In this paper we present an original model of sequential problem choice within scientific communities. Disciplinary knowledge is accumulated by solving problems emerging in a growing tree-like web of research areas. Knowledge production is sequential since the problems solved generate new problems that may be handled. The model allows us to study how the reward system in science influences the scientific community in stochastically selecting at each period its research agendas, and the long term resulting disciplines. We present some evidence on a decrease in the generation of new areas, a path dependency in specialization, and circumstances under which collapsing dynamics arise.
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Paper provided by Bureau d'Economie Théorique et Appliquée, ULP, Strasbourg in its series Working Papers of BETA with number
2003-12.