Towards a Theory of Confidence in Market-Based Predictions

被引：0

作者：

Freeman, Rupert ^{[1
]}

Pennock, David M. ^{[2
]}

Reeves, Daniel M. ^{[3
]}

Rothschild, David ^{[4
]}

Waggoner, Bo ^{[5
]}

机构：

[1] Univ Virginia, Charlottesville, VA 22903 USA

[2] Rutgers State Univ, Dept Comp Sci, New Brunswick, NJ USA

[3] Beeminder, New Brunswick, NJ USA

[4] Microsoft Res New York City, New York, NY USA

[5] Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA

来源：

PROCEEDINGS OF THE TWELVETH INTERNATIONAL SYMPOSIUM ON IMPRECISE PROBABILITY: THEORIES AND APPLICATIONS | 2021年 / 147卷

关键词：

Prediction Markets; Forecasting;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Prediction markets are a way to yield probabilistic predictions about future events, theoretically incorporating all available information. In this paper, we focus on the confidence that we should place in the prediction of a market. When should we believe that the market probability meaningfully reflects underlying uncertainty, and when should we not? We discuss two notions of confidence. The first is based on the expected profit that a trader could make from correcting the market if it were wrong, and the second is based on expected market volatility in the future. Our paper is a stepping stone to future work in this area, and we conclude by discussing some key challenges.

引用

页码：365 / 368

页数：4

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