Gerd Gigerenzer’s collection of essays Rationality for Mortals: How People Cope with Uncertainty covers most of Gigerenzer’s typical turf: ecological rationality, heuristics that make us smart, understanding risk and so on. Below are observations on three of the more interesting essays: the first on different approaches to decision making, the second on the power of simple heuristics, and the third on how biologists treat decision making. Four ways to analyse decision making

In an excellent article over at Behavioral Scientist (read the whole piece), Koen Smets writes: A widespread misconception is that biases explain or even produce behavior. They don’t—they _describe_ behavior. The endowment effect does not _cause _people to demand more for a mug they received than a mug-less counterpart is prepared to pay for one. It is not _because of _the sunk cost fallacy that we hang on to a course of action we’ve invested a lot in already.

Michael Mauboussin’s Think Twice: Harnessing the Power of Counterintuition is a multi-disciplinary book on how to improve your decision making. Framed around eight common decision-making mistakes, Mauboussin draws on disciplines including psychology, complexity theory and statistics. Given the scope of the book, it does not reach great depth for most of its subject areas. But the interdisciplinary nature of the book means that most people are likely to find something new.

Before tackling Robert Sapolsky’s new book Behave: The Biology of Humans at Our Best and Worst, I decided to read Sapolsky’s earlier, well-regarded book Why Zebra’s Don’t Get Ulcers. I have been a fan of Sapolsky’s for some time, largely through his appearance on various podcasts. (This discussion with Sam Harris is excellent.) Why Zebra’s Don’t Get Ulcers is a wonderful book. Sapolsky is a great writer, and the science is interesting.

From an interview of Tom Griffiths by Julia Galef on the generally excellent Rationally Speaking podcast (transcript here): **Julia:** There's this ongoing debate in the heuristics and biases field and related fields. I’ll simplify here, but between, on the one hand, the traditional Kahneman and Tversky model of biases as the ways that human reasoning deviates from ideal reasoning, systematic mistakes that we make, and then on the other side of the debate are people, like for example Gigerenzer, who argue, "

From the preface of one print of Philip Tetlock’s Expert Political Judgement (hat tip to Robert Wiblin who quoted this passagein the introduction to an 80,000 hours podcast episode): The experts surest of their big-picture grasp of the deep drivers of history, the Isaiah Berlin–style “hedgehogs,” performed worse than their more diffident colleagues, or “foxes,” who stuck closer to the data at hand and saw merit in clashing schools of thought.

Daniel Engber writes: Picture the following situation: You are taking a freshman-level philosophy class in college, and your professor has just asked you to imagine a runaway trolley barreling down a track toward a group of five people. The only way to save them from being killed, the professor says, is to hit a switch that will turn the trolley onto an alternate set of tracks where it will kill one person instead of five.

Since first coming across Joshua Miller and Adam Sanurjo’s great work demonstrating that the hot-hand fallacy was itself a fallacy, I’ve been looking for a good way to explain simply the logic behind their argument. I haven’t found something that completely hits the mark yet, but the following explanation from Miller and Sanjurjo in The Conversation might be useful to some: In the landmark 1985 paper “The hot hand in basketball: On the misperception of random sequences,” psychologists Thomas Gilovich, Robert Vallone and Amos Tversky (GVT, for short) found that when studying basketball shooting data, the sequences of makes and misses are indistinguishable from the sequences of heads and tails one would expect to see from flipping a coin repeatedly.

Go back ten years, and most published attempts to link specific genetic variants to a trait were false. These candidate-gene studies were your classic, yet typically rubbish, “gene for X” paper. The proliferation of poor papers was in part because the studies were too small to discover the effects they were looking for (see here for some good videos describing the problems). As has become increasingly evident, most human traits are affected by thousands of genes, each with tiny effects.

I argued in a recent post that the conceptual replication of the marshmallow test was largely successful. A single data point - whether someone can wait for a larger reward - predicts future achievement. That replication has generated a lot of commentary. Most concerns the extension to the original study, an examination of whether the marshmallow test retained its predictive power if they accounted for factors such as the parent and child’s background (including socioeconomic status), home environment, and measures of the child’s behavioural and cognitive development.