Title: Controlling the Average Behavior of Business Rule Programs

Abstract: Business Rules are a programming paradigm for nonprogrammer business users. They are designed to encode empirical knowledge of a business unit by means of “if-then” constructs. The classic example is that of a bank deciding whether to open a line of credit to a customer, depending on how the customer answers a list of questions. These questions are formulated by bank managers on the basis of the bank strategy and their own experience. Banks often have goals about target percentages of allowed loans. A natural question then arises: can the Business Rules be changed so as to meet that target on average? We tackle the question using “machine learning constrained” mathematical programs, which we solve using standard off-the-shelf solvers. We then generalize this to arbitrary decision problems.

Joint work with Christian de Sainte-Marie (IBM), Changhai Ke (IBM), Claudia D’Ambrosio (LIX), Leo Liberti (LIX)