of an optimal outcome, we make the following basic assumptions regarding our modified game:

1.  We assume that characteristic function $v(.)$ used to value predictive models is **monotonic**. That is, if $C \subseteq C'$, then $v(C) \leq v(C')$; this assumption implies that the value (as valued by the characteristic function) of the model generated by the largest group of players $N$ is also the largest. This assumption implies that larger coalition generates more value than smaller ones.

2.  We use the ***shapley value***, as defined in Section 3.1.3, as the relative contribution measure $\mathcal{C}$ to gauge a player’s contribution in the data sharing process. As mentioned, the ***shapley value*** has many desirable properties.

With these assumptions, we introduce a general algorithm to determine the reward allocated to each player in a modified cooperative game. This algorithm will be used by a central agency who receives the data from all participating players.

$\phi \gets$ shapley($v(.), D_1,D_2,\dots,D_n$);

In the following sections, we analyse how functions $getOptimalOutcome$ and $getSuboptimalOutcome$ derive optimal and suboptimal outcomes. The central agency can use these results to allocate rewards for the participating players.

Optimal outcome
===============

To contextualise an optimal outcome in our modified game to a description 