arXiv:2602.06361v2 Announce Type: replace-cross Abstract: We introduce a problem of fairly allocating indivisible goods (items) in which the agents' valuations cannot be observed directly, but instead can only be accessed via noisy queries. In the two-agent setting with Gaussian noise and bounded valuations, we derive upper and lower bounds on the required number of queries for finding an envy-free allocation in terms of the number of items, $m$, and the negative-envy of the optimal allocation, $\Delta$. In particular, when $\Delta$ is not too small (namely, $\Delta \gg m^{1/4}$), we establish
This research addresses a fundamental computational problem in fair resource allocation, a core challenge for complex AI systems and distributed decision-making.
Fair allocation with noisy data is critical for developing robust and trustworthy AI agents that can operate in real-world, imperfect information environments, impacting resource distribution and ethical AI design.
The ability to achieve envy-free allocations despite noisy information moves theoretical AI closer to practical, deployable agentic systems for resource management.
- · AI researchers
- · Multi-agent system developers
- · Organizations implementing automated resource management
- · Systems reliant on perfect information
- · Inefficient resource allocation methods
Improved fairness and efficiency in automated resource allocation systems built with AI agents.
Reduced friction in complex distributed systems where agents must bargain or allocate resources based on uncertain preferences.
Enhanced development of fully autonomous AI economies or governance structures where agents negotiate and distribute goods under realistic conditions.
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