If the previous section felt a bit tedious, it’s normal. Usually with simple examples like the one above, Bayesian statistics doesn’t feel particularly useful, and the complexity of changing frameworks feels hardly worth it. However, such situations rarely occur in real life as-is. I recently came upon a much more interesting use-case where the tradeoff between prior and likelihood modeling came up in a very interesting way.
Innovation, powered by R&D, is a cornerstone of economic expansion, enabling greater output from identical resources. This explains why nations worldwide, including the United States, support it through mechanisms like tax incentives and public research funding, such as university grants. The think tanks' recommendations to tax R&D would recklessly undermine these efforts. For instance, the CATO Institute advocates that the federal government claim a share of royalties when universities profit from patents originating from publicly funded studies. A more radical stance from the Brownstone Institute calls for the complete elimination of the Bayh-Dole licensing framework. These align with comparable suggestions from the Department of Commerce, which has even explored taxing patents directly.
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