This proposal develops a new perspective on social systems by combining analytical sociology with evolutionary game theory, agent-based modeling, computational social science, complexity science, and experimental research. Our focus on co-evolutionary processes will shed new light on emergent phenomena in social systems and elaborate a “social ecosystem” perspective. We will overcome problems of previous approaches by implementing social mechanisms, learning rules, and parameters in an evolutionary way. Our major goal is to show how a “homo socialis” can emerge as the result of an evolutionary competition. For this, we will demonstrate that strict payoff maximization or mechanistic, stimulus-response interactions may eventually be replaced by other-regarding behaviors. In particular, we will study how social cooperation and social norms emerge from repeated social interactions. We will furthermore equip agents with small virtual “brains” and simulate the co-evolution of individual and societal complexity. This will add cognitive complexity to our modeling approach and allow us to study origins and effects of subjectivity, but also early stages in human social evolution. We plan to demonstrate that, in a complex society, boundedly rational agents can perform equally well as a perfect “homo economicus”, and that both types of agents emerge and spread under different conditions. Our project is broad and highly interdisciplinary. It combines various methodologies and pursues an innovative complexity science approach to solve long-standing scientific puzzles. It has the potential to bridge previously incompatible research traditions by revealing unexpected and seemingly paradoxical relationships between them. Thereby, it will help to overcome existing controversies and the related fragmentation in the social sciences.