Information Exchange in Prediction Markets: How Social Networks Promote Forecast Efficiency
in Proceedings of the Hawai’i International Conference on System Science 2013
Liangfei Qiu – Department of Economics – University of Texas at Austin
Huaxia Rui – Simon School of Business – University of Rochester
Andrew B. Whinston – Department of Information, Risk and Operations Management – University of Texas at Austin
This paper studies the effects of information transmission on wisdom of the crowd. We provide a game-theoretic framework to resolve the question: Do social networks promote the forecast efficiency in prediction markets?
Our study shows that a social network is not a panacea in terms of improving forecast accuracy. The use of social networks could be detrimental to the forecast performance when the cost of information acquisition is high. We also study the effects of social networks on information acquisition in prediction markets. In the symmetric Bayes-Nash equilibrium, all participants use a threshold strategy, and the equilibrium information acquisition is decreasing in the number of participant’s friends and increasing in the network density. The aforementioned results are robust to two commonly used mechanisms of prediction markets: a forecast-report mechanism and a security-trading mechanism.
In the paper, we compare the performance of non-networked prediction markets (NNPM) with the performance of social-network-embedded prediction markets (SEPM). In the simulation, we use two measures of prediction market performance: the forecast accuracy and the mean squared errors (MSE) of the prediction market.
Figure #1 A & B – A Comparison between the Performances of the SEPM and the NNPM
Figure #1(a) – Forecast Accuracy
Figure 1(a) shows that when the cost of information acquisition is low, the SEPM outperforms the NNPM in terms of forecast accuracy, and when the cost is high, the NNPM outperforms the SEPM.
Figure #1(b) – MSE
In Figure 1(b), this result is robust to a different measure of prediction market performance: MSE. represents the MSE computed in the NNPM, and represents the MSE in the SEPM. When is small, , which means that the SEPM outperforms the NNPM. As increases, decreases, and when is large enough, the NNPM performs better than the SEPM.
There are two implications of this result. First, when the cost of information acquisition is low, a social network can enhance forecast accuracy in prediction markets. Second, a social network also has a negative effect on the forecast accuracy of a prediction market when the cost of information acquisition is high.
The paper at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2047904