In my last post I discussed how I would like to redo my article “Please Not Another Bias! An Evolutionary Take on Behavioural Economics”. Apart from the removing the weak experimental evidence that I referenced, I wanted to make a few points more explicitly, such as the need for theory. Well, that re-write is here in the latest edition of Works in Progress. Not much has survived from the original except the opening framing and the continued belief that evolutionary theory will play a role in developing that theory.

In 2015 I gave a presentation titled “Please Not Another Bias! An Evolutionary Take on Behavioural Economics” at the Marketing and Science Ideas Exchange (MSIX) conference. I posted my presentation on this blog, where it had around 100,000 readers in the first month (a lot for this blog). A copy of the post was the most popular post on Evonomics in its first year. I still see the post shared, which brings a slight cringe, as its not the article I would write today.

Last year I posted some notes for a course on Consumer Financial Decision Making. I have now refreshed those notes, but probably more usefully, also put them into book form using Quarto. Relative to this blog, which is built using markdown and Hugo, Quarto allows the incorporation of computation. It also allows for richer formatting and cross-referencing (without becoming the pain that is LaTeX), and easy production of an associated pdf.

0. Introduction In December last year Katherine Milkman and friends published a “megastudy” testing 54 interventions to increase the gym visits of 61,000 experimental participants. But more than just testing these interventions, the long list of authors stated: Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals.

In a seminar for a team from an investment manager I described how base rates are often neglected when people are grappling with conditional probabilities. My description was somewhat confusing, so the below is a short write-up for the participants. – Consider the following question scenario. You test yourself with a rapid antigen test for COVID-19. The following information is known: The probability that a person has COVID-19 is 1% (the prevalence).

A past regular feature of this blog was “A week of links’. Primarily, it was a useful way to aggregate interesting articles - I often search my blog posts for material (they are a collection of text files on my computer). But, the regularity of the feature drove my behaviour: I started looking for links for the post. So, I killed it. Now for the partial resurrection, with links posts to be delivered at random intervals to share articles or ideas that are worth a read.

In an oft-quoted and cited Nature paper, Business culture and dishonesty in the banking industry, Cohn and colleagues argue that the culture in banking weakens and undermines the honesty norm. In the abstract they state: [W]e show that employees of a large, international bank behave, on average, honestly in a control condition. However, when their professional identity as bank employees is rendered salient, a significant proportion of them become dishonest.

Over the last few years I have appeared on several podcasts, the most recent being a discussion with Phil Agnew on the Nudge podcast. I am definitely more a writer than a speaker, but if you prefer audio to the written, check out the below. Nudge podcast - Beware of Behaviour Science BS 42courses - Behavioural Science & Evolutionary Biology Todd Nief - Loss Aversion and Ergodicity Economics: This was a long and pretty technical conversation on the back of a primer I wrote on ergodicity economics.

I have always been a sucker for stories about an outsider tearing down what everyone believes to be true. With that, it’s no surprise that I have fond memories from my first read of M. Mitchell Waldrop’s book Complexity: The Emerging Science at the Edge of Order and Chaos (which must been around 20-years ago). The book is framed around the coalescence of a group of researchers into the Santa Fe Institute.

In Scarcity: Why having too little means so much, Sendhil Mullainathan and Eldar Shafir tell the following (now famous) story: To see the effect of scarcity on fluid intelligence, we ran some studies with our graduate student, Jiaying Zhao, in which we gave people in a New Jersey mall the Raven’s Progressive Matrices test. First, half the subjects were presented with simple hypothetical scenarios, such as this one: Imagine that your car has some trouble, which requires a $300 service.