I have been collaborating with my PhD student, Qianni Wang, on information design for over a year. This paper—currently under review at Transportation Science—is the first fruit of our dedicated effort, and I am confident it will not be the last.

At its core, the idea behind information design is both simple and fascinating. If you are not yet familiar with it, I encourage you to explore Bayesian Persuasion, one of the earliest and most influential works published on the subject.

Information asymmetry—where one party holds information unavailable to others — lies at the heart of many important problems in economic and sociotechnical systems . In principal-agent problem (or the incentive problem), for example, the interests of the principal and agents diverge because the principal cannot perfectly monitor agents’ hidden actions or intentions. In the selection problem, one party in a transaction has private information that can lead to mismatches or undesirable outcomes. The beauty contest (or social learning) problem—which causes socially harmful herd behaviors—arise from individuals’ imperfect information, both of the system state and of what others know. The adverse effects of information asymmetry may be mitigated by aligning incentives, increasing transparency and inducing truthful behavior, often achieved through mechanism design.

Information design is motivated by information asymmetry that strongly favors the principal over the agents in a principal-agent game. In the context of transportation management, the principal is the manager and the agents are the users. Here, the users’ payoff depends on certain information observable only to the manager. As a result, the manager can exploit the information asymmetry to better align the interests of the two parties through persuasion . A unique feature of information design is that the manager is committed to an information structure ex-ante, which provides transparency and ensures incentive compatibility of the users.

This paper applies the concept of information design to a particular transportation application: resolving chaos in EV charging.  You can find an abstract below, and download a preprint here.

Charging remains an obstacle to the mass adoption of electric vehicles (EVs), especially for long-distance travel. If many EV drivers take to the road roughly at the same time, their “range anxiety” may create a self-fulfilling prophecy. As the drivers anticipate uncertainty and congestion at charging stations, they tend to make overly conservative decisions about when and where to charge. Yet, these decisions could worsen the very problem they try to prevent, collectively leading to chaos and inefficiency. Here, we show that information design can be used to persuade the drivers to adopt decisions that are better for the system while being consistent with their self-interest as defined by Bayesian Nash equilibrium. Our stylized model incorporates the congestion effect into the drivers‘ payoffs. It also assumes that the information designer has private knowledge about the state of charging and the driver type, defined by vehicle range. We consider both public and private information designs. The former does not depend on the driver type while the latter does. For the private design problem, we propose a novel cutoff structure that enables us to reformulate an infinite-dimensional problem as a finite one. When the random charging state can only take two discrete values, we prove that the optimal public design equals full information revelation. The optimal private design, however, promises significantly better results, potentially delivering the system optimal outcome under favorable conditions — e.g. when the charging state is highly uncertain and the marginal cost is similar regardless of the charging decision. We also show that the value of information increases with the level of uncertainty and the extra cost imposed by charging.