When self-interested agents compete for scarce resources, they often exaggerate the promise of their activities. As such, principals must consider both the quality of each opportunity and each agent’s credibility. We show that principals are better off with less transparency because they gain...

Challenge funds (CF) induce competition between grant applicants as they develop proposals to address important social problems. We develop a game theoretic model to study how funding availability and proof of concept requirements (e.g., pilots or other forms of early stage screening) influence...

In a model of project design, evaluation, and selection, we explore how the incentives to improve the design of projects depend on the availability of funding and the process of evaluation. We show that project designers (researchers or NGOs) prefer to subject their projects to less-rigorous...

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing...

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher...

This paper investigates the extent to which syndication in financial markets is related to collusive behavior. A group of financiers who have private information regarding their capability of monitoring an entrepreneur must decide whether to provide a loan individually in a competitive fashion,...