following manner: $$\begin{split}
 & v(A) = 1
\\
 & v(B) = 1
 \\
 & v(A \cup B) = 2
\end{split}$$ Based on conventional outcomes studied in cooperative game theory, if the players choose to collaborate, the only stable solution payoff is ${x_A,x_B} = {1,1}$. Furthermore, the shapley value gives us $\phi_A,\phi_B = 1,1$ as well. But clearly, since players are producing predictive models, which can be duplicated for free, we should be rewarding both players with the payoff ${x_A,x_B} = {2,2}$ (notice we did not create resources from the thin air, but merely duplicated them).\
\
We first try to understand why this happens. In example (2), the *shapley value* explains the contribution of each player well: it seems correct that both players have contributed to $v(A \cup B)$ equally. However, we observe that, unlike conventional cooperative games, predictive models created from the data sharing process can be duplicated in part or entirely for negligible cost. As such, we do not have to restrict our total payoff to a coalition of players such that it sums to the value of one data model. In fact, in the extreme case, we can distribute the same entire learnt model to each player, 