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Complexity basically means that everything is circuitous and hard to understand. Hard to understand means that the things that lead to the things you want aren't the things you want. They're very complex. So you need to do things like work on vacuum tubes to get to computers because vacuum tubes are in the first computers, even though vacuum tubes ... See more

Patrick O'Shaughnessy • Greatness Without Goals

The two most important problems (at least how I am thinking about them currently), are:

Finding a rigorous scientific framework for how different agent skills, personalities, and instructions combine to be most capable for different problems (think of this as social management science for AI agents) .

Figuring out how you formally validate and verify... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

In complexity economics, agents differ and in general lack full knowledge of each other and of the situation they are in. Fundamental uncertainty is therefore the norm; ill-defined problems are the norm; and rationality is not necessarily well defined. Agents explore and learn and adapt and open to novel behavior. Outcomes may not be in equilibrium

W. Brian Arthur • Complexity Economics: Proceedings of the Santa Fe Institute's 2019 Fall Symposium

AI & ML

Luc Cheung • 2 cards

The coordination headwind is inexorable. It increases super-linearly as: 1. Uncertainty increases; 2. Projects teams get larger and more spread out; 3. A more bottoms up culture.

Alex Komoroske • Coordination Headwind - How Organizations Are Like Slime Molds

Complexity economics views the economy as a system not necessarily in equilibrium, but rather as one where agents constantly change their actions and strategies in response to the outcomes they mutually create.

W. Brian Arthur • Complexity Economics: Proceedings of the Santa Fe Institute's 2019 Fall Symposium

I still think that we are thinking about scaling of network-based intelligence looking at this particular network. An extended network of machines and humans in larger networks facilitated by smaller and increasingly embodied devices might in themselves add an AI-augmented neocortex to our (human-machine) systems. Intelligence will continue to emer... See more

Azeem Azhar • 🧠 AI’s $100bn question: The scaling ceiling

But the way in which complex phenomena are hidden, beyond masking by space and time, is through nonlinearity, randomness, collective dynamics, hierarchy, and emergence—a deck of attributes that have proved ill suited to our intuitive and augmented abilities to grasp and to comprehend.

Jessica C. Flack • Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, 1984–2019 (Compass)

The economy is thus comprised of evolving networks of interacting agents, institutions, and technologies—networks of networks. The macro- level patterns of the economy—growth, innovation, business cycles, market booms and busts, inequality, and carbon emissions—then emerge from these dynamic micro- and meso-level interactions. From the complexity e