Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation

Prediction markets are a promising instrument for drawing on the “wisdom of the crowds”. For instance, in a corporate context they have been used successfully to forecast sales or project risks by tapping into the heterogeneous information of decentralized actors in and outside of companies. Among the main market mechanisms implemented so far in prediction markets are (1) the continuous double auction and (2) the logarithmic market scoring rule. However, it is not fully understood how this choice affects crucial variables like prediction market accuracy or price variation. Our paper uses an experiment-based and micro validated simulation model to improve the understanding of the mechanism-related effects and to inform further laboratory experiments. The results underline the impact of mechanism selection. Due to the higher number of trades and the lower standard deviation of the price, the logarithmic market scoring rule seems to have a clear advantage at a first glance. This changes when the accuracy level, which is the most important criterion from a practical perspective, is used as an independent variable; the effects become less straightforward and depend on the environment and actors. Besides these contributions, this work provides an example of how experimental data can be used to validate agent strategies on the micro level using statistical methods.