From prediction markets to interpretable collective intelligence
This addresses the challenge of harnessing collective intelligence for decision-making in fields like science and medicine, though it appears incremental as it builds on existing prediction market concepts.
The paper tackles the problem of eliciting accurate probability estimates and interpretable collective information from a group of experts, proposing a self-resolving prediction market with play money that incentivizes direct information exchange to efficiently solve scientific or medical problems.
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 simultaneously many experts to collectively solve scientific or medical problems in a very efficient manner. We also note that in our considerations, experts are not assumed to be Bayesian.