1. More options, more action: contradicting a classic finding - a new paper challenges a classic medical choice overload story.

2. The study that just won’t die: disfluency edition - a decade after it was shown that disfluent fonts don’t boost accuracy, I keep running into claims they do.

As always, comments and feedback are welcome.

Cheers

Jason

1. Why I don’t trust most human-AI interaction experimental research: experimental practices in human-computer interaction remind me of psychology circa 2005.

2. Is following AI advice “anchoring bias”?: I don’t believe that’s the best way to describe it, especially when people are deceived about the accuracy of the AI advice.

3. Is there an AI workslop problem?: AI workslop exists, but that doesn’t tell us it’s a productivity disaster.

4. Pulling apart a classic nudge story: the loft insulation trial: Digging into the report on the trial shows it’s hard to call it a success.

As always, comments and feedback are welcome.

Cheers

Jason

–

Professor of Sociology William H. Sewell was deeply interested in social mobility. Do career aspirations affect career achievement? Do individual and social traits underlie those aspirations? Despite some preliminary research in the 1950s, Sewell lacked the data to answer these questions.

In 1962, Sewell made a lucky discovery: sitting unused in a University of Wisconsin administration building were the survey schedules and punch cards from a 1957 survey on the educational plans of all Wisconsin high school seniors. Now he had what he needed. He randomly selected 10,317 of these seniors and, in 1964, sent postcard surveys to their parents, asking about the seniors’ education, career aspirations, and socioeconomic status. Eighty-seven per cent of the parents responded. A 1975 telephone survey of the graduates themselves had a response rate of 89 per cent.

These steps began the Wisconsin Longitudinal Study; longitudinal studies observe subjects over time. In 1977, the survey expanded to include randomly selected siblings of the graduates. By 2020, the survey had accumulated over 60 years of data. The high school graduates were 81 years old.

In 2006-07 was a significant milestone. Saliva sample kits for genetic analysis were posted to participants. Kits were mailed in 2011 to those who missed the first round. Overall, 64 per cent of graduates and 36 per cent of siblings provided samples, covering about half of the 18,000 participants.

As genetic data doesn’t change, these samples enriched 50 years of data, including that initial collection in 1957. Each new collection, such as those in 2011 and 2020, is also augmented by this genetic data.

In 2018, Daniel Belsky and colleagues published a paper in the Proceedings of the National Academy of Sciences using the Wisconsin Longitudinal Study’s genetic data. For each student, they calculated an education “polygenic score”, a measure of the genetic influence on educational attainment. Students with higher scores had higher career success and social rank than their parents did in 1957. They were upwardly mobile. This observation held within families: siblings with higher scores achieved more as well.

As genetics are the cause of many phenomena we study, genetic data can be of immense value. Studying the mechanisms of DNA transmission and recombination between generations can help policymakers investigating issues such as social mobility, poverty, and inequality. We could assess interventions, from education to tax reform to childcare. By learning which interventions work, we could allocate public resources more efficiently. Kathryn Paige Harden makes the case for the value of genetic analysis comprehensively in the excellent The Genetic Lottery: Why DNA Matters for Social Equality. In what follows, I will remake that case in only the most superficial way.

However, there is a major barrier to using genetic data in this way: most of the datasets we use to investigate socio-economic phenomena do not include genetic data, preventing us from including genetics in our analyses.

We should address this problem by collecting genetic data from the participants in longitudinal research. As our DNA is fixed for life, we should supplement old longitudinal data with genetic data, enriching decades of past work. The addition of genetic data to the Wisconsin Longitudinal Survey provides a template.

We could also be bringing genetic data to bear in our experimental work. The addition of genetic data to experimental panels could provide rich insight into the heterogeneity of behaviour.

Importantly, we can do this now. While many discussions on genetic data in social science focus on growing sample size, new tools and future possibilities, existing tools can give us insight today and bring future possibilities to life.

In what follows, I will first touch on two topics of interest to economists (my own profession): using genetic transmission to infer causation and some examples of genetic data applied to questions of social mobility and inequality. That will take me to my main point, that we should be supplementing our core research datasets with genetic data now.

Causation

There is abundant data indicating the intergenerational persistence of educational outcomes and socioeconomic status. Here are two Australian examples (I’ll lean on material from my home country, which I know best): A Year 9 student (aged 14-15) whose parents have a bachelor’s degree or higher will, on average, have numeracy skills almost four years ahead of those of classmates whose parents do not have a bachelor’s degree. A child born in Australia to a family in the bottom 20 per cent of parental incomes has a 12 per cent chance of being in the top 20 per cent of incomes 30 years later.

For an economic policymaker, these statistics raise questions. Do the children of educated, wealthy parents have an unfair advantage? Would equalising wealth through taxes and transfers close the intergenerational gap? Designing robust solutions requires accurately assessing causality, but a correlation between child and parent outcomes does not prove that higher parental education or socioeconomic class causes outcomes in the next generation. We need to consider if a third factor might be “confounding” the result.

