A dialogue on the role of computational modeling in developmental science: To model or not to model?

All sciences use models of some variety to understand complex phenomena. In developmental science, however, modeling is mostly limited to linear, algebraic descriptions of behavioral data. Some researchers have suggested that complex mathematical models of developmental phenomena are a viable (even necessary) tool that provide fertile ground for developing and testing theory as well as for generating new hypotheses and predictions. This paper explores the concerns, attitudes, and historical trends that underlie the tension between two cultures: one in which computational simulations of behavior are an important complement to observation and experimentation, and another which emphasizes evidence from behavioral experiments and linear models enhanced by verbal descriptions. This tension is explored as a dialogue between three characters: Ed (Experimental Developmentalist), Mira (Modeling Inclusive Research Advocate), and Phil (Philosopher of Science).All sciences use models of some variety to understand complex phenomena. In developmental science, however, modeling is mostly limited to linear, algebraic descriptions of behavioral data. Some researchers have suggested that complex mathematical models of developmental phenomena are a viable (even necessary) tool that provide fertile ground for developing and testing theory as well as for generating new hypotheses and predictions. This paper explores the concerns, attitudes, and historical trends that underlie the tension between two cultures: one in which computational simulations of behavior are an important complement to observation and experimentation, and another which emphasizes evidence from behavioral experiments and linear models enhanced by verbal descriptions. This tension is explored as a dialogue between three characters: Ed (Experimental Developmentalist), Mira (Modeling Inclusive Research Advocate), and Phil (Philosopher of Science).