Learning from delayed rewards

The productivity and the impact are two most recognized aspects to evaluate the research performance of scientists. Figuring out whether and how these two factors shape the evolution of scientists’ research interests may facilitate researchers to go deep into scientists’ topic selection behavior. In this paper, we employ Microsoft Academic Graph as our data source, and propose two correlation metrics, by which over 20,000 scientists’ publication sequence from the computer science field are analyzed. We confirm that the productivity and the impact are related to the evolution of scientists’ research interests, and scientists tend to select topics which help them produce the productivity and the impact. To further explore how these two factors affects topic selection behavior, we propose a novel Q seashore walk model based on the interactive mechanism hypothesis. Our analysis results based on the simulation data are consistent with those based on the empirical data, which confirms the validity of our model and reports the evidence for the interactive mechanism. Based on the simulation data, we also analyze the role of reward for scientists’ research performance, and find that “too much is as bad as too little”. This research may help researchers deeply understand the process of topic selection, and provide a theoretical basis for research and development policy formulation.