Algorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestion

In today's digital economy, there are numerous situations where many players have to compete for limited resources. For instance, on ride-hailing platforms such as Uber and Lyft, drivers pick an area to go and then compete with other drivers for riding requests at that area; on content platforms such as Youtube and Tiktok, content providers choose a style/theme for their contents and then compete with other providers of the same theme for Internet traffic interested in that theme; on digital markets such as Amazon and Wayfair, retailers choose a particular product category (e.g., pet supplies or home&kitchen, etc.) to focus on and compete with other retailers for sale demands on that category. All these problems share the following similarity: (1) many players make a choice (e.g., a ride-sharing area or a content theme) from multiple options and their payoffs has negative externalities with other players of the same choice due to competition; (2) players have high uncertainty about the payoffs of their choices since the entire system's demand of riding requests or Internet traffic are unknown to an individual player, whereas the system usually has much fined-grained information about these uncertainties. An important operational task common in all these applications is the following: how can the system (the sender) strategically reveal her privileged information to influence the decisions of so many players (the receivers) in order to steer their collective decisions towards a desirable social outcome? This task, also known as information design or persuasion [1, 2, 3, 4], has attracted extensive recent interests.