This week on Complexity, we speak with SFI External Professor Tina Eliassi-Rad, Professor of Computer Science at Northeastern University, about her complex systems research on democracy, what forces stabilize or upset democratic process, and how to rigorously study the relationships between technology and social change.

Read the full article at: complexity.simplecast.com

What makes a satisfying explanation? Understanding and prediction are two different goals at odds with one another — think fundamental physics versus artificial neural networks — and even what defines a “simple” explanation varies from one person to another. Held in a kind of ecosystemic balance, these diverse approaches to seeking knowledge keep each other honest…but the use of one kind of knowledge to the exclusion of all others leads to disastrous results. And in the 21st Century, the difference between good and bad explanations determines how society adapts as rapid change transforms the world most people took for granted — and sends humankind into the epistemic wilds  to find new stories that will help us navigate this brave new world.

This week we dive deep with SFI External Professor Simon DeDeo at Carnegie Mellon University to explore his research into intelligence and the search for understanding, bringing computational techniques to bear on the history of science, information processing at the scale of society, and how digital technologies and the coronavirus pandemic challenge humankind to think more carefully about the meaning that we seek, here on the edge of chaos…

Read the full article at: complexity.simplecast.com

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A transdisciplinary group of thinkers consider time and its relation to an Interplanetary future.

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When imagining interplanetary life and human civilization in space, it’s always a matter of time. Philosophers and physicists from Aristotle to Carlo Rovelli have deeply considered the nature of time. Given the scale of the social-technical systems required for any off-Earth endeavor, however, this age-old discussion requires broader input.

Complex systems emerge from a multitude of time-scales, clocks, arrows of time, and therefore a multitude of rates at which things come together and fall apart. But our experience of time seems to vary with the perspective we take on a subject: the lifespan of an organism seems to be the result of constraints of mass and energy; a firm, the flows and stocks of capital and labor; a state, the developments of its people and their political economy.

How do these different time-scales interrelate and inform one another on Earth today? What might a reconsideration of the complexity of time add to our collective effort to sustain life on and with other planets? And how can we create scalable yet adaptable social-technical systems that work together to achieve our interplanetary futures?

This panel will bring together researchers, scientists and theorists to attempt an answer to these questions. They will explore the possible methods and tools for complex collaboration, and consider what it will take to support and grow life beyond the Earth while keeping, at the center of it all, the beating heart of time.

Participants:

Laura Maguire
Zara Mirmalek
Geoffrey West
Sean Carroll
Moderator: David Krakauer

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One of the major resource requirements of computers—ranging from biological cells to human brains to high-performance digital computers—is the energy used to run them. Those energy requirements of performing a computation have been a long-standing focus of research in statistical physics, going back (at least) to the early work of Landauer and colleagues.

However, one of the most prominent aspects of computers is that they are inherently non-equilibrium systems. They are also often quite small, far from the thermodynamic limit. Unfortunately, the research by Landauer and co-workers was grounded in the statistical physics of the 20th century, which could not properly address the thermodynamics of non-equilibrium, nanoscale systems.

Fortunately, recent revolutionary breakthroughs in stochastic thermodynamics have overcome the limitations of 20th century statistical physics. We can now analyze arbitrarily off-equilibrium systems, of arbitrary size. Here I show how to apply these recent breakthroughs to analyze the thermodynamics of computation. Specifically, I present formulas for the thermodynamic costs of implementing (loop-free) digital circuits, of implementing Turing machines, and of implementing multipartite processes like the interacting organelles in a cell.

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