[colloquium] PICASso Talk TODAY "Aggregating Human Expertise: An Application for Alternating Projection Algorithms"

[Steven Kleinstein]

Thought you might be interested in today's PICASso seminar on "Aggregating
Human Expertise: An Application for Alternating Projection Algorithms". An
announcement is below.

Cheers,

-Steve...


** PICASso:
** Program in Integrative Information, Computer and Application Sciences
** www.cs.princeton.edu/picasso
** Monday, October 17, 2005
**
** Interdisciplinary Computational Lunchtime Seminars
** www.cs.princeton.edu/picasso?computational_lunch.html


TITLE:    Aggregating Human Expertise:
          An Application for Alternating Projection Algorithms
SPEAKER:  Joel B. Predd, Department of Electrical Engineering,
          Princeton University
TIME:	    Seminar begins 12:30 pm (lunch available at 12:20)
LOCATION: Room 302, 3rd Floor, Computer Science Building

ABSTRACT:

In the panel aggregation problem, each expert on a panel forecasts the
chance of a set of events (about, say, the future state of the stock
market). The events in question are logically complex and dependent; since
the judges are human, the forecasts are plagued by probabilistic
incoherence. Several methods have been proposed to fuse the experts'
disparate forecasts into a coherent corpus. For problems of interest, these
methods are impractical in theory and practice; applications in risk
assessment, marketing, and business demand a new approach. What is an
efficient algorithm for aggregating the advice of hundreds of judges who
provide forecasts for thousands of events?

In this talk, we discuss ongoing research aimed at addressing this question
using alternating projection algorithms (e.g., the von Neumann-Halperin
algorithm). In particular, we show how such classical algorithms can be
applied to construct fast and scalable algorithms for aggregating forecasts
of chance. We validate the algorithms with experiments in market and
geopolitical forecasting.

In addition, we discuss how the same methods are useful in other distributed
decision making tasks. In particular, we discuss how von Neumann-Halperin
can be applied to derive energy and bandwidth efficient training algorithms
for distributed kernel regression in wireless sensor networks.

Joint work with Professors Sanjeev Kulkarni (EE), Daniel Osherson (Psych),
and Vincent Poor (EE).

****************

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PLEASE FORWARD THIS MESSAGE TO OTHER COMPUTATIONALLY-ORIENTED RESEARCHERS
WHO MAY BE INTERESTED IN THESE EVENTS, OR FUTURE PROGRAMS.

THANKS!