grantmaking.ai Launch Round
$5,000
$10,000
Personal living expenses.
Develop a decision-theory framework for AI self-alignment in nested, cyclic, partially observable multi-agent environments where delayed punishment for welfare compromise promotes cooperative behavior.
Develop a decision-theory framework for AI self-alignment in nested, cyclic, partially observable multi-agent environments where delayed punishment for welfare compromise promotes cooperative behavior.
Project Details
Updated 07/09/26 · By grantmaking.ai · VerifiedThe project's core idea is to develop a decision theory framework for self-alignment of AI systems through recursively nested, cyclic, and partially observable environments. In these environments, multiple agents with potentially conflicting goals interact without direct access to the loss functions of other agents, but are forced to infer each other's goals through interaction. Agents that compromise the welfare function are systematically "punished" with a delay, which should encourage the emergence of cooperative and stable aligned behavior without explicit external supervision.
Theory of Impact
Updated 07/09/26 · By grantmaking.aiThanks to the delayed punishment and varying levels of simulation, no agent can be sure that they are not inside a simulation controlled by a higher level of supervision. Therefore, even powerful agents are motivated to behave safely when released into reality, fearing punishment from non-existent higher levels.
This architecture aims to simultaneously mitigate several classical problems by building harm minimization directly into the reward structure and by making it structurally impossible for agents to modify the loss functions of others with impunity.
People
Updated 07/09/26 · By grantmaking.aiTeam Member
Funding Asks
Applied
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