Risk,trust, and altruism in genetic data sharing

How does concern about genetic data privacy compare with other concerns? We conduct behavioral experiments to compare risk attitudes towards sharing genetic data with a healthcare provider with risk attitudes towards sharing financial data with a money manager. Both scenarios involve identical decisions and monetary stakes, permitting us to focus on how the framing of data sharing influences attitudes. To delve deeper into individual motivations to share data, we provide treatments that study how data sharers' altruism and trust affect their decisions. Our findings (with 162 subjects) indicate that individuals are more willing to risk a loss to privacy of genetic data (for an anticipated return framed as health benefits) than they are to risk loss of financial data (for an anticipated return in financial benefits). We also find that 50%–60% of data recipients choose to protect another person's data, with no significant differences between frames.     

Matching soulmates

We study iterated matching of soulmates (IMS), a recursive process of forming coalitions that are mutually preferred by members to any other coalition containing individuals as yet unmatched by this process. If all players can be matched this way, preferences are IMS-complete. A mechanism is a soulmate mechanism if it allows the formation of all soulmate coalitions. Our model follows Banerjee, Konishi, and Sönmez, except reported preferences are strategic variables. We investigate the incentive and stability properties of soulmate mechanisms. In contrast to prior literature, we do not impose conditions that ensure IMS-completeness. A fundamental result is that, (1) any group of players who could change their reported preferences and mutually benefit does not contain any players who were matched as soulmates and reported their preferences truthfully. As corollaries, (2) for any IMS-complete profile, soulmate mechanisms have a truthful strong Nash equilibrium, and (3) as long as all players matched as soulmates report their preferences truthfully, there is no incentive for any to deviate. Moreover, (4) soulmate coalitions are invariant core coalitions—that is, any soulmate coalition will be a coalition in every outcome in the core. To accompany our theoretical results, we present real-world data analysis and simulations that highlight the prevalence of situations in which many, but not all, players can be matched as soulmates. In the Appendix we relate IMS to other well-known coalition formation processes.