"Hodge-Shapley game: a Laplacian-based Shapley-like associated game for eXplainable AI"
Abstract:
In cooperative game theory, a set of players or decision-makers should negotiate to decide how to allocate the worth gained by the coalition composed of all the players. A value is a solution concept that suggests the outcome of the negotiation among players. Among the many existing alternative solution concepts, it is prevalent the Shapley value solution concept. Its popularity also derives from the property of being a fair allocation, where a set of desirable properties or axioms describes fairness. The axioms characterize the Shapley value in the sense that it is the unique value satisfying those properties; at the same time, the axioms allow deriving a simple explicit combinatorial formula to compute the Shapley value. In our approach, coalitions are the main subjects of cooperation, instead of single players, and, inspired by the Shapley value, the goal is to derive a fair associated game, i.e. an allocation to coalitions satisfying a set of desirable properties. The methodology is based on using the Hodge decomposition of the simplicial complex associated with the partially ordered set of the subsets of the set of players ordered by inclusion. We will motivate this investigation within the framework of Explainable Artificial Intelligence (XAI).Joint work with Antonio Mastropietro (Eurecom - F)
We encourage in-person partecipation. Should you be unable to come, here is the link to the event on Teams:
The seminar is part of the Excellence Project Math@TOV.
You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .