2020 - 18:04

David Pastor-Escuredo and Juan C. del Álamo

Front. Phys.


The growing availability of imaging data, calculation power, and algorithm sophistication are transforming the study of morphogenesis into a computation-driven discipline. In parallel, it is accepted that mechanics plays a role in many of the processes determining the cell fate map, providing further opportunities for modeling and simulation. We provide a perspective of this integrative field, discussing recent advances and outstanding challenges to understand the determination of the fate map. At the basis, high-resolution microscopy and image processing provide digital representations of embryos that facilitate quantifying their mechanics with computational methods. Moreover, innovations in in-vivo sensing and tissue manipulation can now characterize cell-scale processes to feed larger-scale representations. A variety of mechanical formalisms have been proposed to model cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature of embryonic tissue and its spatio-temporal heterogeneity. Also, the increasing complexity and variety of implementations make it difficult to harmonize and cross-validate models. The solution to these challenges will likely require integrating novel in vivo measurements of embryonic biomechanics into the models. Machine Learning has great potential to classify spatio-temporally connected groups of cells with similar dynamics. Emerging Deep Learning architectures facilitate the discovery of causal links and are becoming transparent and interpretable. We anticipate these new tools will lead to multi-scale models with the necessary accuracy and flexibility to formulate hypotheses for in-vivo and in-silico testing. These methods have promising applications for tissue engineering, identification of therapeutic targets, and synthetic life.


Disturbance in human gut microbiota networks by parasites and its implications in the incidence of depression

Fri, 03/06/2020 - 16:03

Elvia Ramírez-Carrillo, Osiris Gaona, Javier Nieto, Andrés Sánchez-Quinto, Daniel Cerqueda-García, Luisa I. Falcón, Olga A. Rojas-Ramos & Isaac González-Santoyo
Scientific Reports volume 10, Article number: 3680 (2020)


If you think you are in control of your behavior, think again. Evidence suggests that behavioral modifications, as development and persistence of depression, maybe the consequence of a complex network of communication between macro and micro-organisms capable of modifying the physiological axis of the host. Some parasites cause significant nutritional deficiencies for the host and impair the effectiveness of cognitive processes such as memory, teaching or non-verbal intelligence. Bacterial communities mediate the establishment of parasites and vice versa but this complexity approach remains little explored. We study the gut microbiota-parasite interactions using novel techniques of network analysis using data of individuals from two indigenous communities in Guerrero, Mexico. Our results suggest that Ascaris lumbricoides induce a gut microbiota perturbation affecting its network properties and also subnetworks of key species related to depression, translating in a loss of emergence. Studying these network properties changes is particularly important because recent research has shown that human health is characterized by a dynamic trade-off between emergence and self-organization, called criticality. Emergence allows the systems to generate novel information meanwhile self-organization is related to the system’s order and structure. In this way, the loss of emergence means a depart from criticality and ultimately loss of health.


Living robots

Thu, 03/05/2020 - 20:07

Philip Ball 
Nature Materials volume 19, page 265(2020)


The original ‘robots’, described in the 1921 play R.U.R. by the Czech writer Karel Čapek (the word is Czech for ‘labourer’) were not made from steel and controlled by electronics, but were fleshy and autonomous. Čapek’s manufacturing process, in which organs and other parts were made from vats of flesh-like dough and assembled into bodies, took inspiration from the emerging technology of in vivo tissue culture. It blurred the boundaries between engineering and biotechnology in a way that seemed far beyond the technologies of the time.


The results now reported by Kriegman et al. make this vision seem almost unnervingly prescient1. They describe ‘reconfigurable organisms’ made from living cells assembled into conglomerates about a millimetre across with arbitrary shapes, which are designed in silico for particular functions such as locomotion. These structures have been dubbed xenobots — which might be given the literal and apt interpretation of ‘strange robots’, although here ‘xeno’ comes from the use of embryonic stem cells of the African clawed frog Xenopus laevis as the construction material.


