An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 510-515.
Abstract
In this paper, we employ simulation-based methods to study the role of a market maker in improving price discovery in a prediction market. In our model, traders receive a lagged signal of a ground truth, which is based on real price data from prediction markets on NBA games in the 2014-2015 season. We employ empirical game-theoretic analysis to identify equilibria under different settings of market maker liquidity and spread. We study two settings: one in which traders only enter the market once, and one in which traders have the option to reenter to trade later. We evaluate welfare and the profits accrued by traders, and we characterize the conditions under which the market maker promotes price discovery in both settings.
@inproceedings{ijcai2016p79,
title = {An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets},
author = {Wah, Elaine and Lahaie, Sébastien and Pennock, David M.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {510--515},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/79},
}
TY - CPAPER TI - An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets AU - Wah, Elaine AU - Lahaie, Sébastien AU - Pennock, David M. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 510 EP - 515 UR - https://ijcai.org/proceedings/2016/79 ER -Copied
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