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The increasing availability of large-scale datasets that trace the entirety of the scientific enterprise, have created an unprecedented opportunity to explore scientific production and reward. Parallel developments in data science, network science, and artificial intelligence offer us powerful tools and techniques to make sense of these millions of data points. Together, they tell a complex yet insightful story about how scientific careers unfold, how collaborations contribute to discovery, and how scientific progress emerges through a combination of multiple interconnected factors. These opportunities—and challenges that come with them—have fueled the emergence of a multidisciplinary community of scientists that are united by their goals of understanding science. These practitioners of the science of science use the scientific methods to study themselves, examine projects that work as well as those that fail, quantify the patterns that characterize discovery and invention, and offer lessons to improve science as a whole. In this talk, I’ll highlight some examples of research in this area, hoping to illustrate the promise of science of science as well as its limitations.

Watch at: www.youtube.com

Those of us who think that that the laws of physics underlying everyday life are completely known tend to also think that consciousness is an emergent phenomenon that must be compatible with those laws. To hold such a position in a principled way, it’s important to have a clear understanding of “emergence” and when it happens. Anil Seth is a leading researcher in the neuroscience of consciousness, who has also done foundational work (often in collaboration with Lionel Barnett) on what emergence means. We talk about information theory, entropy, and what they have to do with how things emerge.

Read the full article at: www.preposterousuniverse.com

Today we’re talking to IU Professor Johan Bollen about the impact social media is having on us, and the complex relationship we have with the tech companies that run them.

Read the full article at: www.wfyi.org

What is the economy?  People used to tell stories about the exchange of goods and services in terms of flows and processes — but over the last few hundred years, economic theory veered toward measuring discrete amounts of objects.  Why?  The change has less to do with the objective nature of economies and more to do with what tools theorists had available.  And scientific instruments — be they material technologies or concepts — don’t just make new things visible, but also hide things in new blind spots.  For instance, algebra does very well with ratios and quantities…but fails to properly address what markets do: how innovation works, where value comes from, and how economic actors navigate (and change) a fundamentally uncertain shifting landscape.  With the advent of computers, new opportunities emerge to study that which cannot be contained in an equation. Using algorithms, scientists can formalize complex behaviors – and thinking economics in both nouns and verbs provides a more complete and useful stereoscopic view of what we are and do.

This week we speak with W. Brian Arthur of The Santa Fe Institute, Stanford University, and Xerox PARC about his recent essay, “Economics in Nouns and Verbs.” In this first part of a two-part conversation, we explore how a mathematics of static objects fails to describe economies in motion — and how a process-based approach can fill gaps in our understanding.

Listen (Part 1) at: complexity.simplecast.com

Also, listen to Part 2 on “Prim Dreams of Order vs. Messy Vitality” in Economics, Math, and Physics

From the spreading of diseases and memes to the development of
opinions and social influence, dynamical processes are influenced heavily
by the networks on which they occur. In this talk, I’ll discuss social
influence and opinion models on networks. I’ll present a few types of
models — including threshold models of social contagions, voter models
that coevolve with network structure, and bounded-confidence models with
continuous opinions — and illustrate how such processes are affected by
the networks on which they occur. I’ll also connect these models to
opinion polarization and the development of echo chambers in online social
networks.

Watch at: www.youtube.com