24 min listen
The Economy and Complexity Science: Part 2
The Economy and Complexity Science: Part 2
ratings:
Length:
31 minutes
Released:
Jan 9, 2023
Format:
Podcast episode
Description
In our last episode, we heard from W. Brian Arthur, who shared his journey in economics as he studied increasing returns. Now, Brian's going to take us to 1987, to a small meeting in the Rockies in Santa Fe. At this time, he was struggling to gain recognition for his work within the economics community, but it was when Brian went to what would become the Santa Fe Institute that things really kicked off.
In this episode, you're going to hear again from W. Brain Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he remembers the early days of the Santa Fe Institute. From the early meetings of economists, physicists, and a biologist that started it all, to an early model Brian built of a stock market that was unique to any models before it — because this model included booms and busts.
Connect:
Simplifying Complexity on Twitter
Sean Brady on Twitter
Sean Brady on LinkedIn
Brady Heywood website
This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.
In this episode, you're going to hear again from W. Brain Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he remembers the early days of the Santa Fe Institute. From the early meetings of economists, physicists, and a biologist that started it all, to an early model Brian built of a stock market that was unique to any models before it — because this model included booms and busts.
Connect:
Simplifying Complexity on Twitter
Sean Brady on Twitter
Sean Brady on LinkedIn
Brady Heywood website
This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.
Released:
Jan 9, 2023
Format:
Podcast episode
Titles in the series (41)
Can you tell when a system is about to tip? by Simplifying Complexity