My dissertation contains three studies centering on the question: how to motivate people to share high quality information on online information aggregation systems, also known as social computing systems? I take a social scientific approach to "identify" the strategic behavior of individuals in information systems, and "analyze" how non-monetary incentive schemes motivate information provision. In my first study, I use statistical modeling to infer users' information provision strategies from their actions. Information system users' strategies for contribution (e.g., I only contribute if others have contributed a certain amount) are often not directly observable, but identifying their strategies is useful in system design. With my co-authors, Jeffrey MacKie-Mason and Paul Resnick, I constructed a maximum likelihood model with simultaneous equations to estimate strategic feedback reciprocation (i.e., I only provide feedback if you give me feedback first) among the traders on eBay. We found about 23% of the traders strategically reciprocate feedback. In my second study, I focus on truthful provision of information in information markets--markets in which the participants trade bets about future events. The resulting market price reflects an aggregated prediction for the event. Theory predicts that when traders' private information is substitutable--contains similar information--they profit most by trading honestly. But when traders' private information is complementary--contains exclusively different information--traders are better off bluffing, i.e., first trading dishonestly to mislead others and later profiting from others' mistakes. Using human-subject experiments, my co-author Rahul Sami and I found traders indeed bluff more in markets with complements than in markets with substitutes. In my third study, I use game theory to analyze two non-monetary mechanisms for motivating information provision: the minimum threshold mechanism (MTM), under which one can access the public goods if she contributes more than a threshold, and the ratio mechanism (RM), under which a user consumes at most an amount proportional to her contribution level. I found whenever RM can achieve the social optimum, MTM can achieve the same. Furthermore, if RM implements a no-exclusion equilibrium, the same outcome can always be implemented by MTM. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]