1-convex extensions of incomplete cooperative games and the average value

TL;DR

This paper studies 1-convex extensions of incomplete cooperative games, proposing methods to complete missing data, define fair payoff solutions, and introduces the average value as a new solution concept with axiomatic foundations.

Contribution

It introduces the concept of 1-convex extensions for incomplete games, generalizes solution concepts, and defines the average value with axiomatic characterizations.

Findings

Set of 1-convex extensions characterized by extreme points and rays.

The average value coincides with classical solutions for minimal incomplete games.

Different conclusions are reached for games with upper vector constraints.

Abstract

The model of incomplete cooperative games incorporates uncertainty into the classical model of cooperative games by considering a partial characteristic function. Thus the values for some of the coalitions are not known. The main focus of this paper is the class of 1-convex cooperative games under this framework. We are interested in two heavily intertwined questions. First, given an incomplete game, in which ways can we fill in the missing values to obtain a classical 1-convex game? Such complete games are called \emph{1-convex extensions}. For the class of minimal incomplete games (in which precisely the values of singletons and grand coalitions are known), we provide an answer in terms of a description of the set of 1-convex extensions. The description employs extreme points and extreme rays of the set. Second, how to determine in a rational, fair, and efficient way the payoffs of players based only on the known values of coalitions? Based on the description of the set of 1-convex extensions, we introduce generalisations of three solution concepts (values) for complete games, namely the $\tau$-value, the Shapley value and the nucleolus. We consider two variants where we compute the centre of gravity of either extreme games or of a combination of extreme games and extreme rays. We show that all of the generalised values coincide for minimal incomplete games which allows to introduce the \emph{average value}. For this value, we provide three different axiomatisations based on axiomatic characterisations of the $\tau$-value and the Shapley value for classical cooperative games. Finally, we turn our attention to \emph{incomplete games with defined upper vector}, asking the same questions and this time arriving to different conclusions. This provides a benchmark to test our tools and knowledge of the average value.