After watching the interview with Professor Brendan Meade and Professor Susan Murphy, I had a few questions about data science and modeling in science. Although Professor Susan Murphy’s definition of data science as anytime scientists are using data to try to improve the field is useful in its simplicity, are there multiple ways to interpret “improvement”? Further, what one person interprets as “improvement” might be perceived by another person as an encroachment on their free will and personal choice. For example, self-driving cars are seen by many scientists as an “improvement” but many other people are not supportive of the concept of self-driving cars. I was interested in the idea that more interesting problems are often more complex. What are some examples where this is true? During the interview, I also wondered about fields other than science where “triangulation” can add additional meaning and understanding. Finally, in the process of experimenting and inventing new things, how do scientists continually find new areas of study after they understand a previous topic well?
Here is the Prediction X interview with Professor Brendan Meade and Professor Susan Murphy!
I like your question about new topics, Emily. My own answer would be that the best questions lead to more new questions than they do answers--or at least that's been my experience as a scientist! (In essence, almost nothing is only fully "answered," only better understood.)