Funding public projects: A Case for the Nash product rule
Florian Brandl (University of Bonn), Felix Brandt (Technische Universität München), Matthias Greger (Technische Universität München), Dominik Peters (University of Toronto), Christian Stricker (Technische Universität München) and Warut Suksompong (National University of Singapore)
GPI Working Paper No. 14-2021, published in Journal of Mathematical Economics
We study a mechanism design problem where a community of agents wishes to fund public projects via voluntary monetary contributions by the community members. This serves as a model for public expenditure without an exogenously available budget, such as participatory budgeting or voluntary tax programs, as well as donor coordination when interpreting charities as public projects and donations as contributions. Our aim is to identify a mutually beneficial distribution of the individual contributions. In the preference aggregation problem that we study, agents report linear utility functions over projects together with the amount of their contributions, and the mechanism determines a socially optimal distribution of the money. We identify a specific mechanism—the Nash product rule—which picks the distribution that maximizes the product of the agents’ utilities. This rule is Pareto efficient, and we prove that it satisfies attractive incentive properties: it spends each agent’s contribution only on projects the agent finds acceptable, and agents are strongly incentivized to participate.
Other working papers
Existential Risk and Growth – Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford) and Leopold Aschenbrenner
Technologies may pose existential risks to civilization. Though accelerating technological development may increase the risk of anthropogenic existential catastrophe per period in the short run, two considerations suggest that a sector-neutral acceleration decreases the risk that such a catastrophe ever occurs. First, acceleration decreases the time spent at each technology level. Second, since a richer society is willing to sacrifice more for safety, optimal policy can yield an “existential risk Kuznets curve”; acceleration…
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. …
AI alignment vs AI ethical treatment: Ten challenges – Adam Bradley (Lingnan University) and Bradford Saad (Global Priorities Institute, University of Oxford)
A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching…