A Mechanism for Mutual Fairness in Cooperative Games with Replicable Resources -- Extended Version
Bj\"orn Filter, Ralf M\"oller, \"Ozg\"ur L\"utf\"u \"Oz\c{c}ep

TL;DR
This paper introduces a new mechanism for fair resource allocation in cooperative systems with replicable resources, ensuring mutual fairness and balanced reciprocity among participants.
Contribution
It proposes a novel mechanism and proof that guarantees mutual fairness in cooperative games with replicable resources, extending classical fairness concepts.
Findings
The mechanism satisfies the Balanced Reciprocity Axiom.
It ensures equal benefits for all pairs of players.
Addresses fairness issues in replicable resource scenarios.
Abstract
The latest developments in AI focus on agentic systems where artificial and human agents cooperate to realize global goals. An example is collaborative learning, which aims to train a global model based on data from individual agents. A major challenge in designing such systems is to guarantee safety and alignment with human values, particularly a fair distribution of rewards upon achieving the global goal. Cooperative game theory offers useful abstractions of cooperating agents via value functions, which assign value to each coalition, and via reward functions. With these, the idea of fair allocation can be formalized by specifying fairness axioms and designing concrete mechanisms. Classical cooperative game theory, exemplified by the Shapley value, does not fully capture scenarios like collaborative learning, as it assumes nonreplicable resources, whereas data and models can be…
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