Transcript – An Information Theoretic Approach to Originality and Bias in Science

This research is about the originality in science. The originality of a scientific idea or research question is a generally accepted necessity to perform brilliant research. Yet too often, funding agencies and reviewers in high profile journals seem to disregard or not even understand this key component of research. Perhaps they have a lack of this quality or have other motives. For example, many scientists have been turned down multiple times by National Science Foundation with the following argument: High risk research, high risk research, high risk research. High risk research.

High risk means the results cannot be predicted, indicating an original open problem. The refusal of so-called high risk projects indicates that the preferred type of funded projects have largely predictable outcomes that make them low risk without surprises. One of the main conclusions of our T3 project is that NSF should actually filter and refuse all the science projects that are not high risk. Then it should select the winners solely from the set, considered as high risk based on validity and importance. To get to this conclusion, first of all, we must understand what is scientific research and how to measure the originality of a scientific question. Scientific research is the creation of new objective information about the target of the study. As Shaitan and Renee pointed out, the information entropy of the results, deterministically calculated from axioms, cannot be more than that of the axioms themselves. Cheating and others used computational complexity-based approach to free will. We introduced a new measure of originality, which is the information entropy of the scientific question. The higher the uncertainty about the output of the research, the higher is its information, entropy and originality. In this scheme, the scientific research process is forming a communication channel where the transmitter is the object of research and the recipient is the reader of the published paper. However, this simple approach of ours works only in simple, idealistic cases when bias, either natural or instrumental, or errors and error corrections are present. The entropy formula needs corrections. Our final formula gives a pessimistic approach to the potential information content, which is the originality of the research question.