research-article

What you jointly know determines how you act: strategic interactions in prediction markets

Authors:

Yiling ChenAuthors Info & Claims

EC '13: Proceedings of the fourteenth ACM conference on Electronic commerce

Pages 489 - 506

https://doi.org/10.1145/2492002.2482592

Published: 16 June 2013 Publication History

Abstract

The primary goal of a prediction market is to elicit and aggregate information about some future event of interest. How well this goal is achieved depends on the behavior of self-interested market participants, which are crucially influenced by not only their private information but also their knowledge of others' private information, in other words, the information structure of market participants. In this paper, we model a prediction market using the now-classic logarithmic market scoring rule (LMSR) market maker as an extensive-form Bayesian game and aim to understand and characterize the game-theoretic equilibria of the market for different information structures. Prior work has shown that when participants' information is independent conditioned on the realized outcome of the event, the only type of equilibria in this setting has every participant race to honestly reveal their private information as soon as possible, which is the most desirable outcome for the market's goal of information aggregation. This paper considers the remaining two classes of information structures: participants' information being unconditionally independent (the I game) and participants' information being both conditionally and unconditionally dependent (the D game). We characterize the unique family of equilibria for the I game with finite number of participants and finite stages. At any equilibrium in this family, if player i's last stage of participation in the market is after player j's, player i only reveals his information after player j's last stage of participation. This suggests that players race to delay revealing their information, which is probably the least desirable outcome for the market's goal. We consider a special case of the D game and cast insights on possible equilibria if one exists.

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Cited By

Shan LZhang SZhang JWang ZChua TNgo CKumar RLauw HKa-Wei Lee R(2024)On Truthful Item-Acquiring Mechanisms for Reward MaximizationProceedings of the ACM on Web Conference 202410.1145/3589334.3645345(25-35)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645345
Ban A(2018)Strategy-Proof Incentives for PredictionsWeb and Internet Economics10.1007/978-3-030-04612-5_4(51-65)Online publication date: 21-Nov-2018
https://doi.org/10.1007/978-3-030-04612-5_4
Chen YWaggoner B(2017)Intro to informational substitutesACM SIGecom Exchanges10.1145/3144722.314472716:1(53-57)Online publication date: 25-Sep-2017
https://dl.acm.org/doi/10.1145/3144722.3144727
Show More Cited By

Index Terms

What you jointly know determines how you act: strategic interactions in prediction markets
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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What you jointly know determines how you act: strategic interactions in prediction markets
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cover image ACM Conferences

EC '13: Proceedings of the fourteenth ACM conference on Electronic commerce

June 2013

924 pages

ISBN:9781450319621

DOI:10.1145/2492002

General Chair:
Michael Kearns
University of Pennsylvania., USA
,
Program Chairs:
Preston McAfee
Google, USA
,
Éva Tardos
Cornell University, USA

Copyright © 2013 ACM.

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Published: 16 June 2013

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EC '13: ACM Conference on Electronic Commerce

June 16 - 20, 2013

Pennsylvania, Philadelphia, USA

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EC '13 Paper Acceptance Rate 72 of 223 submissions, 32%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Shan LZhang SZhang JWang ZChua TNgo CKumar RLauw HKa-Wei Lee R(2024)On Truthful Item-Acquiring Mechanisms for Reward MaximizationProceedings of the ACM on Web Conference 202410.1145/3589334.3645345(25-35)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645345
Ban A(2018)Strategy-Proof Incentives for PredictionsWeb and Internet Economics10.1007/978-3-030-04612-5_4(51-65)Online publication date: 21-Nov-2018
https://doi.org/10.1007/978-3-030-04612-5_4
Chen YWaggoner B(2017)Intro to informational substitutesACM SIGecom Exchanges10.1145/3144722.314472716:1(53-57)Online publication date: 25-Sep-2017
https://dl.acm.org/doi/10.1145/3144722.3144727
Wah ELahaie SPennock D(2016)An empirical game-theoretic analysis of price discovery in prediction marketsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060693(510-516)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060693
Chakraborty MDas S(2016)Trading on a rigged gameProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060644(158-164)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060644
Carvalho A(2016)An Overview of Applications of Proper Scoring RulesDecision Analysis10.1287/deca.2016.033713:4(223-242)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1287/deca.2016.0337
Chen YWaggoner B(2016)Informational Substitutes2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS.2016.33(239-247)Online publication date: Oct-2016
https://doi.org/10.1109/FOCS.2016.33
Chen YGao XGoldstein RKash I(2015)Market manipulation with outside incentivesAutonomous Agents and Multi-Agent Systems10.1007/s10458-014-9249-129:2(230-265)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1007/s10458-014-9249-1
Huang EShoham YBazzan AHuhns MLomuscio AScerri P(2014)Price manipulation in prediction marketsProceedings of the 2014 international conference on Autonomous agents and multi-agent systems10.5555/2615731.2615768(213-220)Online publication date: 5-May-2014
https://dl.acm.org/doi/10.5555/2615731.2615768
Chen YDevanur NPennock DVaughan JBabaioff MConitzer VEasley D(2014)Removing arbitrage from wagering mechanismsProceedings of the fifteenth ACM conference on Economics and computation10.1145/2600057.2602876(377-394)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2600057.2602876

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