Bayesian epistemics

An interesting inverse design question: how should I design a system to optimise for truthfulness? Brief summary here.

@Frongillo2024Recent:

This note provides a survey for the Economics and Computation community of some recent trends in the field of information elicitation. At its core, the field concerns the design of incentives for strategic agents to provide accurate and truthful information. Such incentives are formalized as proper scoring rules, and turn out to be the same object as loss functions in machine-learning settings, providing many connections. More broadly, the field concerns the design of mechanisms to obtain information from groups of agents and aggregate it or use it for decision making. Recently, work on information elicitation has expanded and been connected to online no-regret learning, mechanism design, fair division, and more.

One of many topics on which I am not an expert but find it useful to track. This one is more useful than many to track because the scientific literature on this area is opaque and the popular stuff is of highly variable quality, e.g. the rationalists like to talk off-handedly about prediction markets but every time I try to chase that concept down I find myself deep in a lesswrong.org rabbit hole and become discouraged.

I am currently reading, and strongly recommend, Chapter 2 of @Neyman2024Algorithmic which is a well-written introduction to many of the concepts in this area. In particular, it explains beautifully the connection between Bregman divergences and proper scoring rules, and incentive mechanisms for truthfulness. Potential applications to epistemic communities, prediction markets etc are immediate.