Conference Paper

Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution

DOI: 10.1145/1807342.1807374 Conference: Proceedings 11th ACM Conference on Electronic Commerce (EC-2010), Cambridge, Massachusetts, USA, June 7-11, 2010
Source: DBLP

ABSTRACT

We conduct laboratory experiments on variants of market scoring rule prediction markets, under different information distribution patterns, in order to evaluate the efficiency and speed of information aggregation, as well as test recent theoretical results on manipulative behavior by traders. We find that markets structured to have a fixed sequence of trades exhibit greater accuracy of information aggregation than the typical form that has unstructured trade. In comparing two commonly used mechanisms, we find no significant difference between the performance of the direct probability-report form and the indirect security-trading form of the market scoring rule. In the case of the markets with a structured order, we find evidence supporting the theoretical prediction that information aggregation is slower when information is complementary. In structured markets, the theoretical prediction that there will be more delayed trading in complementary markets is supported, but we find no support for the prediction that there will be more bluffing in complementary markets. However, the theoretical predictions are not borne out in the unstructured markets.

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    • "Considering that liquidity might represent a real threat for prediction markets [64], our approach to create a forecast mitigates the risk of illiquidity. Though, automated market makers (AMM) such as Hanson's market scoring rule or the dynamic parimutuel market maker have proven to mitigate the problem of illiquidity [65] [66], the complexity inherent in these AMMs and the resulting difficulties of explaining the market mechanisms discouraged us to employ automated market makers. "
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    • "Thus, market makers add infinite liquidity to PMs. Hanson's (2003) Logarithmic Market Scoring Rules (LMSR) maker is currently the most applied market maker (Jian/Sami, 2012, Slamka et al., 2012). As the LMSR market maker, most market makers apply some kind of mechanism for adjusting its pricing algorithm and its effective liquidity or price elasticity, that can be defined as the degree prices for a given contract change due to a single transaction. "
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    • "Following [14] "
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    ABSTRACT: Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the events' outcome. Previous research on prediction markets has mostly analyzed the prediction markets by building complex analytical models. In this paper, we posit that simpler yet powerful Boolean rules can be used to adequately describe the operations of a prediction market. We have used a multi-agent based prediction market where Boolean network based rules are used to capture the evolution of the beliefs of the market's participants, as well as to aggregate the prices in the market. We show that despite the simplification of the traders' beliefs in the prediction market into Boolean states, the aggregated market price calculated using our BN model is strongly correlated with the price calculated by a commonly used aggregation strategy in existing prediction markets called the Logarithmic Market Scoring Rule (LMSR). We also empirically show that our Boolean network-based prediction market can stabilize market prices under the presence of untruthful belief revelation by the traders.
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