We propose a model in which dividend payments occur at regular, deterministic intervals in an otherwise continuous model. This contrasts traditional models where either the payment of continuous dividends is controlled or the dynamics are given by discrete time processes. Moreover, between two dividend payments, the structure allows for…

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors…

We study optimal manipulation of a Bayesian learner through adaptive provisioning of information. The problem is motivated by settings in which a firm can disseminate possibly biased information at a cost, to influence the public’s belief about a hidden parameter related to the firm’s payoffs. For example, firms advertise…

Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, has been derived only…