Information markets vs. opinion pools

Chen, Yi‐Ling; Chu, Chenhui; Mullen, Tracy; Pennock, David M.

doi:10.1145/1064009.1064016

Cited by 45 publications

(27 citation statements)

References 25 publications

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“…The PMs predict the winning probabilities in the NFL accurately. The actual winning rates match the predicted ones (Chen et al, 2005, O'connor and Zhou, 2008, Rosenbloom and Notz, 2006, Servan-Schreiber et al, 2004. This relationship is true until the game starts.…”

Section: Sportssupporting

confidence: 66%

“…The correlation is r=0.94 (r=0.96) for the virtual (real) money PM. The additional comparison with opinion pools shows no significant difference in the forecasting accuracy for the 210 analysed games (Chen et al, 2005). The operation with real or virtual money has no significant impact on the forecasting accuracy for sport PMs (Rosenbloom and Notz, 2006).…”

Section: Sportsmentioning

confidence: 88%

See 1 more Smart Citation

Prediction Markets – A New Tool for Managing Supply Chains

Hedtrich¹,

Loy²,

Müller³

2011

Supply Chain Management - New Perspectives

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Section: Sportssupporting

confidence: 66%

Section: Sportsmentioning

confidence: 88%

Prediction Markets – A New Tool for Managing Supply Chains

Hedtrich¹,

Loy²,

Müller³

2011

Supply Chain Management - New Perspectives

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“…Other empirical studies examine markets on TradeSports.com, an Irish betting exchange [WZ06,WSZ07,Tet04,Tet06]. Perhaps surprisingly, even play-money market games perform well compared to experts and real-money markets [CCMP05,PLGN01, …”

Section: Acknowledgmentsmentioning

confidence: 99%

Computational Aspects of Prediction Markets

Pennock¹,

Sami

2007

Algorithmic Game Theory

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Prediction markets (also known as information markets) are markets established to aggregate knowledge and opinions about the likelihood of future events. This chapter is intended to give an overview of the current research on computational aspects of these markets. We begin with a brief survey of prediction market research, and then give a more detailed description of models and results in three areas: the computational complexity of operating markets for combinatorial events; the design of automated market makers; and the analysis of the computational power and speed of a market as an aggregation tool. We conclude with a discussion of open problems and directions for future research.

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“…There are many papers focusing on combination of probabilistic information, ranging from a pure statistical approach (see [4,5] for a review) to more applied problems, as the combination of classifiers [6], prediction markets [7], various sources of information in a single BN [8] or different BNs into a unique model [3].…”

Section: Related Workmentioning

confidence: 99%

Average and Majority Gates: Combining Information by Means of Bayesian Networks

Campos

Fernández‐Luna

Huete

et al. 2007

Lecture Notes in Computer Science

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Abstract. In this paper we focus on the problem of belief aggregation, i.e. the task of forming a group consensus probability distribution by combining the beliefs of the individual members of the group. We propose the use of Bayesian Networks to model the interactions between the individuals of the group and introduce average and majority canonical models and their application to information aggregation. Due to efficiency restrictions imposed by the Group Recommending problem, where our research is framed, we have had to develop specific inference algorithms to compute group recommendations.

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Information markets vs. opinion pools

Cited by 45 publications

References 25 publications

Prediction Markets – A New Tool for Managing Supply Chains

Prediction Markets – A New Tool for Managing Supply Chains

Computational Aspects of Prediction Markets

Average and Majority Gates: Combining Information by Means of Bayesian Networks

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