Please mark your calendars and plan to attend the C4I Seminar Series on 
Friday, Jan 24, from 1:30 pm to 2:30 pm in the Engineering Building, 
room 4705. Dr. Wei Sun discusses "/*Efficient User Assets Management by 
Trade-based Asset Blocks and Dynamic Junction Tree for Combo Prediction 
Markets*/." For further information contact Deb Schenaker, (703) 
993-3682, email: [log in to unmask], or visit our website _c4i.gmu.edu_.


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 
an 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 


Dr. Wei Sun is a research assistant professor of the Sensor Fusion Lab 
and the C4I Center at George Mason University, where he works 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 is the recipient of the 
GMU's Academic Excellence Award in 2003 and PhD Fellowship during 2003-2007.

Debra J. Schenaker
C4I Center Admin, MSN 4B5
Volgenau School of Engineering
4400 University Drive
Fairfax, VA  22030
(703) 993-3682 (p)
(703) 993-1706 (f)
[log in to unmask]