Janyl Jumadinova, a candidate for the position of Assistant Professor of Computer Science will be on campus on Thursday, March 28. All are invited to attend a research presentation in Alden Hall, Room 101 at 4:00 PM.
Thursday, March 28, 2013
Alden Hall, Room 101
4:00 PM
Presenter:
Janyl Jumadinova <http://myweb.unomaha.edu/~jjumadinova/index.html>
Title:
FORETELL: Aggregating Distributed, Heterogeneous Information from Diverse Sources Using Market-based Techniques
Abstract:
Predicting the outcome of uncertain events that will happen in the future is a frequently indulged task by humans while making critical decisions. The process underlying this prediction and decision making is called information aggregation, which deals with collating the opinions of different people, over time, about the future event’s possible outcome. The information aggregation problem is non-trivial as the information related to future events is distributed spatially and temporally, the information gets changed dynamically as related events happen, and, finally, people’s opinions about events’ outcomes depends on the information they have access to and the mechanism they use to form opinions from that information. This talk will discuss how we address the problem of distributed information aggregation by building computational models and algorithms for different aspects of information aggregation so that the most likely outcome of future events can be predicted with utmost accuracy. We have employed a commonly-used market-based framework called a prediction market to formally analyze the process of information aggregation. The behavior of humans performing information aggregation within a prediction market is implemented using software agents which employ sophisticated algorithms to perform complex calculations on behalf of the humans, to aggregate information efficiently. We have considered different yet crucial problems related to information aggregation and have verified our proposed techniques through analytical results and experiments while using commercially available data from real prediction markets within a simulated, multi-agent based prediction market.
