Networks of social and economic interactions are often influenced by unobserved structures among the nodes. Based on a simple model of how an unobserved community structure generates networks of interactions, we axiomatize a method of detecting the latent community structures from network data. The method is based on maximum likelihood estimation. * Č opič is the department of economics at UCLA,

Because they provide exclusive property rights, patents are generally considered to be an effective way to promote intellectual discovery. Here, we propose a different compensation scheme, in which everyone holds shares in the components of potential discoveries and can trade those shares in an anonymous market. In it, incentives to invent are indirect, through changes in share prices. In a series of experiments, we used the knapsack problem (in which participants have to determine the most valuable subset of objects that can fit in a knapsack of fixed volume) as a typical representation of intellectual discovery problems. We found that our "markets system" performed better than the patent system.

A risk neutral seller and buyer with private information bargain over an indivisible item. We prove that optimal robust bilateral trade mechanisms are payoff equivalent to non-wasteful randomized posted prices.