Mutually Reinforcing Systems

People can be used by computers to solve problems. In most cases this 'human computation' is used to gather information that computers struggle to create. These problems can be phrased as games to provide an incentive for people to do the work. In the past, these games have captured a broad level of information in the hope that specific needs will be covered. But what happens when we need specific information that the games have not been designed to create? Mutually reinforcing systems are a new approach to human computation that tries to attain this focus by allowing multiple systems to work together so that each one can benefit from the other's strengths. This dissertation shows that extending human computation techniques to allow the collection and classification of useful contextual information in mobile environments is possible and can be extended to allow the by-products to match the specific needs of another system.