Computational Aspects of Cooperative Game Theory

By Michael J. Wooldridge. Agent and Multi-Agent Systems: Technologies and Applications, 2011

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Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems (2016, background): “Computing the Shapley or Banzhaf values exactly is generally computationally intractable (see [21, Chapter 4] for a general overview); however, their probabilistic nature means that they can be well-approximated via random sampling.”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “Cooperative game theory concentrates on division of the payoff, and not so much on what players do to achieve those payoffs [223].”
Multi-objective multi-agent decision making: a utility-based analysis and survey (2019, background): “Stable solutions come in many different levels of strictness [35], from core stability, to Nash equilibria, to individual stability.”
Shapley values for feature selection: The good, the bad, and the axioms (2021, unknown): “While much attention has been paid by game theorists and economists to interpreting and reformulating the axioms, and towards investigating axiom sets (and game formulations) that lead to Shapley value alternatives [28], [29], [31], we found comparatively little attention to these matters in ML [24]”