Synthetic ablations in the C. elegans nervous system

Thu, 03/05/2020 - 18:05

Emma K. Towlson and Albert-László Barabási

Network Neuroscience


"Synthetic lethality" in cell biology is an extreme example of the effects of higher order genetic interactions: The simultaneous knockout of two or more individually nonessential genes leads to cell death. We define a neural analog to this concept in relation to the locomotor response to gentle touch in C. elegans. Two or more neurons are synthetic essential if individually they are not required for this behavior, yet their combination is. We employ a network control approach to systematically assess all pairs and triplets of neurons by their effect on body wall muscle controllability, and find that only surprisingly small sets of neurons are synthetic essential. They are highly localized in the nervous system and predicted to affect control over specific sets of muscles.


Networks and long-range mobility in cities: A study of more than one billion taxi trips in New York City

Thu, 03/05/2020 - 14:05

A. P. Riascos & José L. Mateos 
Scientific Reports volume 10, Article number: 4022 (2020)


We analyze the massive data set of more than one billion taxi trips in New York City, from January 2009 to December 2015. With these records of seven years, we generate an origin-destination matrix that has information of a vast number of trips. The mobility and flow of taxis can be described as a directed weighted network that connects different zones of high demand for taxis. This network has in and out degrees that follow a stretched exponential and a power law with an exponential cutoff distributions, respectively. Using the origin-destination matrix, we obtain a rank, called "OD rank”, analogous to the page rank of Google, that gives the more relevant places in New York City in terms of taxi trips. We introduced a model that captures the local and global dynamics that agrees with the data. Considering the taxi trips as a proxy of human mobility in cities, it might be possible that the long-range mobility found for New York City would be a general feature in other large cities around the world.


Elites, communities and the limited benefits of mentorship in electronic music

Tue, 03/03/2020 - 09:18

Milán Janosov, Federico Musciotto, Federico Battiston & Gerardo Iñiguez 
Scientific Reports volume 10, Article number: 3136 (2020)


While the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry. These groups of artists are temporally segregated, sequentially formed around leading musicians, and represent changes in musical genres. We show that a major driving force behind the formation of music communities is mentorship: around half of musicians entering the top 100 have been mentored by current leading figures before they entered the list. We also find that mentees are unlikely to break into the top 20, yet have much higher expected best ranks than those who were not mentored. This implies that mentorship helps rising talents, but becoming an all-time star requires more. Our results provide insights into the intertwined roles of success and collaboration in electronic music, highlighting the mechanisms shaping the formation and landscape of artistic elites in electronic music.


The Collective Computation of Reality in Nature and Society

Sun, 02/23/2020 - 08:46

The first computers were not invented by humans but by nature. The mantra of complexity science — that complexity arises from interactions among simple components — is wrong. The parts—whether cells, neurons, bees, or humans—are often wonderfully complex themselves but operate under many constraints and are prone to failure and myopia and, consequently, errors in information processing that can lead to a profound misunderstanding of the nature of reality. In this public lecture, Jessica Flack will discuss how nature computes. She will build on the above points to argue collective computation—computation by the parts together—evolved as a solution to imperfect information processing, sometimes resulting in recovery of the “ground truth out there in the world” and sometimes resulting in a collectively constructed reality that takes on a life and meaning of its own. Flack will also discuss how an understanding of computation in nature challenges us to broaden our understanding of computation’s theoretical foundations.

All things are words belonging to that language
In which Someone or Something, night and day,
Writes down the infinite babble that is, per se,
The history of the world. And in that hodgepodge
Both Rome and Carthage, he and you and I,
My life that I don’t grasp, this painful load
Of being riddle, randomness, or code,
And all of Babel’s gibberish stream by.
—Jorge Luis Borges, two stanzas from his poem, The Compass

Jessica Flack is a professor at the Santa Fe Institute and director of its Collective Computation Group. Flack’s interests include the role of collective computation in the origins of biological space and time, coarse-graining in nature, causality, and robustness.


