For homogeneous agents in multi-agent linear bandits the regret-based TU game is convex with non-empty core containing the Shapley value; for heterogeneous agents a simple regret-based payout lies in the core and satisfies three Shapley axioms.
This is a quantity internal to Mul algorithm that depends on actions chosen by Sin, and not a quantity inherent to the bandit problem
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2026 1verdicts
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Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
For homogeneous agents in multi-agent linear bandits the regret-based TU game is convex with non-empty core containing the Shapley value; for heterogeneous agents a simple regret-based payout lies in the core and satisfies three Shapley axioms.