The Crowd at HICSS 2013 Series – #3

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

a1

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

 b

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

2 thoughts on “The Crowd at HICSS 2013 Series – #3

  1. Interesting work. When looking for information in (online) social networks, users can be subject to an information “filter bubble”. Similarly, when asking your social network (for information) to make a prediction, the result can be biased due to homophily or simply due to access to similar resources. Do you think this concept is applicable in the SEPM?

    Also, there are some typos that need to be fixed in the second to last paragraph of the post.

  2. Yes, if your information (signal) is correlated with your friends’ information, then their information is less useful.

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