A likely third factor is genetics. Children and parents share DNA. If parents genetically transmitted traits like intelligence and conscientiousness to their children, we could see a correlation between the child and parent even if parental education or income had no direct effect on the child. It should not be controversial to say that genetics could underlie this result: the first law of behaviour genetics is that all human behavioural traits are heritable.

The genetic confound raises the question of how to infer the cause. Economists are infatuated with causation; absent a randomised controlled trial, they scour the world for interesting data sets and quasi-experiments to tease out causality. This pursuit led to innovative approaches to infer causation. Economists celebrate the “credibility revolution”, demanding rigorous study design.

An example quasi-experiment concerns protests in Paris in May 1968, which led authorities to be lenient in university entrance exams. Eric Maurin and Sandra McNally studied students who barely passed despite the increased leniency, and likely would not have been accepted in other years. These students earned higher future wages, and, in turn, their children obtained more education. As the riot did not affect the genetics of the parents, we can take the change in schooling as the cause.

Economists’ focus on causation does have a benefit. The social science literature is littered with studies for which genetics is an obvious confound. There are fewer examples in the economics literature (that is my impression, at least). Although economists rarely discuss genetics, they prefer experimental designs that avoid confounds. That, however, places a constraint on the questions that we can answer. Thankfully, overcoming the genetic confound does not always require a city to descend into riots.

Human genomes consist of 3 billion base pairs, with over 99 per cent of base pairs the same from person to person. Most of the variation between people are single nucleotide polymorphisms (SNPs), changes in just one base pair that are present in at least 1 per cent of the population. Genetic databases, like the Wisconsin Longitudinal Study, typically contain samples of SNPs. If you’ve taken a DNA test from companies like 23andMe (R.I.P.) or MyHeritage, they analysed your SNPs too.

Research using SNP data suggests that most traits are polygenic: that is, many genes underlie the traits’ heritability. For example, one study identified 3,952 SNPs linked to educational attainment. This reflects a proposed fourth law of behaviour genetics: ‘A typical human behavioural trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioural variability.’

A long list of SNPs alone is not useful for examining social science outcomes. As a result, scientists developed polygenic scores, a weighted count of SNPs enhancing a trait. To give an example, one study found that a person in the 84th percentile of polygenic scores for educational attainment was 19 per cent more likely to complete a university degree than someone with an average score. It may not sound like much, but this is a similar effect size to that of many social and cultural factors, like family socioeconomic status.

Despite the apparent link between polygenic scores and outcomes, this is again correlation and not proof of causation. Does a link between polygenic score and outcome mean genetics caused the difference? We cannot jump to an answer of “yes”. Population stratification may be at play, where genetic differences arise between groups due to historical, cultural and social factors. For example, intermarriage among highly educated groups can lead to genetic variant concentration over time, showing genetics to be a historic contingency and not truly causal. We can at least rule out reverse causation; education does not alter DNA.

However, the nature of DNA transmission from parent to child provides a mechanism by which we can get closer to the cause. Your genetic material is organized into 23 pairs of chromosomes, one-half of each pair coming from each of your father and mother. Each parent’s chromosomes in turn came from your grandparents, but you didn’t receive exact copies of your grandparents’ chromosomes. During the creation of your parents’ eggs and sperm, your grandparents’ chromosomes were spliced together, resulting in each egg or sperm having different combinations of your grandparents’ chromosomes. Women create about 45 splices, and men create about 26. The result is that siblings receive a random draw from each parent’s chromosomes. This draw involves a small number of chunks (around 23+45=68 from the mother and 23+26=49 from the father), not each of the three billion base pairs independently. Because of this small number of chunks, the genetic relatedness between siblings can vary significantly from the average of 50 per cent, with most siblings sharing between 43 per cent and 57 per cent of their genetic material. My identical twin sons share only 41 per cent of their DNA with their younger brother.

Understanding how DNA is transmitted provides a fantastic opportunity. Historically, behaviour genetics relied on comparing identical and fraternal twins or examining adoptees. Relatedness data now allows an extension of the twin study methodology to families without twins. The lower variation in relatedness between siblings (as compared to identical versus fraternal twins) reduces the ability to link between genes and outcomes, but this is offset by the larger samples available when you are no longer constrained to twin samples. Researchers have since expanded this methodology to include broader population samples, not just siblings, to estimate the heritability of traits like educational attainment.

Within-family variation in SNPs also allows us to calculate more robust polygenic scores. Since each sibling’s set of SNPs is the outcome of a lottery, causation is less susceptible to bias.

Another opportunity arises because some genetic variants are not passed from parent to child. If these non-transmitted variants affect child outcomes, we can rule out genetic transmission and focus on environmental channels such as socioeconomic status. Kathryn Paige Harden and Philipp Koellinger call this a ‘virtual parent’ design, which mimics adoption studies in that the children are raised by someone with different genes. Kong and colleagues used this premise to show that the effect of the non-transmitted variants on child education was 30 per cent as strong as the transmitted polygenic score.

