Participation, Feedback & Incentives in a Competitive Forecasting Community

Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest. An arising question is how to design incentive schemes and feedback mechanisms to motivate participants to contribute to such an information exchange. We design a prediction market for economic derivatives that aggregates macro-economic information. We show that the level of participation is mainly driven by a weekly newsletter which acts as a reminder. In public goods projects participation feedback has been found to increase participants\u27 contributions. We find that the induced competitiveness of market environments seem to superpose classical feedback mechanisms. We show that forecast errors fall over the prediction horizon. The market generated forecasts compare well to the Bloomberg-survey forecasts, the industry standard. Additionally we can predict community forecast error by using an implicit market measure

Crowd Labor Markets as Platform for IS Research: First Evidence from Electronic Markets

Crowd labor markets such as Amazon Mechanical Turk (MTurk) have emerged as popular platforms where researchers can inexpensively run web-based experiments. Recent work even suggests that MTurk can be used to run large-scale field experiments such as prediction markets in which participants interact synchronously in real-time. Besides technical issues, several methodological questions arise and lead to the question of how results from MTurk and laboratory experiments compare. In this work we provide first insights into running market experiments on MTurk and compare the key property of markets, information efficiency, to a laboratory setting. The results are mixed at best. On MTurk, information aggregation took place less frequently than in the lab. Our results suggest that MTurk participants cannot handle as much complexity as laboratory participants in time-pressured, synchronized experiments

Identifying Experts in Virtual Forecasting Communities

Macroeconomic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest in the scientific world and in industry. An arising question is how to detect valuable user input and identify experts in such online communities. Detecting such input would possibly enable us to improve the information aggregation mechanism and the forecast performance of such systems. We design a prediction market for economic derivatives that aggregates macro-economic information. Using market-based measures we find that user input can be evaluated ad-hoc. Further analysis shows that aggregated measures outperform established methods -such as reputation- in identifying forecasting experts. Moreover, using data from a two year field-experiment we find that expertise is stable for longer time horizons

Same same but different - Towards a taxonomy for digital involvement projects

Governments and public institutions increasingly embrace digital opportunities to involve citizens in public issues and decision making. While public participation is generally seen as an important and promising venture, the design of the participation processes and the utilized digital infrastructure poses challenges, especially to the public sector. Instead of limiting conceptual guidance and exchange to one domain, we therefore develop a taxonomy for digital involvement projects that unites the domains of e-participation, citizen science and crowd-X. Embedded in a design science research approach, we follow an iterative design process to elaborate the key characteristics of a digital involvement project based on the participation process, its individuals and digital infrastructure. Through evaluating the artifact in a focus group with domain practitioners, we find support for the usefulness of our taxonomy and its ability to provide guidance and a basis for discussion of digital involvement projects across domains

Continuous Market Engineering - Focusing Agent Behavior, Interfaces, and Auxiliary Services

Electronic markets spread out amongst business entities as well as private individuals. Albeit numerous approaches on developing electronic markets exist, a unified approach targeting market development, redesign, and refinement has been lacking. This thesis studies the potential of continuously improving electronic markets. Thereby, the experiments? design focuses on Agent Behavior, Interfaces, and Auxiliary Services and thus unveils the potential of continuously improving electronic markets