regardless of how much they contributed to it (of course, this payoff is not fair). As such, the *shapley value* should merely give us an idea of what the relative contribution of each player is, and not dictate the final division of reward to each player.

In the following section, we will modify conventional assumptions surrounding cooperative games to suit a multi-party machine learning data-sharing process and analyse what can be ideal outcomes in such modified games. In particular, we observe that the model created by coalitions in a data-sharing process can be categorised as a *non-rivalrous good*, where players can enjoy part of it without diminishing its availability to other players. Furthermore, we use the *Shapley value* merely as a measure of contribution between the players and not as an indicator of the reward that should be allocated to each of them.

Deviating from conventional cooperative games
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Overview
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We wish to capture the notion that resources created are *non-rivalrous* in our modified game. In fact, each player can receive as high as the total value created by the group. However, the central agency mediating the collaborative process is able to control the level of reward given to 