Choosing experiments to accelerate collective discovery

Andrey Rzhetsky; Jacob G. Foster; Ian T. Foster; James A. Evans

doi:10.1073/pnas.1509757112

New Research In

Edited by Yu Xie, University of Michigan, Ann Arbor, MI, and approved September 8, 2015 (received for review May 18, 2015)

Significance

Scientists perform a tiny subset of all possible experiments. What characterizes the experiments they choose? And what are the consequences of those choices for the pace of scientific discovery? We model scientific knowledge as a network and science as a sequence of experiments designed to gradually uncover it. By analyzing millions of biomedical articles published over 30 y, we find that biomedical scientists pursue conservative research strategies exploring the local neighborhood of central, important molecules. Although such strategies probably serve scientific careers, we show that they slow scientific advance, especially in mature fields, where more risk and less redundant experimentation would accelerate discovery of the network. We also consider institutional arrangements that could help science pursue these more efficient strategies.

Abstract

A scientist’s choice of research problem affects his or her personal career trajectory. Scientists’ combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity’s importance corresponds to its degree centrality, and a problem’s difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies.

Footnotes

↵¹To whom correspondence may be addressed. Email: arzhetsky{at}uchicago.edu or jevans{at}uchicago.edu.

Author contributions: A.R., J.G.F., and J.A.E. designed research; A.R., J.G.F., and J.A.E. analyzed data; and A.R., J.G.F., I.T.F., and J.A.E. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
*The notion that linking distant literatures is hard but potentially fruitful underwrites Swanson’s work on literature-based discovery (31).
^†Scientists often study several entities in combination. This complicates the modeling, so we approximate the discovery process with dyadic strategies.
^‡Some values of αμ and αι describe a mechanism analogous to preferential attachment (21, 33), in which researchers choose concepts in proportion to the product of their degrees. Our model encodes many types of preferential attachment, e.g., versions that are superlinear in the degrees. We find that such preferential attachment strategies can be much more efficient for discovery.
^§Observed behavior is generated by the interaction between preferences and the evolving set of opportunities. This makes interpretation subtle. For example, when considering chemicals in different connected components, a specific opportunity to combine them would be preferred (i.e., has a higher probability than an opportunity to connect similar chemicals at finite distance). Over time, however, more nodes enter the giant component. Hence, fewer opportunities exist to connect nodes in different components, leading to their small absolute number (Figs. S3B and S6).
^¶We assume that published research reflects the underlying distribution of research effort in a relatively undistorted way. Recent survey data on scientific choice are consistent with this assumption (41). Although we interpret Fig. 1 and Fig. S6D to imply that scientists pursue less risky projects over time, it is possible that scientists pursue such projects with the same intensity, but that fewer succeed and are published in later periods. We cannot tackle this issue directly, but consider how effort is screened by experimental failure, publication bias, etc., to produce the distribution of published choices. Our interpretation assumes that although a priori “risky” strategies (like combining two distant, low-degree chemicals) may fail more often than conservative alternatives, the risk is not so high that the published record no longer reflects the underlying distribution of effort. It also requires that risky strategies do not become much riskier over time. If the selection process has these plausible properties—i.e., it is well behaved and near stationary—then changes in the observed distribution and inferred parameters will track changes in the unobserved distribution of research effort and scientific choice.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1509757112/-/DCSupplemental.