We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new...

We consider the problem of designing optimal mechanisms for settings where agents have dynamic private information. We present the Virtual-Pivot Mechanism, that is optimal in a large class of environments that satisfy a separability condition. The mechanism satisfies a rather strong equilibrium...

We study a firm's optimal pricing policy under price commitment. The firm's objective is to maximize its long-term average revenue given a steady arrival of strategic customers. In particular, customers arrive over time, are strategic in timing their purchases and are heterogeneous along two...

The likely arrival of autonomous vehicle (AV) technology in the near future has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread use of AVs is via open platforms that can sustain high-utilization,...

We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firms needs to price these products to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the...

We study the (perfect Bayesian) equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible...

We study the relationship between capacity and performance for a service firm with spatial operations, in the sense that requests arrive with origin-destination pairs. An example of such a system is a ride-hailing platform in which each customer arrives in the system with the need to travel from...

We study a sequential model of Bayesian social learning in networks in which agents have heterogeneous preferences, and neighbors tend to have similar preferences — a phenomenon known as homophily. We find that the density of network connections determines the impact of preference diversity...

We consider decentralized platforms facilitating many-to-many matches between two sides of a marketplace. In the absence of direct matching, inefficiency in market outcomes can easily arise. For instance, popular supply agents may garner many units from the demand side, while other supply units...