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Postgraduate School of Thinking, Vrije Universiteit Brussel

Complexity Digest - Fri, 06/14/2019 - 10:36

At the most fundamental level many of the problems we face are the unfortunate outcome of the malpractice of thinking. Whichever complex problem one may consider –be it ecological, societal, political, economic, organisational etc.– one will likely find that it is caused by the clashing of incompatible or inadequate manners of thinking. Even when these are genuinely well intended and strongly self-justified, they often inadvertently contribute to composite problematics.
The inadequacies of our thinking are deeply entrenched in the way that we humans, perceive the world, ourselves in the world, and how we interact with it. Our professional, educational, cultural and metaphysical systems strongly dispose us towards outlining sharp boundaries, separating objects from backgrounds, ’us’ from ‘them’, defining identities and curving out what is to be of significance from what can be dismissed, disposed of, or exploited. Such dispositions result in oversimplifications which are often apparent to us in the thinking of others, but much less in our own thinking. Yet, they are omnipresent and almost impossible to avoid. Once cohered by logical reasoning, anchored in captivating symbolism and encoded in algorithms, such simplifications turn into cages: mental, emotional, operational… Moving beyond them becomes literally unthinkable. We may repeat the mantra of ‘thinking outside the box’, we may praise critical, independent, creative and disruptive thinking, but these get deployed only in as far as they prove usable for the affirmation of our respective, deeply rooted worldviews.

Source: schoolofthinking.be

News on the limits of AI and alternatives

Dr. Tom Froese - Thu, 06/13/2019 - 12:57

My university published an interview about my views on the limits of AI and what I think are better alternatives for technological development.

Here is a short video clip:

Call for Applications: Cátedra Germinal Cocho en Ciencias de la Complejidad (Senior posdoc)

Complexity Digest - Thu, 06/13/2019 - 12:28

The Center for Complexity Sciences (C3) at the Universidad Nacional Autónoma de México is seeking candidates for a one year researcher position (extensible for a second year).


The candidates should have more than ten publications in indexed journals and to have directed at least one thesis (doctorate, masters, or bachelors). Projects can be individual or related to current research at the C3.

Source: complexes.blogspot.com

Call for Applications: Cátedra Germinal Cocho en Ciencias de la Complejidad (Senior posdoc)

Complexes - Thu, 06/13/2019 - 12:27
The Center for Complexity Sciences (C3) at the Universidad Nacional Autónoma de México is seeking candidates for a one year researcher position (extensible for a second year).

The candidates should have more than ten publications in indexed journals and to have directed at least one thesis (doctorate, masters, or bachelors). Projects can be individual or related to current research at the C3.

Interested candidates should send CV and research statement before June 20th to cgg at unam dot mx.

Advances in Complex Systems and Their Applications to Cybersecurity

Complexity Digest - Thu, 06/13/2019 - 11:38

Cybersecurity is one of the fastest growing and largest technology sectors and is increasingly being recognized as one of the major issues in many industries, so companies are increasing their security budgets in order to guarantee the security of their processes. Successful menaces to the security of information systems could lead to safety, environmental, production, and quality problems.

One of the most harmful issues of attacks and intrusions is the ever-changing nature of attack technologies and strategies, which increases the difficulty of protecting computer systems. As a result, advanced systems are required to deal with the ever-increasing complexity of attacks in order to protect systems and information.

This special issue received several contributions, 5 of which have been accepted for publication.


Volume 2019, Article ID 3261453, 2 pages
Advances in Complex Systems and Their Applications to Cybersecurity
Fernando Sánchez Lasheras, Danilo Comminiello, and Alicja Krzemień

Source: www.hindawi.com

Complex Methods Applied to Data Analysis, Processing, and Visualisation

Complexity Digest - Wed, 06/12/2019 - 11:37

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).


Volume 2019, Article ID 9316123, 2 pages
Complex Methods Applied to Data Analysis, Processing, and Visualisation
Jose Garcia-Rodriguez, Anastasia Angelopoulou, David Tomás, and Andrew Lewis

Source: www.hindawi.com

Roundtable talk on the problem of meaning in AI

Dr. Tom Froese - Tue, 06/11/2019 - 19:29

I was invited to give a presentation on the problem of meaning in artificial intelligence as part of an international roundtable on machine learning, artificial intelligence, and super-computation.

Here is the official poster with the details:

Complexity in Forecasting and Predictive Models

Complexity Digest - Tue, 06/11/2019 - 16:35

The challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications.

This issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, soft computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and finite mixture minimum squares (FIMIX-PLS)

Mainly, all the papers are focused on triggering a substantive discussion on how the model predictions can face the challenges around the complexity field that lie ahead. These works help to better understand the new trends in computing and statistical techniques that allow us to make better forecasts. Complexity plays a prominent role in these trends, given the increasing variety and changing data flows, forcing academics to adopt innovative and hybrid methods.


Volume 2019, Article ID 8160659, 3 pages
Complexity in Forecasting and Predictive Models
Jose L. Salmeron, Marisol B. Correia, and Pedro R. Palos-Sanchez


Source: www.hindawi.com

On complexity of branching droplets in electrical field

Complexity Digest - Tue, 06/11/2019 - 13:16

Decanol droplets in a thin layer of sodium decanoate with sodium chloride exhibit bifurcation branching growth due to interplay between osmotic pressure, diffusion and surface tension. We aimed to evaluate if morphology of the branching droplets changes when the droplets are subject to electrical potential difference. We analysed graph-theoretic structure of the droplets and applied several complexity measures. We found that, in overall, the current increases complexity of the branching droplets in terms of number of connected components and nodes in their graph presentations, morphological complexity and compressibility.


On complexity of branching droplets in electrical field
Mohammad Mahdi Dehshibi, Jitka Cejkova, Dominik Svara, Andrew Adamatzky

Source: arxiv.org

A simple contagion process describes spreading of traffic jams in urban networks

Complexity Digest - Mon, 06/10/2019 - 16:40

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.


A simple contagion process describes spreading of traffic jams in urban networks
Meead Saberi, Mudabber Ashfaq, Homayoun Hamedmoghadam, Seyed Amir Hosseini, Ziyuan Gu, Sajjad Shafiei, Divya J. Nair, Vinayak Dixit, Lauren Gardner, S. Travis Waller, Marta C. González

Source: arxiv.org

Machine Learning and Modeling at CSS’2019

Complexity Digest - Mon, 06/10/2019 - 15:32

The science of complex systems provides the framework for understanding patterns of behavior, and their emergence, at multiple scales in social and other types of systems. The analytical toolsets provided by AI and Machine Learning are good to recognize and measure such patterns in the data. The combination of pattern recognition and generation mechanisms provides an opportunity to advance our understanding of the complexity of real systems. Ultimately, we could benefit from such complexity, rather than being endangered by it, design better technologies, decisions and strategies.

  • Show new ways to model complex and social systems by means of big data analysis, machine learning and AI.
  • Explore new ways to analyze the data, taking into account the complexity of underlying systems.
  • We would like to address how to formulate the right questions and retrieve the relevant information.

The opportunities available from big data and machine learning could solve challenging problems but we must analyze and interpret the data properly. Wrong assumptions and simplified views could separate modeling from reality. We expect to raise awareness about interventions in complex systems, the risk we face when societies become global, the opportunities that are created, and the role of complexity in data analytics.

Source: sites.google.com

Gender-specific preference in online dating

Complexity Digest - Mon, 06/10/2019 - 13:12

In this paper, to reveal the differences of gender-specific preference and the factors affecting potential mate choice in online dating, we analyze the users’ behavioral data of a large online dating site in China. We find that for women, network measures of popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the network measures of popularity of the women they contact are significantly positively associated with their messaging behaviors. Secondly, when women send messages to men, they pay attention to not only whether men’s attributes meet their own requirements for mate choice, but also whether their own attributes meet men’s requirements, while when men send messages to women, they only pay attention to whether women’s attributes meet their own requirements. Thirdly, compared with men, women attach great importance to the socio-economic status of potential partners and their own socio-economic status will affect their enthusiasm for interaction with potential mates. Further, we use the ensemble learning classification methods to rank the importance of factors predicting messaging behaviors, and find that the centrality indices of users are the most important factors. Finally, by correlation analysis we find that men and women show different strategic behaviors when sending messages. Compared with men, for women sending messages, there is a stronger positive correlation between the centrality indices of women and men, and more women tend to send messages to people more popular than themselves. These results have implications for understanding gender-specific preference in online dating further and designing better recommendation engines for potential dates. The research also suggests new avenues for data-driven research on stable matching and strategic behavior combined with game theory.


Gender-specific preference in online dating
Xixian Su and Haibo Hu
EPJ Data Science 2019 8:12

Source: epjdatascience.springeropen.com

Human information processing in complex networks

Complexity Digest - Sun, 06/09/2019 - 09:46

Humans communicate using systems of interconnected stimuli or concepts — from language and music to literature and science — yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system’s network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules — the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.


