Information aggregation in exponential family markets
EC(2014)
摘要
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 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 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 budget-constrained traders.
更多查看译文
关键词
exponential family,risk aversion,logarithmic score,economics,budget constraints,maximum entropy,learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络