Checkmating the Data Deluge
Assistant Professor of Data Analytics Eren Bilen outside Althouse Hall, home of the Department of Data Analytics. Photo by Dan Loh.
OFFICE HOURS: EREN BILEN, Assistant Professor of Data Analytics
by Tony Moore
Assistant Professor of Data Analytics Eren Bilen earned his Ph.D. in economics from the University of South Carolina. His primary research area is behavioral and applied economics, particularly centering on incentives and their role on performance in competitive environments. He has published in several academic journals, including the Journal of Economic Behavior & Organization and the Canadian Journal of Economics.
Data analytics is about as popular as it gets right now as an area of study, and Dickinson’s department is displaying real muscularity. What about the program here is drawing students to it like moths to a flame (or maybe a more apt metaphor would be, like moths to a computer monitor filled with an endless stream of 1s and 0s)?
I think it has to do with the broader “data revolution” we are all going through and the strength of our curriculum. The department did not cut corners when designing the curriculum. Other institutions, for example, typically don’t have the capacity to offer a very crucial course for the major such as the Ethics of Data. I think prospective students notice this and respond when considering Dickinson. I’d also emphasize that data analysis without a domain background is like wandering through a deep forest without a compass. We require our students to take a three-course sequence from a list that has 27 departments represented with more being added each semester. Interdisciplinarity is essential to data analytics, and it’s what I like the most about working with data. There are so many things the students can explore with data, from topics such as improving the efficiency of public transportation and finding the authorship of ancient texts to studying genomics or supernovas.
I see that a segment of your work “scrapes and analyzes data from an online chess platform with millions of moves made by players all around the world.” What are you doing with this sprawling tangle of information, and how does it play into your intellectual interests?
I’m a behavioral economist, and behavioral economists care about how people behave in different environments and situations. The particular (timely) question we are trying to answer focuses on whether players from hostile nations show aggression toward each other when they are randomly matched on chess.com. If war is just a game between politicians without common support, we should not expect any increase in aggression or changes in performance in our observations. But if people do hold hostile beliefs, our tools enable us to detect the potential changes. Our setup is unique because it is almost impossible to find people from hostile nations interacting with one another. But it turns out they play online chess against each other even during a war. Lastly, the evidence we provide is arguably more reliable than survey responses. As a behavioral scientist, I can assure you that what people say and how they act may not always align.
Data analytics has its fingers in a wide variety of pies—from economics to healthcare to geospatial analysis to art to biotechnology. What makes it so ubiquitous and relied on by so many industries and areas of study?
There is a titanic amount of data being generated by billions of people every second. To stay competitive, companies are essentially forced to be on top of their data analysis infrastructure. Governments must find reliable ways to collect and protect data. Researchers need to utilize data in creative and ambitious ways to test hypotheses. And the core idea to all of this is nothing new. The scientific process requires one to collect initial data, run some experiments and re-collect data to see how one’s environment changes. This is the exact same process Mendel took when constructing his theory about biological inheritance. What’s different in our modern time is the frequency and the scale of data. We have access to extremely strong computers at our fingertips. These computers can easily crunch the numbers and produce, for example, a super strong chess engine, or ChatGPT. Applications at such scale used to be science fiction, but now it is a reality.
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Published June 3, 2024