It was nice to see the ABO section taking the lead on thinking about big data research. Two panelists (Steve Kaplan and Mandy Chen) brought very different perspectives to how we might approach big data and even more broadly Artificial Intelligence research from a behavioural perspective.

One of the key messages that I heard from the panel was that at times we are too good, as academics, at seeing the underlying structural issues of practice problems. There is so much research in both psychology and BAR about data presentation, pattern matching, and related topics that we do not realize that practitioners in AI and Big Data might not see the links. Why? because the surface features are so different. Steve Kaplan calls this academics needing to look for low hanging fruit, I call it a classic case of analogical reasoning difficulties when the surface features of a setting are too different to easily generate an analogy by busy practitioners trying to make a living. Lots of strong research on this analogical transfer problem by DeidreGunter and her colleagues.

Good example of ABO research was a paper on data visualization (Yibo (James) Zhang’s research). Zhang saw current data presentation issues in online annual reports through the lens of 1970’s era research by Shane Moriaty on the use of faces to transit complex financial statement analysis ratios. while the surface features are very different, it is a good structural analogy that when combined with later visualization research lead to some interesting hypotheses and results in zhang’s research.

But Steve and Mandy gave numerous examples of how these, what appearing on the surface a huge new challenges, can be studied via clever use of social and behavioural researchers core competencies.