Learning

We consider the design of service systems that process tasks with types that are ex ante unknown, and employ servers with different skill sets. We show that the performance loss due to the uncertainty can be significant and that the system’s stability region is dependent on the rate at which information about tasks’ types is generated. Furthermore, we consider endogenizing the servers’ capabilities and explore the problem of jointly optimizing over training and staffing levels and the resource allocation policy. We find that among optimal designs there always exists one with a hierarchical structure, where all tasks are initially routed to the least skilled servers and then progressively move to more skilled ones, if necessary.

Participants race towards completing an innovation project and learn about its feasibility from their own efforts and their competitors’ gradual progress. Information about the status of competition can alleviate some of the uncertainty inherent in the contest, but it can also adversely affect effort provision from the laggards. We show that the probability of obtaining the innovation as well as the time it takes to complete the project are largely affected by when and what information the designer chooses to disclose. We establish that intermediate awards may be used by the designer to disseminate information about the status of competition. Our proposed design matches several features observed in real-world innovation contests.

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. We define asymptotic learning as the fraction of agents taking the correct action converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from “information hubs,” which receive and distribute a large amount of information.