LARB essay. "... I want to spend some time thinking about what counts as a romance novel, and why the genre — in all its permutations — matters."

"This article examines the economic effects of prizes with implications for the diversity of market positions, especially in cultural fields. Many prizes have three notable features that together yield an emergent reward structure: (1) consumers treat prizes as judgment devices when making purchase decisions, (2) prizes introduce sharp discontinuities between winners and also-rans, and (3) appealing to prize juries requires costly sacrifices of mass audience appeal. When all three conditions obtain, winning a prize is valuable, but seeking it is costly, so trying and failing yields the worst outcome—a logic we characterize as a Tullock lottery. We test the model with analyses of Oscar nominations and Hollywood films from 1985 through 2009. We create an innovative measure of prize-seeking, or “Oscar appeal,” on the basis of similarity to recent nominees in terms of such things as genre, plot keywords, and release date. We then show that Oscar appeal has no effect on profitability. However, this zero-order relationship conceals that returns to strong Oscar appeals are bimodal, with super-normal returns for nominees and large losses for snubs. We then argue that the effect of judgment devices on fields depends on how they structure and refract information."

Concise account of the uses of the Fisher Information Matrix.

""" An Outsider's Tour of Reinforcement Learning Ben Recht • Updated: Jun 25, 2018 Table of Contents. Make It Happen. Reinforcement Learning as prescriptive analytics. Total Control. Reinforcement Learning as Optimal Control. The Linearization Principle. If a machine learning algorithm does crazy things when restricted to linear models, it’s going to do crazy things on complex nonlinear models too. The Linear Quadratic Regulator. A quick intro to LQR as why it is a great baseline for benchmarking Reinforcement Learning. A Game of Chance to You to Him Is One of Real Skill. Laying out the rules of the RL Game and comparing to Iterative Learning Control. The Policy of Truth. Policy Gradient is a Gradient Free Optimization Method. A Model, You Know What I Mean? Nominal control and the power of models. Updates on Policy Gradients. Can we fix policy gradient with algorithmic enhancements? Clues for Which I Search and Choose. Simple methods solve apparently complex RL benchmarks. The Best Things in Life Are Model Free. PID control and its connection to optimization methods popular in machine learning. Catching Signals That Sound in the Dark. PID for iterative learning control. Lost Horizons. Relating popular techniques from RL to methods from Model Predictive Control. Coarse-ID Control. Combining high-dimensional statistics and robust optimization for the data-driven control of uncertain systems. Towards Actionable Intelligence. """

Textbook for UC Berkeley course "Data 8: Foundations of Data Science". Uses Jupyterbook.

Good work by John Mark Ockerbloom. Here's the start of the post, "Back in June, I announced that we had completed an inventory of all serials with active copyright renewals made through 1977, based on listings in the Copyright Office’s Catalog of Copyright Entries. At the time, I said we’d also be releasing a draft of suggested procedures for using the information there, along with other data, to quickly identify and check public domain serial content."

Project-Gutenberg-like for Japanese texts. (Wikipedia page)

Essay by Aaron Hanlon.

"This paper begins by outlining the basic logic for the implementation of intellectual property rights and detail alternatives to providing private monopolies to promote innovation."

A Paul Ford Businessweek essay on Bitcoin.

Concise intro to annealed importance sampling.

Data visualization mistakes made in The Economist, with corrected versions.

Cosma Shalizi's notes on time series modeling.