Rating Growth of Scientific Knowledge and Risk from Theory Bubbles

TL;DR

This paper proposes a website to evaluate emerging scientific theories' potential by analyzing data trends, aiming to prevent risky theory bubbles and guide research investments.

Contribution

It introduces a novel data-driven approach to assess the future value of untested theories using publicly available indicators.

Findings

Data trends can predict the future impact of scientific theories.

Monitoring research activity helps identify risky theory bubbles.

The approach can inform funding and research priorities.

Abstract

In physics the value of a theory is measured by its agreement with experimental data. But how should the physics community gauge the value of an emerging theory that has not been tested experimentally as of yet? With no reality check, a hypothesis like string theory may linger for a while before physicists will know its actual value in describing nature. In this short article, I advocate the need for a website operated by graduate students that will use various measures of publicly available data (such as the growth rate of newly funded experiments, research grants, publications, and faculty jobs) to gauge the future dividends of various research frontiers. The analysis can benefit from past experience (e.g. in research areas that suffered from limited experimental data over long periods of time) and aim to alert the community of the risk from future theory bubbles.