What power-seeking theorems do not show
David Thorstad (Vanderbilt University)
GPI Working Paper No. 27-2024
Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is that artificial agents may be power-seeking, aiming to acquire power and in the process disempowering humanity. A range of power-seeking theorems seek to give formal articulation to the idea that artificial agents are likely to be power-seeking. I argue that leading theorems face five challenges, then draw lessons from this result.
Other working papers
Existential risk and growth – Leopold Aschenbrenner (Columbia University)
Human activity can create or mitigate risks of catastrophes, such as nuclear war, climate change, pandemics, or artificial intelligence run amok. These could even imperil the survival of human civilization. What is the relationship between economic growth and such existential risks? In a model of directed technical change, with moderate parameters, existential risk follows a Kuznets-style inverted U-shape. …
Future Suffering and the Non-Identity Problem – Theron Pummer (University of St Andrews)
I present and explore a new version of the Person-Affecting View, according to which reasons to do an act depend wholly on what would be said for or against this act from the points of view of particular individuals. According to my view, (i) there is a morally requiring reason not to bring about lives insofar as they contain suffering (negative welfare), (ii) there is no morally requiring reason to bring about lives insofar as they contain happiness (positive welfare), but (iii) there is a permitting reason to bring about lives insofar as they…
Quadratic Funding with Incomplete Information – Luis M. V. Freitas (Global Priorities Institute, University of Oxford) and Wilfredo L. Maldonado (University of Sao Paulo)
Quadratic funding is a public good provision mechanism that satisfies desirable theoretical properties, such as efficiency under complete information, and has been gaining popularity in practical applications. We evaluate this mechanism in a setting of incomplete information regarding individual preferences, and show that this result only holds under knife-edge conditions. We also estimate the inefficiency of the mechanism in a variety of settings and show, in particular, that inefficiency increases…