Human information processing in complex networks

Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, Danielle S. Bassett

Source: arxiv.org

Interacting contagions are indistinguishable from social reinforcement

Complexity Digest - Sat, 06/08/2019 - 09:44

From fake news to innovative technologies, many contagions spread via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source. Contrarily, biological agents such as Ebola or measles are typically thought to spread as simple contagions. Here, we demonstrate that interacting simple contagions are indistinguishable from complex contagions. In the social context, our results highlight the challenge of identifying and quantifying mechanisms, such as social reinforcement, in a world where an innumerable amount of ideas, memes and behaviors interact. In the biological context, this parallel allows the use of complex contagions to effectively quantify the non-trivial interactions of infectious diseases.


Interacting contagions are indistinguishable from social reinforcement

Laurent Hébert-Dufresne, Samuel V. Scarpino, Jean-Gabriel Young

Source: arxiv.org

Simplicial models of social contagion

Complexity Digest - Fri, 06/07/2019 - 14:50

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.


Simplicial models of social contagion
Iacopo Iacopini, Giovanni Petri, Alain Barrat & Vito Latora
Nature Communications 10, Article number: 2485 (2019)

Source: www.nature.com

Proceedings A Special Feature: A Generation of Network Science. Call for papers

Complexity Digest - Fri, 06/07/2019 - 09:48

On the eve of 20th century, three papers launched the modern Network Science by bringing it to the attention of a wider community of physicists, computer scientists and applied mathematicians. The papers – by Watts and Strogatz [1], Barabasi and Albert [2], and Google founders Brin and Page [3] – introduced “small world networks”, “preferential attachment,” and “PageRank” into the vernacular of network scientists. They showed that simple models could reproduce much of the complexity observed in network structure and that the structure of networks was linked to their function. As we mark the 20th anniversary of the publication of these seminal works, it is time to reflect on the state of Network Science and where the field is headed. What have we learned about networks over the past two decades? How does network structure affect its function? How do we represent networks, predict and control their behavior? How do networks grow and change? What are the limits of our understanding, and finally, what are the important open problems in network science?

Source: royalsocietypublishing.org

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

Complexity Digest - Tue, 06/04/2019 - 10:00

Nature’s spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life’s creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.


An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami,
Artificial Life
Volume 25 | Issue 2 | Spring 2019 p.93-103

Source: www.mitpressjournals.org

Technology seems open-ended, and it is not living… or is it?

Worlds Hidden in Plain Sight

Complexity Digest - Sun, 06/02/2019 - 14:42

Over the last three decades, the Santa Fe Institute and its network of researchers have been pursuing a revolution in science.

Ignoring the boundaries of disciplines and schools and searching for novel fundamental ideas, theories, and practices, this international community integrates the full range of scientific inquiries that will help us to understand and survive on a complex planet.

This volume collects essays from the past thirty years of research, in which contributors explain in clear and accessible language many of the deepest challenges and insights of complexity science.

Explore the evolution of complex systems science with chapters from Nobel Laureates Murray Gell-Mann and Kenneth Arrow, as well as numerous pioneering complexity researchers, including John Holland, Brian Arthur, Robert May, Richard Lewontin, Jennifer Dunne, and Geoffrey West.

Source: www.santafe.edu

What is the Entropy of a Social Organization?

Complexity Digest - Sat, 06/01/2019 - 11:35

We quantify a social organization’s potentiality, that is its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To encode interaction preferences between individuals, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from 3 empirical and 2 synthetic systems.


What is the Entropy of a Social Organization?
Christian Zingg, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer

Source: arxiv.org

New paper on the Enactive Torch

Dr. Tom Froese - Fri, 05/31/2019 - 16:22

Here is a paper on the Enactive Torch that resulted from a nice student project:

Quantification of movement patterns during a maze navigation task

Ariel Sáenz, Leonardo Zapata-Fonseca, Tom Froese, and Ruben Fossion

Homeostatic systems tend to have a preferred state that it can be referred as a healthy state in traditionally-known systems such as the cardiovascular system. Any deviation from this state has been linked to disease. Different types of variables interact within homeostatic systems. Recently it has been described 2; “regulated” and “regulating” variables both of them with specific statistics that correlate to their function in maintaining homeostasis. We stated in this study that perception and mastery of a task with a sensory substitution system can be viewed and studied in a similar manner as traditionally-known homeostatic systems. We propose and exemplified with 2 cases of study that the state of mastery, from a time series perspective, share similarities between the statistics of their variables with healthy states in traditionally-known homeostatic systems, and that variations from that state of mastery share similarities with disease processes in traditionally-known homeostatic systems.


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