Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
复制标题
优化预测市场中的贝叶斯信息揭示策略：Alice Bob Alice 案例
DOI：
10.4230/lipics.itcs.2018.14
复制
发表时间：
2018
影响因子：
1.5
通讯作者：
G. Schoenebeck
中科院分区：
文献类型：
--
作者：
Yuqing Kong;G. Schoenebeck
预测市场提供了一种独特而引人注目的方式来销售和汇总信息，然而，对于参与此类市场的代理的最佳策略的良好理解仍然是难以捉摸的。为了模拟这种复杂的环境，之前的工作提出了一个三阶段博弈，称为爱丽丝鲍勃爱丽丝（a - b - a）博弈——爱丽丝首先参与市场，然后鲍勃加入，然后爱丽丝有机会再次参与。虽然之前的工作在对某些有趣的边缘情况的最佳策略进行分类方面取得了进展，但对于一般信息结构，计算Alice在a - b - a博弈中的最佳策略仍然是一个悬而未决的问题。
Prediction markets provide a unique and compelling way to sell and aggregate information, yet a good understanding of optimal strategies for agents participating in such markets remains elusive. To model this complex setting, prior work proposes a three stages game called the Alice Bob Alice (A-B-A) game - Alice participates in the market first, then Bob joins, and then Alice has a chance to participate again. While prior work has made progress in classifying the optimal strategy for certain interesting edge cases, it remained an open question to calculate Alice's best strategy in the A-B-A game for a general information structure.
In this paper, we analyze the A-B-A game for a general information structure and (1) show a "revelation-principle" style result: it is enough for Alice to use her private signal space as her announced signal space, that is, Alice cannot gain more by revealing her information more "finely"; (2) provide a FPTAS to compute the optimal information revelation strategy with additive error when Alice's information is a signal from a constant-sized set; (3) show that sometimes it is better for Alice to reveal partial information in the first stage even if Alice's information is a single binary bit.