With the increasing ubiquity and power of mobile devices, as well as the prevalence of social systems, more and more
activities in our daily life are being recorded, tracked, and shared, creating the notion of “social media”. Such abundant and still growing real life data, known as “big data”, provide a tremendous research opportunity in many fields. To analyze, learn and understand such user-generated data, machine/social learning has been an important tool and various machine learning algorithms have been developed. However, since the user-generated data are the outcome of users’ decisions, actions and their socio-economic interactions, which are highly dynamic, without considering users’ local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignore users’ local interactions at the microeconomic level. As such there is a growing need in bridging machine/social learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomenon and local effects to understand/model/analyze better the newly arising issues in the emerging social media with user-generated data. In this paper, we present the emerging notion of “decision learning”, i.e. learning with strategic decision making, that involves users’ behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users’ optimal decision from a user’s perspective as well as design a mechanism from the system designer’s perspective to achieve a desirable outcome.