Bluffing and strategic reticence in prediction markets

Bluffing and strategic reticence in prediction markets (2007)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Yiling Chen , Daniel M. Reeves , David M. Pennock , Robin D. Hanson , Lance Fortnow , Rica Gonen

Venue:	In the third Workshop on Internet and Network Economics
Citations:	8 - 6 self

Versions

Version History

Metadata Version 0

Datum	Value	Source
TITLE	Bluffing and Strategic Reticence in Prediction Markets ⋆	SVM HeaderParse 0.2
AUTHOR NAME	Yiling Chen	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
AUTHOR NAME	Daniel M. Reeves	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
AUTHOR NAME	David M. Pennock	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
AUTHOR NAME	Robin D. Hanson	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
AUTHOR NAME	Lance Fortnow	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
AUTHOR NAME	Rica Gonen	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Yahoo! Research; 2; George Mason University; 3; University of Chicago	SVM HeaderParse 0.2
ABSTRACT	Abstract. We study the equilibrium behavior of informed traders interacting with two types of automated market makers: market scoring rules (MSR) and dynamic parimutuel markets (DPM). Although both MSR and DPM subsidize trade to encourage information aggregation, and MSR is myopically incentive compatible, neither mechanism is incentive compatible in general. That is, there exist circumstances when traders can benefit by either hiding information (reticence) or lying about information (bluffing). We examine what information structures lead to straightforward play by traders, meaning that traders reveal all of their information truthfully as soon as they are able. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a finite-period dynamic game. We employ two different information structures for the logarithmic market scoring rule (LMSR): conditionally independent signals and conditionally dependent signals. When signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE) for LMSR. However, when signals are conditionally dependent, there exist joint probability distributions on signals such that at a PBE in LMSR traders have an incentive to bet against their own information—strategically misleading other traders in order to later profit by correcting their errors. In DPM, we show that when traders anticipate sufficiently better-informed traders entering the market in the future, they have incentive to partially withhold their information by moving the market probability only partway toward their beliefs, or in some cases not participating in the market at all. 1	SVM HeaderParse 0.2
CITATIONS	1 found	ParsCit 1.0