My view of Pedro Domingos’s The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World depends on which part of the book I am thinking about. The opening and the close of the book verge on techno-Panglossianism. The five chapters on the various “tribes” of machine learning, plus the chapter on learning without supervision, are excellent. And I simply don’t have the knowledge to judge the value of Domingos’s reports on his own progress to the master algorithm.

As my day job has shifted toward a statistics and data science focus, I’ve been reviewing a lot of online materials to get a feel for what is available – both for my learning and to see what might be good training for others. One course I went through was Coursera’s Executive Data Science Specialisation, created by John Hopkins University. Billed as the qualification to allow you to run a data science team, it is made up of five “one week” courses covering the basics of data science, building data science teams and managing data analysis processes.

In a sea of books describing a competition between perfectly rational decision makers and biased humans who make systematic errors in the way they decide, Brian Christian and Tom Griffiths’s Algorithms to Live By: The Computer Science of Human Decisions provides a nice contrast. Christian and Griffiths’s decision-making benchmarks are the algorithms developed by mathematicians, computer scientists and their friends. In that world, decision making under uncertainty involves major trade-offs between efficiency, accuracy and the types of errors you are willing to accept.

The best books I read in 2016 - generally released in other years - are below (in no particular order). For the non-fiction books, the links lead to my reviews. Joe Henrich’s The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (2015): A lot of interesting ideas, but left me with a lot of questions. Garett Jones’s Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own (2015): A fantastic exposition of some important but neglected features of the world.

I suspect I would have enjoyed Cal Newport’s So Good They Can’t Ignore You more if it had been written by a grumpy armchair economist. Newport’s advice is just what you would expect that economist to give: Get good at what you do (build human capital), then someone might be willing to pay you for it. If you simply follow your passion but you don’t offer anything of value, you likely won’t succeed.

I was triggered to write my recent posts on overconfidence and the illusion of control - pointing to doubts about the pervasiveness of these “biases” - by Phil Rosenzweig’s entertaining Left Brain, Right Stuff: How Leaders Make Winning Decisions. Part of the value of Rosenzweig’s book comes from his examination of some classic behavioural findings, as those recent posts show. But much of Rosenzweig’s major point concerns the application of behavioural findings to real-world decision making.

In the spirit of my recent post on overconfidence, the illusion of control is another “bias” where imperfect information might be a better explanation for what is occurring. The illusion of control is a common finding in psychology that people believe they can control things that they cannot. People would prefer to pick their lottery numbers than have them randomly allocated - being willing to even pay for the privilege. In laboratory games, people often report having control over outcomes that were randomly generated.

In 1995 Werner De Bondt and Richard Thaler wrote “Perhaps the most robust finding in the psychology of judgment and choice is that people are overconfident.” They are hardly been alone in making such a proclamation. And looking at the evidence, they seem to have a case. Take the following examples: When asked to estimate the length of the Nile by providing a range the respondent is 90% sure contains the correct answer, the estimate typically contains the correct answer only 50% of the time.

When humans compete against chimps in tests of working memory, information processing or strategic play, chimps often come out on top. If you briefly flash 10 digits on a screen before covering them up, a trained chimp will often better identify the order in which the numbers appeared (see here). Have us play matching pennies, and the chimp can converge on the predicted (Nash equilibrium) result faster than the slow to adapt humans.

Garett Jones has built much of his excellent Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own on foundations that, while relatively well established, are likely surprising (or even uncomfortable) for some people. Here’s a quick list off the top of my head: High scores in one area of IQ tests tends to show up in others - be that visual, maths, vocabulary etc. The “g factor” can capture almost half of the variation in performance across the different tests.