Algorithm searches for models that best explain experimental data

Aug 02, 2011
An evolutionary computation approach developed by Franklin University’s Esmail Bonakdarian, Ph.D., was used to analyze data from two classical economics experiments. As can be seen in this figure, optimization of the search over subsets of the maximum model proceeds initially at a quick rate and then slowly continues to improve over time until it converges. The top curve (red) shows the optimum value found so far, while the lower, jagged line (green) shows the current average fitness value for the population in each generation. Credit: Ohio Supercomputer Center

A Franklin University professor recently developed an evolutionary computation approach that offers researchers the flexibility to search for models that can best explain experimental data derived from many types of applications, including economics.

To test the underlying that approach, Esmail Bonakdarian, Ph.D., an assistant professor of Computing Sciences and Mathematics at Franklin, leveraged the Glenn IBM 1350 Opteron cluster, the flagship system of the Ohio Supercomputer Center (OSC).

“Every day researchers are confronted by large sets of survey or experimental data and faced with the challenge of ‘making sense’ of this collection and turning it into useful knowledge,” Bonakdarian said. “This data usually consists of a series of observations over a number of dimensions, and the objective is to establish a relationship between the variable of interest and other variables, for purposes of prediction or exploration.”

Bonakdarian employed his evolutionary computation approach to analyze data from two well-known, classical “public goods” problems from economics: When goods are provided to a larger community without required individual contributions, it often results in “free-riding.” However, people also tend to show a willingness to cooperate and sacrifice for the good of the group.

“While OSC resources are more often used to make discoveries in fields such as physics, chemistry or the biosciences, or to solve complex industrial and manufacturing challenges, it is always fascinating to see how our research clients employ our supercomputers to address issues in broader fields of interest, such as we find in Dr. Bonakdarian’s work in economics and evolutionary computing,” said Ashok Krishnamurthy, interim co-executive director of the center.

“Evolutionary algorithms are inherently suitable for parallel or distributed execution,” Bonakdarian said. “Given the right platform, this would allow for the simultaneous evaluation of many candidate solutions, i.e., models, in parallel, greatly speeding up the work.”

Regression analysis has been the traditional tool for finding and establishing statistically significant relationships in research projects, such as for the economics examples Bonakdarian chose. As long as the number of independent variables is relatively small, or the experimenter has a fairly clear idea of the possible underlying relationship, it is feasible to derive the best using standard software packages and methodologies.

However, Bonakdarian cautioned that if the number of independent variables is large, and there is no intuitive sense about the possible relationship between these variables and the dependent variable, “the experimenter may have to go on an automated ‘fishing expedition’ to discover the important and relevant independent variables.”

As an alternative, Bonakdarian suggests using an evolutionary algorithm as a way to “evolve” the best minimal subset with the largest explanatory value.

“This approach offers more flexibility as the user can specify the exact search criteria on which to optimize the model,” he said. “The user can then examine a ranking of the top models found by the system. In addition to these measures, the algorithm can also be tuned to limit the number of variables in the final model. We believe that this ability to direct the search provides flexibility to the analyst and results in models that provide additional insights.”

Explore further: Forging a photo is easy, but how do you spot a fake?

More information: cs.franklin.edu/~esmail/Papers… EM11_Bonakdarian.pdf

Provided by Ohio Supercomputer Center

4.9 /5 (8 votes)

Related Stories

Free tool kit to assist big-data scientists

Jul 19, 2011

Two years ago, during a cyberinfrastructure meeting convened by the U.S. National Science Foundation, principal investigators from across the country found their scientific concerns begin to converge.

The kids are alright

May 26, 2011

Children should be seen and not heard... who says? A Philosophy academic at The University of Nottingham is challenging the adage by teaching primary school children to argue properly.

Modern society made up of all types

Nov 04, 2010

Modern society has an intense interest in classifying people into ‘types’, according to a University of Melbourne Cultural Historian, leading to potentially catastrophic life-changing outcomes for those typed – ...

Recommended for you

Forging a photo is easy, but how do you spot a fake?

Nov 21, 2014

Faking photographs is not a new phenomenon. The Cottingley Fairies seemed convincing to some in 1917, just as the images recently broadcast on Russian television, purporting to be satellite images showin ...

Algorithm, not live committee, performs author ranking

Nov 21, 2014

Thousands of authors' works enter the public domain each year, but only a small number of them end up being widely available. So how to choose the ones taking center-stage? And how well can a machine-learning ...

Professor proposes alternative to 'Turing Test'

Nov 19, 2014

(Phys.org) —A Georgia Tech professor is offering an alternative to the celebrated "Turing Test" to determine whether a machine or computer program exhibits human-level intelligence. The Turing Test - originally ...

Image descriptions from computers show gains

Nov 18, 2014

"Man in black shirt is playing guitar." "Man in blue wetsuit is surfing on wave." "Black and white dog jumps over bar." The picture captions were not written by humans but through software capable of accurately ...

Converting data into knowledge

Nov 17, 2014

When a movie-streaming service recommends a new film you might like, sometimes that recommendation becomes a new favorite; other times, the computer's suggestion really misses the mark. Yisong Yue, assistant ...

User comments : 1

Adjust slider to filter visible comments by rank

Display comments: newest first

Dug
not rated yet Aug 04, 2011
Sounds like a wonderful way to create mathematical bias without knowing it.

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.