Amos Golan (American University; SFI External Professor)
Abstract. Although in principle prior information can significantly improve inference, incorporating incorrect prior information will bias the estimates of any inferential analysis. This fact deters many scientists from incorporating prior information into their inferential analyses. In the natural sciences, where experiments are more regularly conducted, and can be combined with other relevant information, prior information is often used in inferential analysis, despite it being sometimes nontrivial to specify what that information is and how to quantify that information. In the social sciences, however, prior information is often hard to come by and very hard to justify or validate. In this talk, I will review a number of ways to construct such information. This information emerges naturally, either from fundamental properties and characteristics of the systems studied or from logical reasoning about the problems being analyzed. Borrowing from concepts and philosophical reasoning used in the natural sciences, and within an info-metrics framework, I will discuss two different, yet complimentary, approaches for constructing prior information. I will provide some application to the social sciences.
(This talk is based on Chapter 8 of Info-Metric Foundations (Oxford Press, 2016) and on joint work with Robin Lumsdaine)