  Cross-group externalities and network effects in two-sided platform markets
shape market structure and competition policy, and are the subject of extensive
study. Less understood are the within-group externalities that arise when the
platform designs many-to-many matchings: the value to agent $i$ of matching
with agent $j$ may depend on the set of agents with which $j$ is matched. These
effects are present in a wide range of settings in which firms compete for
individuals' custom or attention. I characterize platform-optimal matchings in
a general model of many-to-many matching with within-group externalities. I
prove a set of comparative statics results for optimal matchings, and show how
these can be used to analyze the welfare effects various changes, including
vertical integration by the platform, horizontal mergers between firms on one
side of the market, and changes in the platform's information structure. I then
explore market structure and regulation in two in-depth applications. The first
is monopolistic competition between firms on a retail platform such as Amazon.
The second is a multi-channel video program distributor (MVPD) negotiating
transfer fees with television channels and bundling these to sell to
individuals.
