Dynamic Directed Random Matching
We develop a general and unified model in which a continuum of agents conduct directed random searches for counterparties. Our results provide the first probabilistic foundation for static and dynamic models of directed search (including the matching-function approach) that are common in search-based models of financial markets, monetary theory, and labor economics. The agents' types are shown to be independent discrete-time Markov processes that incorporate the effects of random mutation, random matching with match-induced type changes, and with the potential for enduring partnerships that may have randomly timed break-ups. The multi-period cross-sectional distribution of types is shown to be deterministic and is calculated using the exact law of large numbers.
Published Versions
Darrell Duffie & Lei Qiao & Yeneng Sun, 2018. "Dynamic directed random matching," Journal of Economic Theory, vol 174, pages 124-183. citation courtesy of 
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