C4I Center Seminar Series:
Efficient User Assets Management by
Trade-based Asset Blocks and Dynamic Junction Tree
for Combo Prediction Markets

Dr. Wei Sun of the C4I Center presents
“Efficient User Assets Management by Trade-based Asset Blocks
and Dynamic Junction Tree for Combo Prediction Markets”

MIST/C Recording


We have often heard that the collective wisdom of an informed and diverse group usually out-performs individual experts on forecasting and estimation tasks. The key question is how best to aggregate those diverse judgments. In 2010, it wasn’t known whether any system could reliably beat the simple average. Mason is among the teams that reliably did so for two years in IARPA’s ACE forecasting challenge. In June 2013 we closed down our geopolitical prediction market to create SciCast, a new and improved science & technology market. This talk discusses ongoing improvements to the SciCast forecasting engine.

Unlike other prediction markets, SciCast allows forecasters make conditional forecasts: the chance that China’s lunar rover would deploy can be made to depend on a successful soft lunar landing. To avoid a combinatorial explosion, SciCast uses Bayesian networks as the underlying probability model. But tracking the joint probability structure is not enough: markets also must track assets for each user, awarding users for correct forecasts and ensuring there is no possible world where they go negative. Previously, we tracked assets using the same junction tree structure as the joint probability model. This approach provides fast computation of the minimum value and expected value. However, it wastes a lot of space: the majority of users trade sparsely relative to the total number of questions, and even more sparsely compared to the whole joint probability space. Therefore most of the asset junction tree remains untouched. Worse, every time a question is added or resolved, we have to update the asset tree for all users, just in case.

We think a trade-based method can overcome this problem and be computationally efficient as well. It turned out that we can build asset blocks involving the questions being traded only, then collect them in a organized manner such as merging sub-block to its super set. Further, when computing user score and cash, we can construct an asset junction tree dynamically, based on the collection of asset blocks then use the asset junction tree for efficient computations. When questions are resolved, it is straightforward to update user’s asset blocks accordingly. Basically, for any asset block which contains the resolved questions, we realize the resolving state and truncate the block.

In this presentation, I will explain in detail how the trade-based asset blocks are built and how to construct the corresponding asset junction tree dynamically. Computational examples will be demonstrated and compared with other alternative methods. For general questions about prediction markets or other background knowledge, please visit https://scicast.org/.


Dr. Wei Sun is a research assistant professor of the Sensor Fusion Lab and the C4I Center at George Mason University, where he Wei Sunworks on stochastic modeling, probabilistic reasoning, optimization, decision support systems, data fusion and general operations research. Dr. Sun focuses his research on inference algorithm for hybrid Bayesian networks, nonlinear filtering, and information fusion. He is an expert in Bayesian inference and developer of several efficient inference algorithms. He is also a contributor/committer of the open-source Matlab BN toolbox. Applications of his research include tracking, fusion, bioinformatics, classification, diagnosis, etc.

Prior to joining GMU, Dr. Wei Sun was a Senior Analyst with United Airlines, Inc. and a professional Electrical Engineer in China. He was the recipient of the GMU’s Academic Excellence Award in 2003 and PhD Fellowship during 2003-2007.

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1:30 pm - 3:00 pm

Engineering Bldg Room 4705