Efficient Matchings on 7 Cups

Eric Gao, Stanford University

We analyze two-sided asymmetric matching markets on 7 Cups, a site for social-emotional support where users in need of help can request to be matched with volunteer listeners who have the sole power to accept requests. The aim of this paper is to analyze user incentives to characterize what their dominant strategies are when deciding what to reveal when requesting a conversation. Listeners are treated as myopic in our model, with their only actions being to accept matches that work and terminate conversations that become undesirable for them. We find truth-telling to be a dominant strategy up to sufficiently small misrepresentations. Finally, we propose implementable suggestions to improve match outcomes.

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