Quadratic Funding with Incomplete Information
Luis V. M. Freitas (Global Priorities Institute,
University of Oxford) and Wilfredo L. Maldonado
(University of Sao Paulo)
GPI Working Paper No. 10 - 2022, published in Social Choice and Welfare
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 in population size and in the variance of expected contribution to the public good. We show how these findings can be used to estimate the mechanism’s inefficiency in a wide range of situations under incomplete information.
Other working papers
Respect for others’ risk attitudes and the long-run future – Andreas Mogensen (Global Priorities Institute, University of Oxford)
When our choice affects some other person and the outcome is unknown, it has been argued that we should defer to their risk attitude, if known, or else default to use of a risk avoidant risk function. This, in turn, has been claimed to require the use of a risk avoidant risk function when making decisions that primarily affect future people, and to decrease the desirability of efforts to prevent human extinction, owing to the significant risks associated with continued human survival. …
Intergenerational experimentation and catastrophic risk – Fikri Pitsuwan (Center of Economic Research, ETH Zurich)
I study an intergenerational game in which each generation experiments on a risky technology that provides private benefits, but may also cause a temporary catastrophe. I find a folk-theorem-type result on which there is a continuum of equilibria. Compared to the socially optimal level, some equilibria exhibit too much, while others too little, experimentation. The reason is that the payoff externality causes preemptive experimentation, while the informational externality leads to more caution…
What power-seeking theorems do not show – David Thorstad (Vanderbilt University)
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.