Yesterday we had our Introductory Pre Session of the Bucerius Legal Tech Essentials … the video for our intro session in now available on YouTube (see video above).
The official first session begins in earnest Monday June 29th Noon US Central / 700pm CEST. It is not too late to join us if you still want to sign up for FREE — https://techsummer.law-school.de/
And the conversation continues at #BuceriusLegalTech …
ARE YOU READY? Bucerius Legal Tech Essentials starts this week with our Intro Session on Thursday (regular sessions begin on Monday June 29th) … Over 3200+ Participants from 90+ Countries and the Conversation continues at #BuceriusLegalTech …
You can still Sign up today for FREE — https://techsummer.law-school.de/
Thanks as always to Baker McKenzie for Sponsoring and thank you to our global academic and organizational partners (Stanford CodeX, Chicago Kent & European Legal Technology Association) !
Nice Shoutout (and perhaps a more approachable explanation) for our paper on the Santa Fe Institute Website.
“We propose a generalizable approach for identifying pivotal components across a wide variety of systems,” says author Edward Lee, a Program Postdoctoral Fellow who studies collective behavior at the Santa Fe Institute. “These systems go beyond voting, and include social media (like Twitter), biology (like the statistics of neurons), or finance (like fluctuations of the stock market).”
In the paper, Lee and his co-authors, Daniel Katz (Illinois Tech), Michael Bommarito (Stanford CodeX), and Paul Ginsparg (Cornell University) identify a statistical signature of pivotal components that they then trace to communities on Twitter, votes in the Supreme Court and Congress, and stock indices within financial markets. They find wide diversity in how social systems depend on sensitive points, when such points exist at all.”
Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components in wide variety of systems. We then apply this approach to a wide variety of empirical datasets including political voting, financial markets and social systems. We find remarkable variety from systems dominated by a median-like component to those without any single special component. Other systems (e.g., S&P sector indices) show varying levels of heterogeneity in between these extremes. Our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of collective outcomes to targeted perturbation and compare social institutions, or even biological networks, with one another and across time.