CONDITIONAL PREDICTION MARKETS AS CORPORATE DECISION SUPPORT SYSTEMS – AN EXPERIMENTAL COMPARISON WITH GROUP DELIBERATIONS

Abstract

Predicting the future is an integral part of effective corporate decision making. Most firms face the critical challenge of aggregating information dispersed among its agents. These agents and thus the aggregation process are prone to judgmental biases. The primary research question we address is whether markets correct these biases better than group deliberations. Using an experimental setting, we find that information markets provide more accurate and less volatile forecasts than group deliberations. We also describe different sources of the behavioral biases we observe. For example, while a deliberating group can be led astray by an influential group member, traders tend to overweight personal preferences. Our results indicate that conditional prediction markets provide a more effective medium for aggregating information than group deliberations.