With the increasing ubiquity and power of mobile devices, as well as the prevalence of social media, 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 big data, machine learning has been an important tool and various machine learning algorithms have been developed. However, since the user-generated big data is 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 decisions 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. In this talk, we present the notion of decision learning that can involve 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' perspective as well as design a mechanism from the system designer's perspective to achieve a desirable outcome.
Bibliographic reference. Liu, K. J. Ray (2014): "Decision learning in data science: where John Nash meets social media", In INTERSPEECH-2014 (abstract).