Two-sided platforms play an important role in reducing frictions and facilitating trade, and in doing so they increasingly engage in collecting and processing data about supply and demand. This paper establishes that platforms have an incentive to strategically disclose (coarse) information about demand to the supply side as this can considerably boost their profits. However, this practice may also adversely affect the welfare of consumers. By optimally designing its information disclosure policy, a platform can influence the entry and pricing decisions of its potential suppliers. In general, it is optimal for the platform to disclose its information only partially to either "nudge'' entry when it is a priori costly for suppliers to join or, conversely, discourage it when suppliers do not have access to attractive outside options. On the other hand, consumers may end up being worse off as they have access to fewer trading options and/or face higher prices compared to when the platform refrains from sharing any demand information to its potential suppliers.
We develop a theoretical model to study strategic interactions between adaptive learning algorithms. Applying continuous-time techniques, we uncover the mechanism responsible for collusion between Artificial Intelligence algorithms documented by recent experimental evidence. We show that spontaneous coupling between the algorithms' estimates leads to periodic coordination on actions that are more profitable than static Nash equilibria. We provide a sufficient condition under which this coupling is guaranteed to disappear, and algorithms learn to play undominated strategies. We apply our results to interpret and complement experimental findings in the literature, and to the design of learning-robust strategy-proof mechanisms. We show that ex-post feedback provision guarantees robustness to the presence of learning agents. We fully characterize the optimal learning-robust mechanisms: they are menu mechanisms.