On the Approximation of the Shapley Value via Machine Learning i n Transportation Network Cooperative Games
Abstract:
The Shapley value, a well-established concept i n cooperative game theory, serves as a metric for assessing the significanc e of each player in a transferable utility game. Recently, it has found app lication in gauging the importance of individual nodes or arcs within a net work. However, in this context, the exact evaluation of the Shapley value i s often computationally expensive, particularly in the case of extensive ne tworks. This study delves into the challenge of approximating the Shapley v alue in a transferable utility game defined on a network, wherein the chara cteristics of the network are parameterized by a variable of interest (e.g. , the traffic demand). We examine the smoothness of the Shapley value with respect to this parameter and leverage such smoothness to theoretically jus tify the adoption of machine-learning techniques for its approximate comput ation. Additionally, we present potential extensions for further research i n this area.
DTSTAMP:20260409T194600Z DTSTART:20260223T130000Z DTEND:20260223T140000Z SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR