用游戏化市场让专家直接共享私有科学信息，无需先验知识或复杂计算。
Collective intelligence in science: direct elicitation of diverse information from experts with unknown information structure
- 设计带聊天功能的虚拟货币预测市场，引导专家直接披露私有信息。
- 系统可达到均衡状态，使信息在无真实答案时仍高效聚合。
- 适合跨领域协作研究，可用真实资产激励专家参与创新项目。
假设需要对一个开放性科学问题进行深度集体分析：存在一个复杂的科学假说，以及一群彼此无关、掌握多样化且不可预知私有信息的专家。这些信息可能包括个人实验结果、原始推理过程或所使用的AI系统输出等。本文提出一种基于自洽结算虚拟货币预测市场的简单机制，该机制与聊天功能耦合。我们证明，此类系统可轻松达到均衡状态，使得参与者通过聊天直接分享其关于假说的私有信息，并如同市场已根据假说真实情况结算般进行交易。即使无法确定真实答案，且专家彼此无知、无法执行复杂贝叶斯计算，该方法仍能实现相关资讯的高效聚合，且结果完全可解释。最后，通过按虚拟货币余额比例奖励专家真实资产，可为任意类型的大型协作研究提供一种创新资助方式。
原文摘要 · Abstract (English)
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.
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