We demonstrate that large language models can produce reasonable numerical ratings of the logical consistency of claims. We also outline a mathematical approach based on sheaf theory for lifting such ratings to hypertexts such as laws, jurisprudence, and social media and evaluating their consistency globally. This approach is a promising avenue to increasing consistency in and of government, as well as to combating mis- and disinformation and related ills.

I think that Wayne Eckerson was the first to define the "purple person" in a 2010 blog post--someone with the mix of business and technology skills that is present in many successful business intelligence and analytics people.1 The same idea also came up--independently, I believe--at insurance company XL Catlin. Jim Wilson, a lead data engineer at the company, was chatting with his boss, Kimberly Holmes, about the people issues the company's "strategic analytics" group faced every day. As Holmes describes the situation, Wilson used the "purple people" analogy to describe a particular problem: "The business people, the actuaries, know what data they need and can define requirements, but typically don't have the skill set to design a data architecture that gives them the data they need. Technology people typically don't understand the business requirements, but they can design the data architectures. It's like the people in IT speak blue, the people in business speak red, but we need people who speak purple in order to create an appropriate solution."