A plan to change how Harvard teaches economics
The difference big data&#x2F;data science&#x2F;empirical study and &#x27;classical&#x27; economics (by which I&#x27;d include any system of economics that seeks to explain human behavior via an underlying metatheory) is that a primarily empirical approach obscures the necessary underlying theory present in any experiment where you&#x27;re trying to fit data to a curve.<p>For example, when you run a science experiment and you plot the data, you may find that you&#x27;re looking at a line. While this is an interesting finding, it has zero predictive value for anything other than the exact situation you&#x27;ve collected data for. In order to formulate scientific law, you first must (a) believe that such a thing exists and (b) have some theory as to what shape the curve ought to fit. For example, a naive look at physics using an &#x27;empirical&#x27; approach might incorrectly conclude that force is mass times acceleration. While moderately useful for many problems, this offers little predictive power in the general case. In order to actually formulate a law that can be of predictive value, you have to first consider various other laws and axioms (such as the constant speed of light for force), at which point -- by deduction, without any need of empirism -- you determine that this is wrong, and you need another kind of equation to fit your data to.<p>I don&#x27;t know if the simplistic demand curves drawn in the original text book are correct or not. However, at least those are based on a particular set of assumptions that can be validated or not. The kind of empiricism put forth by Mr Chetty does not offer this at all.<p>All this is to say that, while data is useful for validation, it is not useful for prediction. The last thing we need is a black-box machine learning model to make major economic decisions off of. What we do need is proper models that are then validated, which don&#x27;t necessarily need &#x27;big data.&#x27;