Proceedings of the Annual Meeting of the Cognitive Science Society

This paper investigates how different strategies affect the success and efficiency of scientific discovery, by examining different approaches in Galilean kinematics. Computational models with biases for inductive or deductive approaches to discovery were constructed to simulate the processes involved in finding coherent and empirically correct sets of laws. The performance of the models shows that the best overall strategy is to begin with an inductive bias and then perform tight cycles of law generation and experimental testing. Comparison of the models with previous findings indicates that the best overall strategy for discovery depends on the relative ease of search in hypothesis and experiment spaces.