Professionals from science, technology, engineering and mathematics (STEM) have been venturing into, and becoming thought leaders in, social sciences for several decades now. In particular, they have been working on humanities subjects such as psychology, sociology, economics, finance, management and anthropology in such a way that their projects can be included in natural sciences. In order to do so, they generally use high-end quantitative methods and econometric models. Increasingly, this practice continues into the 21st century.

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Humanities subjects study human attitudes, characters, preferences and behaviours, which vary from one individual to another. It is for this reason that STEM professionals use the ‘ceteris paribus’ clause, and a set of assumptions while conducting experiments, surveys, exploratory studies and research studies. The research outcomes do not work when the set of assumptions are relaxed, and it is a major limitation. Traditional theories such as the theory of demand and supply in economics, and theories of capital structures and market efficiency in finance, are based on certain specific assumptions. Such theories deliver the outcomes under some standard set of assumptions only.
In the present-day scenario, machine learning algorithms and techniques do attempt to narrow down differences in research outcomes as far as possible. Such algorithms record human behavioural patterns, preferences and cultural aspects to improve research predictions. For example, with the smartphone penetration to the grass-roots levels, better research prediction outcomes are being achieved.

Also, in a fast-changing world where technological advancements are taking place rapidly, many theories that were developed in social sciences previously are no longer applicable to the society. Therefore, modern researchers are working towards removal of assumptions and ceteris paribus clauses in their projects.

The author is assistant professor, Institute of Public Enterprise, Hyderabad