Matching information
研究了如何最优分配信息精度不同的专家到团队中，发现通常应多样化团队构成，导致负向匹配，且条件独立时各团队精度相近。
We analyze the optimal allocation of experts to teams, where experts differ in the precision of their information, and study the assortative matching properties of the resulting assignment. The main insight is that in general it is optimal to diversify the composition of the teams, ruling out positive assortative matching. This diversification leads to negative assortative matching when teams consist of pairs of experts. And when experts' signals are conditionally independent, all teams have similar precision. We also show that if we allow experts to join multiple teams, then it is optimal to allocate them equally across all teams. Finally, we analyze how to endogenize the size of the teams, and we extend the model by introducing heterogeneous firms in which the teams operate.