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Northeast Journal of Complex Systems (NEJCS) | Vol 1 | No. 1

Sun, 09/22/2019 - 12:32

The Northeast Journal of Complex Systems (NEJCS) is launched by the community of Complex Systems researchers in the US Northeast region to provide a solution to the above challenge. As the official journal of the Complex Systems Society US Northeast Chapter, NEJCS serves as an open-access publication venue that is completely free for everyone, including authors, readers, libraries, and the public. Editors and reviewers also work “for free” as this journal is run by many volunteers. This is made possible by the editorial management and support provided by the Center for Collective Dynamics of Complex Systems and the Open Repository @ Binghamton (ORB) initiative at Binghamton University, State University of New York. Even though its name carries the word “Northeast”, this journal is open to everyone regardless of geographical locations. International contributions are more than welcome as well.


Sayama, Hiroki and Georgiev, Georgi (2019) "Editorial Introduction to the Northeast Journal of Complex Systems (NEJCS)," Northeast Journal of Complex Systems (NEJCS): Vol. 1 : No. 1 , Article 1.


Can You Judge Artificial Intelligence? | Cesar A. Hidalgo at Brain Bar

Fri, 09/20/2019 - 12:27

Ai’s are diagnosing cancer, driving cars, acting as police agents, and it seems that judging their decisions are not as easy as we first think.

Are we more forgiving when a machine makes a mistake? Would you trust an AI to be in charge?

Let’s find out with Cesar A. Hidalgo, the mastermind of collective learning from MIT!


Networks 2021

Thu, 09/19/2019 - 11:33

Networks 2021: A Joint Sunbelt and NetSci Conference will take place in Washington D.C. on July 6-11, 2021. We expect this to be the largest networks conference ever held. It will combine the annual meeting of the International Network for Social Network Analysis (Sunbelt XLI), and the annual meeting of the Network Science Society (NetSci 2021).


The WIRED Guide to Artificial Intelligence

Thu, 09/19/2019 - 09:24

Supersmart algorithms won’t take all the jobs, But they are learning faster than ever, doing everything from medical diagnostics to serving up ads.


Even if progress on making artificial intelligence smarter stops tomorrow, don’t expect to stop hearing about how it’s changing the world. Big tech companies such as Google, Microsoft, and Amazon have amassed strong rosters of AI talent and impressive arrays of computers to bolster their core businesses of targeting ads or anticipating your next purchase.


They’ve also begun trying to make money by inviting others to run AI projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware, growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.

Artificial general intelligence

As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them. Meanwhile, consumers can expect to be pitched more gadgets and services with AI-powered features. Google and Amazon in particular are betting that improvements in machine learning will make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their ownersand the world around them.


The commercial possibilities make this a great time to be an AI researcher. Labs investigating how to make smarter machines are more numerous and better-funded than ever. And there’s plenty to work on: Despite the flurry of recent progress in AI and wild prognostications about its near future, there are still many things that machines can’t do, such as understanding the nuances of language, common-sense reasoning, and learning a new skill from just one or two examples. AI software will need to master tasks like these if it is to get close to the multifaceted, adaptable, and creative intelligence of humans. One deep-learning pioneer, Google’s Geoff Hinton, argues that making progress on that grand challenge will require rethinking some of the foundations of the field.


As AI systems grow more powerful, they will rightly invite more scrutiny. Government use of software in areas such as criminal justice is often flawed or secretive, and corporations like Facebook have begun confronting the downsides of their own life-shaping algorithms. More powerful AI has the potential to create worse problems, for example by perpetuating historical biases and stereotypes against women or black people. Civil-society groups and even the tech industry itself are now exploring rules and guidelines on the safety and ethics of AI. For us to truly reap the benefits of machines getting smarter, we’ll need to get smarter about machines.


NECSI Winter School

Tue, 09/17/2019 - 15:44

Winter Session 2020

Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.

Dates: January 6 – 17

Location: MIT, Cambridge, MA
The NECSI Winter School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.

  • Week 1: January 6-10 CX201: Complex Physical, Biological and Social Systems
  • Week 2: January 12-17 CX202: Complex Systems Modeling and Networks


Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences

Tue, 09/17/2019 - 10:52

We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.


Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences
Hector Zenil, Peter Minary
Nucleic Acids Research, gkz750,


Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

Fri, 09/13/2019 - 12:33

During the last two decades, two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are related to their current exports, suggesting that shared capabilities and knowledge diffusion constrain export diversification. Here, we join both of these streams of literature by developing three measures of bilateral relatedness and using them to ask whether the destinations to which a country will increase its exports of a product are predicted by these forms of relatedness. The first form is product relatedness, and asks whether a country already exports many similar products to a destination. The second is importer relatedness, and asks whether the country exports the same product to the neighbors of the target destination. The third is exporter relatedness, and asks whether a country’s neighbors are already exporting the same product to the destination. We use bilateral trade data from 2000 to 2015, and a variety of controls in multiple gravity specifications, to show that countries are more likely to increase their exports of a product to a destination when they have more product relatedness, importer relatedness, and exporter relatedness. Then, we use several sample splits to explore whether the effects of these forms of relatedness are stronger for products of higher complexity, technological sophistication, and differentiation. We find that, in the case of product relatedness, the effects are stronger for differentiated, complex, and technologically sophisticated products. Also, we find the effects of common language and shared colonial past to increase with differentiation, complexity, and technological sophistication, while the effects of shared borders decrease with these three variables. These results suggest that product relatedness and common language capture dimensions of knowledge relatedness that are more important for the exchange of more sophisticated and differentiated products. These findings extend the ideas of relatedness to bilateral trade and show that the evolution of bilateral trade networks are shaped by relatedness among products, exporters, and importers.


Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade
Bogang Jun, Aamena Alshamsi, Jian Gao, César A. Hidalgo

Journal of Evolutionary Economics


Innovation and The Evolution of the Economic Web

Tue, 09/10/2019 - 10:36

Fifty thousand years ago the global economy may have had a diversity of a few thousand goods and services, including fire, unifacial stone scrapers, hides, and so forth. Today, in New York alone, there must be over a billion goods and services. The global economy has exploded in diversity. The question is how and why has this explosion occurred?
The economy, as detailed a bit further below, is a network of complements and substitutes, which I will call the Economic Web. And like the biosphere, it’s evolution is substantially unprestatable, “context dependent,” and creates its own growing “context” that comprises its “Adjacent Possible.” The adjacent possible is what can arise next in this evolution. This evolution is “sucked into” the very opportunities it itself creates. Innovations into the Adjacent Possible drive this growth.
I do not wish to consider here the rich evolution of a single technology. Brian Arthur has brilliantly done so in his book The Nature of Technology [1]. Rather, I wish to discuss the evolution of the entire economic web, for as we shall see, goods and services create novel niches which invite the innovative creation of new complementary and substitute goods such that the web as a whole grows in diversity.


Innovation and The Evolution of the Economic Web
by Stuart Kauffman

Entropy 2019, 21(9), 864


Learning to listen – Alice Eldridge

Sun, 09/08/2019 - 09:13

Alice Eldridge – Learning to listen


Information gerrymandering and undemocratic decisions

Sun, 09/08/2019 - 08:00

In a voter game, information gerrymandering can sway the outcome of the vote towards one party, even when both parties have equal sizes and each player has the same influence; and this effect can be exaggerated by strategically placed zealots or automated bots.

Information gerrymandering and undemocratic decisions
Alexander J. Stewart, Mohsen Mosleh, Marina Diakonova, Antonio A. Arechar, David G. Rand & Joshua B. Plotkin
Naturevolume 573, pages 117–121 (2019)


Iyad Rahwan Is the Anthropologist of Artificial Intelligence

Sat, 09/07/2019 - 19:59

The algorithms that underlie much of the modern world have grown so complex that we can’t always predict what they’ll do. Iyad Rahwan’s radical idea: The best way to understand them is to observe their behavior in the wild.


ICCS 2020 Tenth International Conference on Complex Systems July 26-31, 2020 Boston, MA, USA

Sat, 09/07/2019 - 09:20

Save the Date: July 26-31, 2020

This is the tenth in a series of conferences with two major aims: first, to investigate the common properties of very different complex systems; and second, to encourage cross fertilization among the many disciplines involved. ICCS 2020 will be held in the Boston area. More details will be announced soon.


Beyond integrated information: A taxonomy of information dynamics phenomena

Sat, 09/07/2019 - 09:01

Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise — every ’cause’ variable has an associated ‘effect’ variable, so that a ‘causal arrow’ can be drawn between them. However, analyses that depict interdependencies as directed graphs fail to discriminate the rich variety of modes of information flow that can coexist within a system. This, in turn, creates problems with attempts to operationalise the concepts of ‘dynamical complexity’ or `integrated information.’ To address this shortcoming, we combine concepts of partial information decomposition and integrated information, and obtain what we call Integrated Information Decomposition, or ΦID. We show how ΦID paves the way for more detailed analyses of interdependencies in multivariate time series, and sheds light on collective modes of information dynamics that have not been reported before. Additionally, ΦID reveals that what is typically referred to as ‘integration’ is actually an aggregate of several heterogeneous phenomena. Furthermore, ΦID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures.


Beyond integrated information: A taxonomy of information dynamics phenomena

Pedro A.M. Mediano, Fernando Rosas, Robin L. Carhart-Harris, Anil K. Seth, Adam B. Barrett


Our Human Current: Stories about Complexity, Systems, and Mentorship (Paperback)

Tue, 09/03/2019 - 08:46

Human Current, the complexity podcast, emerged from the belief that the world will be a better place if more people learn about complexity—as a scientific theory and way of understanding the world. Our Human Current is a story of listening and learning, and of the power of mentorship, inspired by our own mentor, Douglas Drane.

