Information Aggregation in Exponential Family Markets
This work addresses prediction market design for information aggregation, but it appears incremental as it applies existing statistical frameworks to a known problem.
The paper tackles the design of automated market makers by modeling them using exponential family distributions, showing that this approach yields a natural analysis of market behavior and price equilibrium under risk-averse traders.
We consider the design of prediction market mechanisms known as automated market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a very natural analysis of the market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as when traders are budget constrained.