Shannon famously applied his “mathematical theory of communication” to human communication, alledgedly having his wife, Betty, estimating word probabilities to calcualte the first approximation of the entropy of English. The following decades have seen creative further applications to humans and social processes (e.g., Miller, 1956; Attneave, 1959; Coleman, 1975; Ellis and Fisher, 1975; Cappella, 1979). These efforts lost steam in the 1980s, mainly because of the lack of adequate data, and limited computational power. Both limitations do not apply anymore. The increase in human interactions taking place in digital environments has led to an abundance of behavioral “big data”, enough even to calculate measures that converge rather slowly.

 

This Special Issue compiles creative research on the innovative uses of information theory, and its extensions, to better understand human behavior and social processes. Among other topics, the focus is set on human communication, social organization, social algorithms, human–machine interaction, artificial and human intelligence, collaborative teamwork, social media dynamics, information societies, digital development, and cognitive and machine biases—all online and/or offline. 

Source: www.mdpi.com

Complexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation.

Here are a few things you should know about complex systems,
result of a worldwide collaborative effort from leading experts, practitioners and students in the field.

Source: complexityexplained.github.io

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

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

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