  We introduce a simple benchmark model of dynamic matching in networked
markets, where agents arrive and depart stochastically and the network of
acceptable transactions among agents forms a random graph. We analyze our model
from three perspectives: waiting, optimization, and information. The main
insight of our analysis is that waiting to thicken the market can be
substantially more important than increasing the speed of transactions, and
this is quite robust to the presence of waiting costs. From an optimization
perspective, naive 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
partial 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.
