We outline how to create a mechanism that provides an optimal way to elicit,
from an arbitrary group of experts, the probability of the truth of an
arbitrary logical proposition together with collective information that has an
explicit form and interprets this probability. Namely, we provide strong
arguments for the possibility of the development of a self-resolving prediction
market with play money that incentivizes direct information exchange between
experts. Such a system could, in particular, motivate experts from all over the
world to collectively solve scientific or medical problems in a very efficient
manner. In our main considerations about real experts, they are not assumed to
be Bayesian and their behavior is described by utilities that satisfy the von
Neumann--Morgenstern axioms only locally.

提出一种使用预测市场机制，通过激励专家之间直接信息交流的方式来解决科学或医学领域问题的方法。

非理性人类智能的可解释集体智能

Interpretable collective intelligence of non-rational human agents

Explainably estimating confidence in published scholarly work offers
opportunity for faster and more robust scientific progress. We develop a
synthetic prediction market to assess the credibility of published claims in
the social and behavioral sciences literature. We demonstrate our system and
detail our findings using a collection of known replication projects. We
suggest that this work lays the foundation for a research agenda that
creatively uses AI for peer review.

本研究开发了一种合成预测市场来评估社会与行为科学文献中已发表声明的可信度，并使用一系列已知的复制项目来展示该系统，并建议此项工作为使用人工智能进行同行评审奠定了基础。