When several two-sided matching markets merge into one, it is inevitable that some agents will become worse off if the matching mechanism used is stable. I formalize this observation by defining the property of integration monotonicity, which requires that every agent becomes better off after any number of matching markets merge. Integration monotonicity is also incompatible with the weaker efficiency property of Pareto optimality.Nevertheless, I obtain two possibility results. First, stable matching mechanisms never hurt more than one-half of the society after the integration of several matching markets occurs. Second, in random matching markets there are positive expected gains from integration for both sides of the market, which I quantify.

The Social Cloud framework leverages existing relationships between members of a social network for the exchange of resources. This thesis focuses on the design of coordination mechanisms to address two challenges in this scenario. In the first part, user participation incentives are studied. In the second part, heuristics for two-sided matching-based resource allocation are designed and evaluated.

This book constitutes the refereed proceedings of the 13th International Symposium on Algorithmic Game Theory, SAGT 2020, held in Augsburg, Germany, in September 2020.* The 21 full papers presented together with 3 abstract papers were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections named: auctions and mechanism design, congestion games and flows over time, markets and matchings, scheduling and games on graphs, and social choice and cooperative games. * The conference was held virtually due to the COVID-19 pandemic.