The hidden universality of movement in cities

Sat, 02/22/2020 - 11:01

Markus Schläpfer, Michael Szell, Hadrien Salat, Carlo Ratti, Geoffrey B. West


The interaction of all mobile species with their environment hinges on their movement patterns: the places they visit and how frequently they go there. In human society, where the prevalent form of cohabitation is in cities, the highly dynamic and diverse movement of people is fundamental to almost every aspect of socio-economic life, including social interactions or disease spreading, and ultimately is key to the evolution of urban infrastructure, productivity, innovation and technology. However, despite the crucial role of the spatio-temporal structure of movement in cities, the laws that govern the variation of population flows to specific locations have remained elusive. Here we show that behind the apparent complexity of movement a surprisingly simple universal scaling relation drives the flow of individuals to any specific location based on both frequency of visitation and distance travelled. We derive a first principles argument stating that the number of visiting individuals should decrease as an inverse square of the product of visitation frequency and travel distance; or, equivalently, as a power law with exponent ≈−2. Using large-scale data analyses, we demonstrate that population flows obey this theoretical prediction in virtually all tested areas across the globe, ranging from Europe and America to Asia and Africa, regardless of the detailed geographies, cultures or levels of development. The revealed regularity offers unprecedented possibilities for the modelling of mobility fluxes at high spatial and temporal resolution, and it places an important constraint on any theory of movement, spatial organisation and social interaction in cities.


A curious formulation robot enables the discovery of a novel protocell behavior

Sat, 02/22/2020 - 08:57

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.


Jonathan Grizou, Laurie J. Points, Abhishek Sharma and Leroy Cronin
Science Advances  31 Jan 2020:
Vol. 6, no. 5, eaay4237
DOI: 10.1126/sciadv.aay4237


Chaos | The Great Courses

Fri, 02/21/2020 - 09:00

It has been called the third great revolution of 20th-century physics, after relativity and quantum theory. But how can something called chaos theory help you understand an orderly world? What practical things might it be good for? What, in fact, is chaos theory? "Chaos theory," according to Dr. Steven Strogatz, Director of the Center for Applied Mathematics at Cornell University, "is the science of how things change." It describes the behavior of any system whose state evolves over time and whose behavior is sensitive to small changes in its initial conditions.


Metrics of Emergence, Self-Organization, and Complexity for EWOM Research

Fri, 02/21/2020 - 08:18

Juan C. Correa

Front. Phys., 21 February 2020


In a recent round table organized by the Santa Fe Institute, the complexity of commerce captured the attention of those interested in understanding how complex systems science can be applicable for settings where consumers and providers interact. Despite the usefulness of applied complexity for commerce-related phenomena, few works have attempted to provide insightful ideas. This mini-review aims at providing a succinct discussion of how the metrics of emergence, self-organization, and complexity might benefit the research agenda of applied complexity and commerce/consumer studies. In particular, the paper argues possible pragmatic ways to understanding the valuable information present in word-of-mouth data found on electronic commerce platforms.


Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London

Thu, 02/20/2020 - 14:11

Luca Maria Aiello, Daniele Quercia, Rossano Schifanella & Lucia Del Prete 
Scientific Data volume 7, Article number: 57 (2020)


We present the Tesco Grocery 1.0 dataset: a record of 420 M food items purchased by 1.6 M fidelity card owners who shopped at the 411 Tesco stores in Greater London over the course of the entire year of 2015, aggregated at the level of census areas to preserve anonymity. For each area, we report the number of transactions and nutritional properties of the typical food item bought including the average caloric intake and the composition of nutrients. The set of global trade international numbers (barcodes) for each food type is also included. To establish data validity we: i) compare food purchase volumes to population from census to assess representativeness, and ii) match nutrient and energy intake to official statistics of food-related illnesses to appraise the extent to which the dataset is ecologically valid. Given its unprecedented scale and geographic granularity, the data can be used to link food purchases to a number of geographically-salient indicators, which enables studies on health outcomes, cultural aspects, and economic factors.


The role of worldviews in the governance of sustainable mobility

Thu, 02/20/2020 - 09:39

Frank Chuang, Ed Manley, Arthur Petersen



In sustainability policy-making, a critical task is to value present and future needs in order to realize good quality of life. To analyze complex ideas of how people interpret reality, develop value orientations, and define needs and the good life, the notion of worldviews proved to be useful. We use worldviews to study how people of distinct ways of life perceive and assess sustainable mobility issues. Through exploring three worldviews (egalitarianism, hierarchy, and individualism), our results map across British people’s attitudes to mobility debates in terms of the economic, environmental, social, and political dimensions. In so doing, our study demonstrates a framework for identifying what behavioral and institutional barriers hinder the transformations needed to achieve better cities and societies.


