  I study collective dynamic information acquisition. Players determine when to
end sequential sampling via a collective stopping rule. With two players,
collective stopping is determined either unilaterally or unanimously. I develop
a methodology to characterize equilibria using an ex ante perspective on
posterior distributions. Under unilateral stopping, each player chooses a
mean-preserving contraction of the other's posterior distribution; under
unanimous stopping, they choose mean-preserving spreads. Equilibrium sampling
regions are determined via concavification. Preference misalignment generates
Pareto inefficiency: there is too little learning under unilateral stopping and
possibly too much under unanimous stopping. Learning inefficiencies are
amplified when players' preferences become less aligned. I demonstrate the
value of my methodology in three applications: committee search, dynamic
persuasion, and competition in persuasion.
