Itai Ashlagi
ABSTRACT
We study dynamic matching in an infinite-horizon stochastic networked market, in which some agents are a priori more difficult to match than others. Agents have compatibility-based preferences and can match either bilaterally, or indirectly through chains. We study the effect matching technologies and matching policies have on efficiency in markets with different compositions of hard and easy-to-match agents. First, we analyze myopic matching policies and identify a strong connection between market thickness and the efficiency driven by the matching technology. We show that when "hard-to-match" agents join the market more frequently than "easy-to-match" ones, moving from bilateral matchings to chains significantly increases efficiency. Otherwise, the difference between matching bilaterally or through a chain is negligible. Second, we show that the lack of thickness cannot be compensated by non-myopic matching policies implying that the only way to thicken the market fruitfully is by attracting more agents.
Index Terms
- On Matching and Thickness in Heterogeneous Dynamic Markets
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