What Differs Us From Machines?

Thu, 02/20/2020 - 08:49

Carlos Gershenson


One of the most amazing things about reading R.U.R. a century after it was first published is noticing how many questions underlying the story are still current. It is worth noting that Čapek’s robots are not mechanical, but living. In this sense, they are closer to artificial life than to artificial intelligence. One has to consider that the play was staged before the first electronic computers were built and before DNA was discovered (no mobile phones, no commercial aviation, no Internet). We still do not have agreed definitions of life nor intelligence, imagine how ambiguous these should have been a century ago.


From language shift to language revitalization and sustainability. Albert Bastardas-Boada.

Tue, 02/18/2020 - 14:58

This book aims to contribute to the overall, integrated understanding of the processes of language contact and their evolution, be they the result of political or economic (dis)integrations or migrations or for technological reasons. Via an interdisciplinary, holistic approach, it also aims to support the theoretical grounding of a unified, common sociolinguistic paradigm, based on an ecological and complexity perspective. This approach built on the fact that linguistic structures do not live in isolation from their social functions and must be situated in relation to the sub-and supra-systems that determine their existence if we are to understand their fortunes. It is a useful contribution to understanding and promoting the processes of linguistic revitalization in the world, combining at the same time the maintenance and development of diversity while ensuring the intercommunication of human species.


Evolution in the Debian GNU/Linux software network: analogies and differences with gene regulatory networks

Mon, 02/17/2020 - 17:04

Pablo Villegas, Miguel A. Muñoz and Juan A. Bonachela

Journal of The Royal Society Interface Volume 17 Issue 163


Biological networks exhibit intricate architectures deemed to be crucial for their functionality. In particular, gene regulatory networks, which play a key role in information processing in the cell, display non-trivial architectural features such as scale-free degree distributions, high modularity and low average distance between connected genes. Such networks result from complex evolutionary and adaptive processes difficult to track down empirically. On the other hand, there exists detailed information on the developmental (or evolutionary) stages of open-software networks that result from self-organized growth across versions. Here, we study the evolution of the Debian GNU/Linux software network, focusing on the changes of key structural and statistical features over time. Our results show that evolution has led to a network structure in which the out-degree distribution is scale-free and the in-degree distribution is a stretched exponential. In addition, while modularity, directionality of information flow, and average distance between elements grew, vulnerability decreased over time. These features resemble closely those currently shown by gene regulatory networks, suggesting the existence of common adaptive pathways for the architectural design of information-processing networks. Differences in other hierarchical aspects point to system-specific solutions to similar evolutionary challenges.


The effect of travel restrictions on the spread of the 2019 novel coronavirus (2019-nCoV) outbreak

Sun, 02/16/2020 - 10:14

Matteo Chinazzi, Jessica T. Davis, Marco Ajelli, Corrado Gioannini, Maria Litvinova, Stefano Merler, View ORCID ProfileAna Pastore y Piontti, Luca Rossi, Kaiyuan Sun, Cécile Viboud, Xinyue Xiong, Hongjie Yu, M. Elizabeth Halloran, Ira M. Longini Jr., Alessandro Vespignani


Motivated by the rapid spread of a novel coronavirus (2019-nCoV) in Mainland China, we use a global metapopulation disease transmission model to project the impact of both domestic and international travel limitations on the national and international spread of the epidemic. The model is calibrated on the evidence of internationally imported cases before the implementation of the travel quarantine of Wuhan. By assuming a generation time of 7.5 days, the reproduction number is estimated to be 2.4 [90% CI 2.2-2.6]. The median estimate for number of cases before the travel ban implementation on January 23, 2020 is 58,956 [90% CI 40,759 – 87,471] in Wuhan and 3,491 [90% CI 1,924 – 7,360] in other locations in Mainland China. The model shows that as of January 23, most Chinese cities had already received a considerable number of infected cases, and the travel quarantine delays the overall epidemic progression by only 3 to 5 days. The travel quarantine has a more marked effect at the international scale, where we estimate the number of case importations to be reduced by 80% until the end of February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.