Market-Based Collective Intelligence in Enterprise 2.0 Decision Making

Abstract

In the context of Enterprise 2.0 collective intelligence approaches, we explore the usage of prediction markets as organizational decision support tool. Such markets typically involve market participants (i.e. experts) with a vested interest in the decision. Simply carrying over knowledge on prediction markets to the design of such decision markets might be premature. To back this hypothesis, we build on theory and evidence on prediction markets, decision markets, and market manipulation, and test the key theoretical predictions in a lab experiment.

Our data suggest that indeed, people who have a stake in the decision manipulate the market and partially corrupt information aggregation. More precisely, we show how different designs of the principal’s decision rule affect manipulation, information aggregation, and decision quality. In a nutshell, we find that the common practice is better than random but worse than an alternative design.

Keywords: prediction markets, electronic markets, information aggregation, manipulation, experiments