  Data sharing issues pervade online social and economic environments. To
foster social progress, it is important to develop models of the interaction
between data producers and consumers that can promote the rise of cooperation
between the involved parties. We formalize this interaction as a game, the data
sharing game, based on the Iterated Prisoner's Dilemma and deal with it through
multi-agent reinforcement learning techniques. We consider several strategies
for how the citizens may behave, depending on the degree of centralization
sought. Simulations suggest mechanisms for cooperation to take place and, thus,
achieve maximum social utility: data consumers should perform some kind of
opponent modeling, or a regulator should transfer utility between both players
and incentivise them.
