Multi-agent system research is concerned with the issues surrounding the performance of collections of interacting agents. A major concern, therefore, is with the design of the decision making mechanism that the individual agents employ in order to determine which actions to take to achieve their goals. An attractive and much sought after property of this mechanism is that it produces decisions that are rational from the perspective of the individual agent. However, agents are also inherently social. Moreover, individual and social concerns often conflict, perhaps leading to inefficient performance of the individual and the system. To address these problems we propose a formal decision making framework, based on social welfare functions, that combines social and individual perspectives in a unified and flexible maner. The framework is realised in an exemplar computational setting and an empirical analysis is made of the relative performance of varying sociable decision making functions in a range of environments.