[Editors] MIT untangles honeybee dances, stock market swings

[Teresa Herbert]

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Deciphering honeybee dances and stock market swings
--MIT grad student’s model brings order to complex systems through math
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For Immediate Release
TUESDAY, DEC. 9, 2008

Contact: Teresa Herbert, MIT News Office
E: therbert at mit.edu, T: 617-258-5403


CAMBRIDGE, Mass. -- What do dancing honeybees and stock markets have  
in common?

At first glance, not much. But both are complicated dynamic systems  
that are extremely difficult to model — until now. An MIT graduate  
student has developed a methodology for automatically constructing  
computer models that can accurately describe the behavior of such  
complex systems with very little background information.

The work has numerous potential applications, from enabling oil  
companies to get a clearer picture of where oil might be located  
underground to allowing port operators to spot suspicious behaviors.

Graduate student Emily Fox, of MIT’s Laboratory for Information and  
Decision Systems, will present her new model at the Neural Information  
Processing Systems conference on Dec. 10.

The methodology is designed to build models for complicated systems  
whose behavior is characterized by abrupt changes. These complex  
dynamic systems include stock markets and dancing bees: Honeybees  
switch between several dances in seemingly random fashion, and stock  
markets are notoriously unpredictable.

While modeling of dynamic systems is a subject that has received  
considerable attention from researchers in many disciplines, most  
require constraining assumptions such as a single, consistent mode of  
dynamic behavior, and possibly prior information regarding the  
structure of the underlying dynamics.

“It’s quite exciting that even when you remove the shackles of putting  
in prior information, there’s a lot you can discover about a complex  
system,” said Fox’s advisor, Professor of Electrical Engineering Alan  
Willsky.

The new methodology sifts through sets of data, looks for patterns and  
comes up with equations that describe these patterns.

In the case of the honeybee, Fox told the model the position of the  
bee and its head angle for 30 seconds, taking data in each of 30  
frames per second. From that information, the model came up with the  
number of different dances, the bee’s dancing state at each time  
point, the probability that the bee will switch to a different dance  
at each point, and equations that describe each dance.

The methodology, which aims to come up with the simplest model that  
explains the data, accurately concluded that the honeybees have three  
dances. Biologists have long known that honeybees use the dances to  
communicate distance and direction of potential food sources or nest  
sites.

The methodology provides a tool that can potentially save time and  
effort for scientists who study the dancing bees, who now have to  
painstakingly review long videos and visually categorize the dances.

“You don’t want to go through and check frame by frame,” said Fox.  
“This is a way of automating that, and labeling the data for them.”

Fox also tested the model on data from the Brazilian stock market,  
using the same algorithm she used for the dancing bees. Given  
information on the Brazilian market’s daily returns over a four-year  
period, the model inferred the number of modes of market volatility  
and the probability that the market would shift to a different state  
of volatility.

Currently the researchers are focusing on the model’s descriptive  
abilities, and the accuracy with which it discovers and extracts  
models. While the work is still in relatively early stages, it offers  
promise in several areas. The first is simply discovering behavior,  
helping scientists and other users with their studies or monitoring  
responsibilities.

While the primary objective of this research is discovering models  
that can explain complex behavior (and thus inform domain experts),  
the extracted models could also be used as the basis for real-time  
estimation, tracking and prediction.

In addition, in the context of oil exploration this methodology could  
be used to discover models that automatically describe “depositional  
environments,” i.e., processes of laying down geological layers in the  
earth, such as those deposited in a river bed or by a sand dune moving  
across a desert.

Erik Sudderth and Michael I. Jordan of the University of California at  
Berkeley are also authors of the paper Fox will present at the NIPS  
conference.

The research was funded by the Army Research Office and the Air Force  
Office of Scientific Research.

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Written by Anne Trafton, MIT News Office
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