Here are the top three books I’ve read in 2019, presented below in chronological order by year published. While quite the cliché, the theme that emerged this year is to not judge a book by its cover. While Measure and Category by John Oxtoby appears to be a terse math treatise, it is a short, well-paced, lucid read (though requiring some prerequisites). Braudel’s The Structures of Everyday Life digs deeply into the minutiae of common experience in early modern Europe rather than providing overarching historical narrative. To finish, Haskel and Weslake’s Capitalism without Capital is a well-researched - if at times dull - look at intangible assets from an economic perspective whose title reminds one of a political polemic.
Below are the top eleven articles I’ve read in 2019. A theme of methodology runs through this set of papers, especially statistical methodology. There’s also some fun miscellany mixed in with blockchain (whose craze seems like a lifetime ago now), unicorns, and the history of the English language. To my surprise, all of these articles are from the present decade. They are presented in chronological order.
When approaching measure theory for the first time, the ideas can seem opaque and unmotivated. This is amplified since many students of measure theory are not coming from a strictly mathematics background and may be approaching the material on their own outside of the classroom. In addition to first-year math graduate students and advanced math undergraduates, students in stats, economics, the hard sciences, etc. will find their way into learning measure theory. This is a guide to resources for learning measure theory that tries to keep in mind that many (myself included) approach the material with an atypical background.
JSON is the typical format used by web services for message passing that’s also relatively human-readable. Despite being more human-readable than most alternatives, JSON objects can be quite complex. For analyzing complex JSON data in Python, there aren’t clear, general methods for extracting information (see here for a tutorial of working with JSON data in Python). This post provides a solution if one knows the path through the nested JSON to the desired information.
In epistemology, we often think of the things we believe as discrete propositions. For instance, you may believe that there is a computer screen in front of you. But how is this belief justified? One way of justifying a belief is by offering a reason, which can itself also be a proposition. For this next proposition, we can then ask how it is justified and so on. The regress problem asks the following question: if any of the things we believe are justified, then what is the structure of that justification? Does the justification question not just keep getting passed backward forever with reasons for reasons for reasons?
Here are the top three books I’ve read in 2018. They are presented below in chronological order. While these three books seem rather disparate, they are bound together by themes of innovation, conflict, and ideology.
Here are the top eleven papers I’ve come across in 2018.$^*$ These papers are mostly recent publications (within the last two years) with some older ones peppered in. They are in chronological order below.