- Award Details
SBIR Phase I: OpIndex: A Dynamic Index of Consumer Opinions
National Science Foundation
Agency Tracking Number:
Solicitation Topic Code:
Small Business Information
720 Greenwich st, Apt B32, New York, NY, 10002-4140
Socially and Economically Disadvantaged:
AbstractThis Small Business Innovation Research Phase I project concentrates on the opportunities that exist in organizing consumers' opinions, unstructured information that makes up a significant portion of Internet content. Organizing this information will lead to efficiency gains in current markets and will enable emerging ones. This proposal integrates prediction markets and casual games to generate a structured, non ad-hoc and dynamic index of consumers' true opinions on a large scale. Prediction markets are explicitly designed mechanisms where consumers are given incentives to reveal their opinions truthfully through trading games. However, historically prediction markets have not scaled. Casual games are informal problem-solving mechanisms that have been shown to scale massively through Internet and mobile devices. However, casual games are ad hoc and do not induce a coordinated purposeful dataset. The intellectual merit of the proposed research lies in integrating these two mechanisms and applying the result to the commercial enterprise. The broader impacts of this research are the ability to (1) provision the collective's opinions on a larger scale by lowering barriers for mass participation in a complex mechanism, that in turn (2) decrease uncertainty and increase confidence in the quality of the information, (3) create greater efficiency in current decision-making processes, (4) enable new markets to emerge given the reduced information asymmetries, and (5) have spill-over benefits to many industrial sectors. The integrative approach is a novel contribution to software design methodologies for emerging social computing platforms, where the architecture is increasingly based on participation and less on monolithic designs. The proposed research will contribute to development of the internal logic of the prediction market itself, through better reward structure and lower transaction costs. If successfully deployed, the approach will enable lower-cost engagement for online research geared toward assessing consumer engagement and trends.
* information listed above is at the time of submission.