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Barry Wellman, FRSC               Director, NetLab Network
Founder, International Network for Social Network Analysis

Bit by bit, putting it together--Sondheim
It's Always Something--Roseanne Roseannadanna

Getting It Done; Getting It Out: A Practical Guide to Writing, Publishing, Presenting, and Promoting in the Social Sciences--coming in 2021 (Guilford Press)

NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman http://amzn.to/zXZg39
http://www.chass.utoronto.ca/~wellman            https://en.wikipedia.org/wiki/Barry_Wellman

-------- Forwarded Message --------
Subject: Latest Complexity Digest Posts
Date: Mon, 27 Jul 2020 11:02:13 +0000
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To: Barry <[log in to unmask]>

Learn about the latest and greatest related to complex systems research. More at https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5d5ab9fdf5&e=55e25a0e3e

Complex systems: Features, similarity and connectivity


Cesar H.Comin, Thomas Peron, Filipi N.Silva, Diego R.Amancio, Francisco A.Rodrigues, Luciano da F.Costa

Physics Reports
Volume 861, 25 May 2020, Pages 1-41

The increasing interest in complex networks research has been motivated by intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern
recognition, optimization, linear algebra, and time series analysis seem to play a particularly basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups of representations, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. For instance, we argue that many models used to generate networks can be understood as a mapping from features to similarity, and then to network connectivity concepts. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also
developed in this article.

Source: www.sciencedirect.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e0dace6028&e=55e25a0e3e)

The Damage We’re Not Attending To



Scientists who study complex systems offer solutions to the pandemic.

Source: nautil.us (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=876ab5af4f&e=55e25a0e3e)

Global socio-economic losses and environmental gains from the Coronavirus pandemic


Lenzen M, Li M, Malik A, Pomponi F, Sun Y-Y, Wiedmann T, et al. (2020) Global socio-economic losses and environmental gains from the Coronavirus pandemic. PLoS ONE 15(7): e0235654. https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=41a13a0c84&e=55e25a0e3e

On 3 April 2020, the Director-General of the WHO stated: “[COVID-19] is much more than a health crisis. We are all aware of the profound social and economic consequences of the pandemic (WHO, 2020)”. Such consequences are the result of counter-measures such as lockdowns, and world-wide reductions in production and consumption, amplified by cascading impacts through international supply chains. Using a global multi-regional macro-economic model, we capture direct and indirect spill-over effects in terms of social and economic losses, as well as environmental effects of the pandemic. Based on information as of May 2020, we show that global consumption losses amount to 3.8$tr, triggering significant job (147 million full-time equivalent) and income (2.1$tr) losses. Global atmospheric emissions are reduced by 2.5Gt of greenhouse gases, 0.6Mt of PM2.5, and 5.1Mt of SO2 and NOx. While Asia, Europe and the USA have been the most directly impacted regions, and transport and tourism the immediately
hit sectors, the indirect effects transmitted along international supply chains are being felt across the entire world economy. These ripple effects highlight the intrinsic link between socio-economic and environmental dimensions, and emphasise the challenge of addressing unsustainable global patterns. How humanity reacts to this crisis will define the post-pandemic world.

Source: journals.plos.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=caf77ed9ac&e=55e25a0e3e)

Socioeconomic bias in influenza surveillance


Scarpino SV, Scott JG, Eggo RM, Clements B, Dimitrov NB, Meyers LA (2020) Socioeconomic bias in influenza surveillance. PLoS Comput Biol 16(7): e1007941. https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b70d1a882f&e=55e25a0e3e

Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.

Source: journals.plos.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9af0148dfb&e=55e25a0e3e)

Segregated interactions in urban and online space

Xiaowen Dong, Alfredo J. Morales, Eaman Jahani, Esteban Moro, Bruno Lepri, Burcin Bozkaya, Carlos Sarraute, Yaneer Bar-Yam & Alex Pentland
EPJ Data Science volume 9, Article number: 20 (2020)

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant
socio-political and economic implications.

Source: epjdatascience.springeropen.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=46fb6be21f&e=55e25a0e3e)

Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles

Jonas L. Juul, Kaare Græsbøll, Lasse Engbo Christiansen, Sune Lehmann

Across the world, scholars are racing to predict the spread of the novel coronavirus, COVID-19. Such predictions are often pursued by numerically simulating epidemics with a large number of plausible combinations of relevant parameters. It is essential that any forecast of the epidemic trajectory derived from the resulting ensemble of simulated curves is presented with confidence intervals that communicate the uncertainty associated with the forecast. Here we argue that the state-of-the-art approach for summarizing ensemble statistics does not capture crucial epidemiological information. In particular, the current approach systematically suppresses information about the projected trajectory peaks. The fundamental problem is that each time step is treated separately in the statistical analysis. We suggest using curve-based descriptive statistics to summarize trajectory ensembles. The results presented allow researchers to report more representative confidence intervals, resulting in more
realistic projections of epidemic trajectories and -- in turn -- enable better decision making in the face of the current and future pandemics.

Source: arxiv.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=631b62058a&e=55e25a0e3e)

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable
growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

Source: arxiv.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c925c0cae2&e=55e25a0e3e)

A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions


Hector Zenil

Entropy 2020, 22(6), 612

Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into
consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.

Source: www.mdpi.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f022c72cd0&e=55e25a0e3e)

ALIFE 2020: The 2020 Conference on Artificial Life (proceedings)


Editors: Josh Bongard, Juniper Lovato, Laurent Hebert-Dufrésne, Radhakrishna Dasari and Lisa Soros

This volume presents the proceedings of the 2020 Conference on Artificial Life (ALIFE 2020) which took place online July 13-18. Originally scheduled to be held in Montreal, Canada, this was the first time our conference had been conducted in this manner. Of course, our community was not alone: just about every human community has had to adapt to the covid-19 pandemic and its repercussions. It is difficult to avoid seeing the irony in this: Artificial Life researchers have declared, since the field’s inception at a small workshop at Los Alamos in 1987, that we wish to understand how life adapts to unforeseen circumstances. Further, we wish to incorporate learned mechanisms of adaptation into our technologies and, possibly, our societies. Put simply, Artificial Life invites us to think and learn about adaptation; SARS-CoV-2 forces us to adapt. More simple yet: ALife is theory; COVID is practice. There is a long tradition in our field of peering at our computer screens or into our petri
dishes, waiting with bated breath to see what new forms emerge. Likewise for the post-pandemic world. Whatever does emerge from the conference, and from the pandemic — and whether we learn from it, and whether we use that knowledge to benefit each other — it is our honor to be part of the adventure with you.

Source: www.mitpressjournals.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=77641a97b5&e=55e25a0e3e)

Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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