Contents

We introduce a simple benchmark model of dynamic matching in networked mar-kets, where agents arrive and depart stochastically and the network of acceptable trans-actions among agents forms a random graph. We analyze our model from three per-spectives: timing, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increas-ing the frequency of transactions, and by characterizing the optimal trade frequency under discounting, we show that this insight is quite robust to the presence of waiting costs. From an optimization perspective, näıve local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even short-horizon information on agents ’ departure times perform substantially better than those that lack such information. To elicit agents ’ departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers