As the world becomes increasingly digitally mediated, people can more and more easily form groups, teams, and communities around shared interests and goals. Yet there is a constant struggle across forms of social organization to maintain stability and coherency in the face of disparate individual experiences and agendas. When are collectives able to function and thrive despite these challenges? In this thesis I propose a theoretical framework for reasoning about collective intelligence --- the ability of people to accomplish their shared goals together. Strong general collective intelligence in the form of "rational group agency" arises from three conditions: aligned utilities, accurate shared beliefs, and coordinated actions. However, achieving these conditions can be difficult, as evidenced by impossibility results related to each condition from the literatures on social choice, belief aggregation, and distributed systems. The main contribution of this thesis is to study how human groups address these difficulties. To this end, I develop computational models of facets of human collective intelligence, and test these models in specific case studies. The models I introduce suggest distributed Bayesian inference as a framework for understanding shared belief formation, and also show that people can overcome other difficult computational challenges associated with achieving rational group agency, including balancing the group "exploration versus exploitation dilemma" for information gathering and inferring levels of "common p-belief" to coordinate actions. I conclude with a brief discussion on how the computational models I propose could inform the design of sociotechnical systems, and how the theoretical perspectives I introduce could relate to the future of computational social science.
Advisors: Josh Tenenbaum, Sandy Pentland
Committee: Leslie Kaelbling, John Tsitsiklis