We conducted more than 125 thoughtful interviews with scientists, influencers, and practitioners in the fields of complexity science and systems thinking. Let us take you on a 365-day journey through the stories, research, anecdotes, and advice from Doug and our conversations with guests including: Stephen Wolfram, a founding father of complexity science; Yaneer Bar-Yam, president of the New England Complex Systems Institute; Jean Boulton, co-author of Embracing Complexity; Melanie Mitchell, professor at the Santa Fe Institute; Margaret Wheatley, best-selling author; Dean Radin, chief scientist at the Institute of Noetic Sciences; Albert-László Barabási, author; and so many more!


Our Human Current: Stories about Complexity, Systems, and Mentorship
By Angela Cross & Haley Campbell-Gross


Postdoctoral Fellowshio in Complex Systems and Data Science @UVMComplexity

Wed, 08/28/2019 - 18:01

This Postdoctoral Fellowship in Complex Systems and Data Science at the University of Vermont’s Complex Systems Center offers early-career scientists a unique experience to tackle open questions related to complex systems and data science that are of utmost importance in science, industry, and society. This postdoctoral fellowship provides a high level of intellectual freedom and the opportunity to work alongside leading academic researchers and industry partners.


The Fair Reward Problem: The Illusion of Success and How to Solve It

Wed, 08/28/2019 - 16:49

Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change — and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e. fairness/meritocracy). Moreover, in many fields, humans are overconfident and pervasively confuse luck for skill (I win, it is skill; I lose, it is bad luck). In some fields, there is too much risk-taking; in others, not enough. Where success derives in large part from luck — and especially where bailouts skew the incentives (heads, I win; tails, you lose) — it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short-term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk-adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration–exploitation trade-off. Several human endeavors — including finance, politics, and science — are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.


The Fair Reward Problem: The Illusion of Success and How to Solve It
Didier Sornette, Spencer Wheatley & Peter Cauwels

Advances in Complex Systems Vol. 22, No. 03, 1950005 (2019) 


Memory formation in the absence of experience

Wed, 08/28/2019 - 15:33

Memory is coded by patterns of neural activity in distinct circuits. Therefore, it should be possible to reverse engineer a memory by artificially creating these patterns of activity in the absence of a sensory experience. In olfactory conditioning, an odor conditioned stimulus (CS) is paired with an unconditioned stimulus (US; for example, a footshock), and the resulting CS–US association guides future behavior. Here we replaced the odor CS with optogenetic stimulation of a specific olfactory glomerulus and the US with optogenetic stimulation of distinct inputs into the ventral tegmental area that mediate either aversion or reward. In doing so, we created a fully artificial memory in mice. Similarly to a natural memory, this artificial memory depended on CS–US contingency during training, and the conditioned response was specific to the CS and reflected the US valence. Moreover, both real and implanted memories engaged overlapping brain circuits and depended on basolateral amygdala activity for expression.


Memory formation in the absence of experience
Gisella Vetere, Lina M. Tran, Sara Moberg, Patrick E. Steadman, Leonardo Restivo, Filomene G. Morrison, Kerry J. Ressler, Sheena A. Josselyn & Paul W. Frankland 
Nature Neuroscience volume 22, pages 933–940 (2019)



Social media usage reveals how regions recover after natural disaster

Wed, 08/28/2019 - 13:36

The challenge of nowcasting and forecasting the effect of natural disasters (e.g. earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events. Traditional disaster recovery estimates, such as surveys and interviews, are usually costly, time consuming and do not scale. Here we present a methodology to indirectly estimate the post-emergency recovery status (‘downtime’) of small businesses in urban areas looking at their online posting activity on social media. Analysing the time series of posts before and after an event, we quantify the downtime of small businesses for three natural disasters occurred in Nepal, Puerto Rico and Mexico. A convenient and reliable method for nowcasting the post-emergency recovery status of economic activities could help local governments and decision makers to better target their interventions and distribute the available resources more effectively.


Social media usage reveals how regions recover after natural disaster
Robert Eyre, Flavia De Luca, Filippo Simini


The Neuroscience of Reality

Sun, 08/25/2019 - 14:34

  • The reality we perceive is not a direct reflection of the external objective world.
  • Instead it is the product of the brain’s predictions about the causes of incoming sensory signals.
  • The property of realness that accompanies our perceptions may serve to guide our behavior so that we respond appropriately to the sources of sensory signals.


The Neuroscience of Reality

Anil Seth

Scientific American


Interactional and Informational Attention on Twitter

Sat, 08/24/2019 - 08:14

Twitter may be considered to be a decentralized social information processing platform whose users constantly receive their followees’ information feeds, which they may in turn dispatch to their followers. This decentralization is not devoid of hierarchy and heterogeneity, both in terms of activity and attention. In particular, we appraise the distribution of attention at the collective and individual level, which exhibits the existence of attentional constraints and focus effects. We observe that most users usually concentrate their attention on a limited core of peers and topics, and discuss the relationship between interactional and informational attention processes—all of which, we suggest, may be useful to refine influence models by enabling the consideration of differential attention likelihood depending on users, their activity levels, and peers’ positions.


Interactional and Informational Attention on Twitter
Agathe Baltzer, Márton Karsai, and Camille Roth
Information 2019, 10(8), 250