An Experiment on Prediction Markets in Science

Prediction markets are powerful forecasting tools. They have the potential to aggregate private information, to generate and disseminate a consensus among the market participants, and to provide incentives for information acquisition. These market functionalities can be very valuable for scientific research. Here, we report an experiment that examines the compatibility of prediction markets with the current practice of scientific publication. We investigated three settings. In the first setting, different pieces of information were disclosed to the public during the experiment. In the second setting, participants received private information. In the third setting, each piece of information was private at first, but was subsequently disclosed to the public. An automated, subsidizing market maker provided additional incentives for trading and mitigated liquidity problems. We find that the third setting combines the advantages of the first and second settings. Market performance was as good as in the setting with public information, and better than in the setting with private information. In contrast to the first setting, participants could benefit from information advantages. Thus the publication of information does not detract from the functionality of prediction markets. We conclude that for integrating prediction markets into the practice of scientific research it is of advantage to use subsidizing market makers, and to keep markets aligned with current publication practice

How to Decrease the Amortization Bias: Experience vs. Rules

We conduct an experimental study that tests the effectiveness of de-biasing a certain form of exponential growth bias found in household finance debt decisions, called the amortization bias. We provide 251 bachelor students at a German university with a short tutorial based on one of three learning methods: experiential learning, learning a simple “I Owe More” debt rule-of-thumb, as well as learning an extended, but more accurate version of the “I Owe More” debt rule. Immediately after completing these tutorials, we retest for the amortization bias and find a significant bias improvement in all three treatments. More importantly, after confronting the same participants with similar debt scenarios approximately three weeks later, we find that those who had previously received a debt tutorial maintain a significantly larger bias improvement over the control group. However, during this short period, most of the individuals who learned the simple and complex rules-of-thumb could no longer apply the rule and reverted back to their biased answers, while the experiential learning group best retained their improvement in bias. We find evidence in this experiment that experience-based learning may be better suited to produce long-lasting improvements for attenuating the amortization bias