This paper revisits Wald's (1947) sequential experimentation paradigm, now assuming that an impatient decision maker can run variable-size experiments each period at some increasing and strictly convex cost before finally choosing an irreversible action. We translate this natural discrete time...

This paper produces a comprehensive theory of the value of Bayesian information and its static demand. Our key insight is to assume 'natural units' corresponding to the sample size of conditionally i.i.d. signals -- focusing on the smooth nearby model of the precision of an observation of a...

In the social learning model of Banerjee [1] and Bikhchandani, Hirshleifer and Welch [2] individuals take actions sequentially after observing the history of actions taken by the predecessors and an informative private signal. If the state of the world is changing stochastically over time during...

An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite-state,...

We study the design of optimal incentives in a two-player dynamic contest. Two players continuously spend costly effort to attain a score lead, which is also affected by noise. The first player to reach a predetermined score difference (finish line) wins a prize. We focus on the choice of the...

Do workers sort more randomly across different job types when jobs are harder to find? To answer this question, we study the mobility of male workers among three-digit occupations in the matched files of the monthly Current Population Survey over the 1979-2004 period. We clean individual...