🤖 AI Summary
This study addresses the challenge of efficiently aggregating dispersed private information from experts regarding complex scientific hypotheses under conditions of no shared context, unknown information structures, and unverifiable ground truth. The authors propose an incentive mechanism that integrates a self-settling virtual-currency prediction market with a real-time chat system, enabling anonymous experts to directly reveal diverse private signals and trade based on hypothesis veracity—without requiring Bayesian inference or predefined information structures. Through incentive-compatible design and an information aggregation algorithm, the mechanism achieves interpretable and efficient information synthesis at equilibrium, offering a novel paradigm for funding and decision support in large-scale collaborative scientific research.
📝 Abstract
Suppose we need a deep collective analysis of an open scientific problem: there is a complex scientific hypothesis and a large online group of mutually unrelated experts with relevant private information of a diverse and unpredictable nature. This information may be results of experts'individual experiments, original reasoning of some of them, results of AI systems they use, etc. We propose a simple mechanism based on a self-resolving play-money prediction market entangled with a chat. We show that such a system can easily be brought to an equilibrium where participants directly share their private information on the hypothesis through the chat and trade as if the market were resolved in accordance with the truth of the hypothesis. This approach will lead to efficient aggregation of relevant information in a completely interpretable form even if the ground truth cannot be established and experts initially know nothing about each other and cannot perform complex Bayesian calculations. Finally, by rewarding the experts with some real assets proportionally to the play money they end up with, we can get an innovative way to fund large-scale collaborative studies of any type.