Knowledge Lab Director James Evans gave the John Von Neumann lecture at the University of Wisconsin, Madison on Oct. 9th
Here's a summary of what James had to say:
"What factors affect a scientist’s choice of research problems? Qualitative research in the history, philosophy, and sociology of science suggests that the choice is shaped by an “essential tension” between a professional demand for productivity and a conflicting drive toward risky innovation. In this talk, I explore this tension and the efficiency of its resolution empirically in the context of biomedical chemistry. In related research, I use complex networks to represent the evolving state of scientific knowledge, as expressed in publications. With collaborators I have built measures and a model of scientific discovery informed by key properties of this network. Measuring such choices in aggregate, we find that high-risk behaviors, which explore new chemical relationships, are decreasingly prevalent in the literature, reflecting a growing focus on established knowledge at the expense of new opportunities. Research following a risky strategy is more likely to be ignored but also more likely to achieve high impact and recognition. While the outcome of a risky strategy has a higher expected reward than the outcome of a conservative strategy, the additional reward is insufficient to compensate for the additional risk. By studying the winners of major prizes, we show that the occasional “gamble” for extraordinary impact is the most plausible explanation for observed levels of risk-taking, but this may not be enough. Performing massive supercomputer experiments, we compare the efficiency of the typical research strategy with thousands of alternatives. Strategies of chemical discovery are efficient only for initial exploration of the network of chemical relationships. Much more efficient strategies for mature fields involve more individual risk-taking than the structure of modern scientific careers supports and I show how publication of experimental failures and investment in alternative paths of discovery could substantially improve the speed of discovery. I explore the implications of these findings for machine science--the expanded use of computation from analysis to hypothesis generation and scientific imagination."