Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

Fair reward distribution is computationally challenging but solvable with AI: The paper demonstrates that neural networks can learn to predict fair payoff allocations based on cooperative game theory principles (like the Shapley value), overcoming computational barriers that make traditional fairness calculations impractical for real-world applications.

AI models can generalize fairness principles beyond their training experience: The trained neural networks successfully predicted fair allocations for games with more players than they encountered during training and for games substantially different from their training data, suggesting the models learned generalizable principles of fairness rather than memorizing specific cases.

This approach has immediate practical applications for explainability: The framework can accelerate Shapley value computations, which are used to explain AI model decisions by identifying which input features contribute most to predictions—making AI systems more transparent and trustworthy.

Balancing fairness with stability matters for team cooperation: The paper addresses two complementary fairness concepts—the Shapley value (which reflects individual contributions) and the Core (which prevents team members from leaving)—highlighting that sustainable cooperation requires both fair reward distribution and incentives for members to stay committed.

Machine learning can democratize access to fairness solutions: By distilling complex game-theoretic solutions into learned models, this work makes sophisticated fairness allocation methods computationally accessible for applications like cost-sharing in joint ventures and feature importance analysis that might otherwise be prohibitively expensive to calculate.