This paper studies the information design problem of a retailer facing two upstream suppliers to whom it can privately disclose information about market demand. One supplier holds priority in fulfilling orders, while the other serves as a backup. The retailer seeks to minimize expected lost-sales costs, whereas suppliers minimize the sum of lost-sales and excess-inventory holding costs. We model the interaction between the retailer and suppliers using a Bayesian persuasion framework in which the retailer commits ex ante to an information disclosure policy and privately sends demand signals to each supplier, who then chooses production quantities. Our main finding is that asymmetric disclosure characterizes the retailer’s optimal policy. Specifically, the retailer strategically favors the lower-priority supplier by providing more informative signals, while keeping the higher-priority supplier relatively less informed. This result relies on very mild assumptions on demand and remains robust to heterogeneous critical ratios and alternative procurement rules. Our results highlight that the value of information sharing depends critically on how disclosure policies interact with procurement structures. In priority sourcing environments, selectively informing lower-priority suppliers can be more effective than uniformly sharing information across suppliers in reducing the retailer’s expected shortage costs.

This paper studies contests in which contestants with different designs compete via effort to win a prize from the host of the competition. The host's ideal design is her private information, which she can disclose to the contestants before they choose their effort levels. We investigate how the host's disclosure policy affects the effort choices of contestants when her objective is to maximize aggregate effort. We find that non-disclosure leads to symmetric equilibria while disclosure induces asymmetry. We derive equilibrium effort levels with and without disclosure and identify a threshold prize value above which disclosure yields a higher aggregate effort. 

This paper explores the strategic allocation of recognition tokens, such as awards, badges, staff picks and trophies (so called non-monetary incentives), in User-Generated Content (UGC) platforms to promote high-quality content and sustain user engagement. Through a stylized model, we investigate the dynamics between the platform, content producers, and consumers (viewers) across diverse content popularity and quality. Specifically, we are interested in the implications of the non-monetary incentives on competition among producers and overall platform engagement. Our model elucidates the trade-offs involved in recognition token generosity, emphasizing the balancing act required to maintain a sense of achievement and exclusivity among users. A central challenge is to optimize the allocation of these tokens to encourage broad user engagement while ensuring that each award remains meaningful. This entails carefully managing the disutility arising from perceived unfairness or bias in how tokens are awarded. Our study aims to identify the optimal point where incentives maximize engagement without diluting the value of recognition, thereby offering valuable perspectives for platform owners and moderators in enhancing platform success and sustainability