Research innovation and selection processes

When selection is brought up in the context of science, it usually means the selection of models or theories with best fit in relation to the facts studied by researchers. This perspective is the classical view of authors such as David Hull and Donald Campbell. But there is another way of looking at research from a selection perspective, taking into account the interplay between available research resources and discovery heuristics such as the ones presented by Abbott

This approach can be described as follows: No matter how good an idea coming out of a discovery heuristic is, it will not be good enough for science unless it is feasible. The classical example is Kepler's theory of orbits having the trajectory of an ellipsis rather than a circle -- unless he had access to Tycho Braher's data to generate and confirm his model, the discovery would not have been made. The point here is that there has to be a fit between available research resources and bright ideas that can be tested using those resources. Ideas alone are in a vacuum that is not acceptable as science.

The Biomedical Research Innovation Language ontology actually provides a framework to classify both the resources (data, analysis methods, experts, infrastructure, and staff) and the innovation heuristics. Beyond the nomenclature in the ontology, and a lot of work is currently being devoted to actually make the two resources and innovation heuristics information result in inferential rules that could serve as decision support systems leading toward innovation. The caveat here is that these inferential rules are still fairly simple, and this is due to a real lack of understanding of what exactly is involved in the process of scientific innovation. In other words, to improve the fit between the information about existing resources and innovation heuristics we should know more details about issues such as:

• What exactly is involved in the intuition of researchers who consistently achieve high impact innovation?
  
• What do they consistently search for? What are their search heuristics?
• Is there a parallel between the way they think and the way publicity professionals think, both trying to achieve the highest possible amount of impact among their target audiences?

These are all questions to which we still don't have answers for, but that will hopefully be clarified over the next few years through some basic Research on Research.