The best books I read in 2020 - generally released in other years - were: Albert Camus, The Plague Tom Chivers, The AI Does Not Hate You: Superintelligence, Rationality and the Race to Save the World: Great introduction to and history of the rationalist community. Melanie Mitchell, Artificial Intelligence, A Guide for Thinking Humans: Mitchell is too easy on humans, but a fair examination of where we are with AI and some great explanations of various AI approaches.
Below is the text of my presentation at Nudgsestock on 12 June 2020. Intro Over the past decade or two, behavioural scientists have had a great ride. There have been bestselling books and Nobel Memorial Prizes. Every second government department and corporate has set up a team. But recently, the wind seems to have changed. We’re told that behavioural economics is itself biased. “Don’t trust the psychologists on coronavirus - Many of the responses to Covid-19 come from a deeply-flawed discipline”.
Most articles on how behavioural science (or “behavioural economics”) can explain “X” are rubbish. “How behavioural economics explains Donald Trump’s election” or the equivalent would have been “How behavioural economics doomed Donald Trump” if he had failed to be elected. It’s after-the-fact storytelling of no scientific substance. Through the last six weeks I have been collecting examples in the media of behavioural science applied to the coronavirus pandemic. There’s plenty of the usual junk.
In a previous post I posed the following bet: Suppose you have $100 and are offered a gamble involving a series of coin flips. For each flip, heads will increase your wealth by 50%. Tails will decrease it by 40%. Flip 100 times. The changes in wealth under a sequence of flips of this nature is “non-ergodic”, as the expected value of the bet does not converge with its time-average growth rate.
Better late than never…. The best books I read in 2019 - generally released in other years - are below. Where I have reviewed, the link leads to that review (not many reviews this year). Nick Chater, The Mind is Flat: A great book in which Chater argues that there are no ‘hidden depths’ to our minds. Stephan Guyenet, The Hungry Brain: Outsmarting the Instincts that Make us Overeat: Excellent summary of modern nutrition research and how the body “regulates” its weight.
In my previous posts on loss aversion (here, here and here), I foreshadowed a post on how “ergodicity economics” might shed some light on whether we need loss aversion to explain people’s choices under uncertainty. This was to be that post, but the background material that I drafted is long enough to be a stand alone piece. I’ll turn to the application of ergodicity economics to loss aversion in a future post.
Summary: Much of the evidence for loss aversion is weak or ambiguous. The endowment effect and status quo bias are subject to multiple alternative explanations, including inertia. There is possibly better evidence for loss aversion in the response to risky bets, but what emerges does not appear to be a general principle of loss aversion. Rather, “loss aversion” is a conditional effect that most typically emerges when rejecting the bet is not the status quo and the stakes are material.
Behavioral Scientist put out the call to share hopes, fears, predictions and warnings about the next decade of behavioral science. Here’s my contribution: As behavioral scientists, we’re not exactly a diverse bunch. We’re university educated. We live in major cities. We work in academia, tech, consulting, banking and finance. And dare I say it, we’re rather liberal. Read the twitter streams or other public outputs of the major behavioral science institutions, publications and personalities, and the topics of interest don’t stray too far from what a Democratic politician (substitute your own nation’s centre-left party) would discuss in a stump speech.
Consider the following claim: We don’t need loss aversion to explain a person’s decision to reject a 50:50 bet to win $110 or lose $100. That just simple risk aversion as in expected utility theory. Risk aversion is the concept that we prefer certainty to a gamble with the same expected value. For example, a risk averse person would prefer $100 for certain over a 50-50 gamble between $0 and $200, which has an expected value of $100.
I am somewhat slow in posting this - the article has been up more than a week - but my latest article is up at Behavioral Scientist. The article is basically an argument that the scrutiny we are applying to algorithmic decision making should also be applied to human decision making systems. Our objective should be good decisions, whatever the source of the decision. The introduction to the article is below.