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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    

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Subject: 	Latest Complexity Digest Posts
Date: 	Mon, 27 Jul 2020 11:02:13 +0000
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Learn about the latest and greatest related to complex systems research. 
More at 

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.

( )

The Damage We’re Not Attending To 


Scientists who study complex systems offer solutions to the pandemic.

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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. 

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.

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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. 

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.

( )

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.

( )

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.

( )

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.

( )

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.

( )

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.

( )

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

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