Sequential problem choice and the reward system in Open Science

In this paper we present an original model of sequential problem choice within scientific communities. Disciplinary knowledge is accumulated in the form of a growing tree-like web of research areas. Knowledge production is sequential since the problems addressed generate new problems that may in turn be handled. This model allows us to study how the reward system in science influences the scientific community in stochastically selecting problems at each period. Long term evolution and generic features of the emerging disciplines as well as relative efficiency of problem selection are analyzed.