The 2022 Berggruen Governance Index

A New Tool to Measure Governance

The Berggruen Governance Index Project is a collaborative project between the UCLA Luskin School of Public Affairs and the Berggruen Institute. The current iteration of the Index examines the performance of 135 countries in key areas over a 20-year period to advance our understanding of why some countries are better managed and enjoy a higher quality of life than others. To this end, the Index analyzes the relationship between democratic accountability, state capacity, and the provision of goods to serve public needs.

The project emerged from a series of conversations between Nicolas Berggruen, Nathan Gardels, and Dawn Nakagawa at the Berggruen Institute, and Helmut K. Anheier, then president of the Hertie School in Berlin. The Berggruen Institute supported a preliminary phase to explore the feasibility of such a governance index. Based on this initial work, and the publication of the 2019 Index, the project expanded and moved to the Luskin School of Public Affairs at UCLA.

Helmut K. Anheier serves as Principal Investigator of the project at UCLA, with Markus Lang as lead analyst, and Dawn Nakagawa as project director at the Berggruen Institute.

The project is guided by a board that includes Nabil Fahmy (American University, Cairo), Mohammed Ibrahim (Mo Ibrahim Foundation), Francis Fukuyama (Stanford), Margaret Levi (Stanford), John Micklethwait (Bloomberg), Gary Segura (UCLA Luskin), Anne-Marie Slaughter (New America), Michael Woolcock (World Bank), and Xue Lan (Tsinghua University).

More information about the project and the 2022 BGI:
governance.luskin.ucla.edu
berggruen.org/2022-governance-index

When using the Berggruen Governance Index data, please make sure to include a proper citation or reference to this database: “Anheier, Helmut K. 2022. The 2022 Berggruen Governance Index Data. Berlin and Los Angeles: Hertie School and UCLA Luskin School of Public Affairs.”

Downloads:

Full Report
Executive Summary
Dataset
CodeBook

People

Dawn Nakagawa

Dawn Nakagawa

Executive Vice President
Helmut K. Anheier

Helmut K. Anheier

Principal Investigator

composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.

RAVE (IRCAM 2021) https://github.com/acids-ircam/RAVE