The methodologies underlying this research are still in development and subject to some interesting debates. Are we effectively controlling for population stratification when we don’t have family-based samples? However, that is not a barrier to building data now, and further data collection will support resolving such debates.

Illustrating the applications

For all of the evidence indicating their influence, genes are not destiny. An example is Arthur Goldberger’s thought experiment about eyeglasses. Poor eyesight due to genetics might be corrected by an environmental intervention.

Although Goldberger made this point to argue against using heritability in policy development, studies of intergenerational status have shown genes and environment are interconnected. Methodologies such as those described above can help us understand the causes of transmission, explore what policy interventions are most prospective and examine the distributional effects of those policies.

This article is not a comprehensive review of policy relevant research, but below are some brief illustrations related to social mobility and intergenerational transmission of socioeconomic status to give a flavour of the questions that can be illuminated with genetic data.

What is the optimal level of social mobility?

I don’t know the answer to that question, but any answer requires us to consider genetics. With genetic endowments, random sorting will not emerge in a society with equal environments. Consider the following. A study of Finnish twins found a substantial genetic effect on lifetime earnings: around 40 per cent of the variance in women’s earnings and around half for men’s. Twenty-one other studies in Australia, Sweden and the United States produced similar estimates for genetic contribution, with the effect of shared environment, comprising common environmental factors such as parental socioeconomic status, being around 9 per cent. This is the second law of behaviour genetics in action: the effect of being raised in the same family is smaller than the effect of genes. However, is heritability capturing an inherent characteristic of the child, the parental response to the child’s genotype, or the environment created due to the parental genotype that is also shared with the child?

Analyses using genetic data are nascent, but they can shed light on this question. Polygenic scores for education are linked to higher socioeconomic status and better labour outcomes, even among siblings. However, those who grow up in high-status households tend to have higher college completion and better socioeconomic outcomes independent of their score. Further, there is a stronger relationship between polygenic scores and college completion in higher socioeconomic groups. While this pattern may partly reflect unobserved genetic variation - polygenic scores capture only some of the heritability of traits - this evidence suggests an opportunity to improve outcomes for talented students in low-status families.

There is growing research into the transmission of skills, one of the pathways by which socioeconomic status might persist. One study identified three genetic pathways: the direct genetic effect, whereby both parent and child have genes that increase their skills; parental investment in children with higher polygenic scores; and parents with higher genetic factors themselves investing more in their children. This study indicated that ignoring genetics overestimates the effect of parental investment on child skills, but that the environment created by the parent, influenced by their genetics, also matters, at least for the children aged seven years or less examined in this study. Examining these investments may provide intervention ideas.

Another study used genetic data to provide insight into the accumulation of wealth. People with a higher polygenic score for years of schooling had greater household wealth at retirement. Those with scores in the 84th percentile had over $150,000 extra wealth. A polygenic score premium persisted even after controlling for education and income, suggesting the score captures other skills. One policy insight comes from a gene-environment interaction. The relationship between wealth and polygenic score was four times as large for those without defined benefit pensions, which involve a guaranteed income and require few decisions about money allocation. Those with lower polygenic scores tended to struggle with managing their retirement investments, indicating that freedom can harm those who find complex financial decisions difficult. This finding could inform policy. In Australia, compulsory defined accumulation plans divert a portion of income into tax-advantaged retirement accounts that cannot be accessed until retirement. This is an ‘eyeglasses’ solution to the problem, albeit everyone gets eyeglasses regardless of need. A more targeted response might be Australia’s relatively generous means-tested pension system.

Genetic factors may also illuminate the distributional effects of policy. Taxes on tobacco were intended to curb usage, but as Jason Fletcher argued in a speculative article, genetically disadvantaged populations might bear a higher burden. Variants of nicotine receptor genes may trigger different responses; those with higher reward responses in the brain didn’t reduce smoking despite the higher cost. This suggests that diversion through nicotine substitutes may be more effective than taxes for some populations.

Examples of heterogeneous genetic responses to policy interventions are growing. Raising the minimum school leaving age reduced the body mass index of those genetically prone to obesity. Students with low polygenic scores attending advantaged schools were less likely to drop out of math classes. Similarly, the relationship between polygenic scores and high school dropout was weaker in higher socioeconomic families, suggesting an ability to buffer against the worst outcomes. A useful heuristic to consider whether responses might vary with genetics is to consider whether they vary with education, income, or other socioeconomic factors. Genetics likely underlies many of these non-genetic variables.

The small but growing body of research provides a consistent picture: genetics influence social mobility and interact with environmental factors in non-obvious ways. Absent genetics, we struggle to assess the causes and overlook insights that can help us evaluate interventions.

The practical steps

Despite the above examples, there are many unexploited opportunities to use genetic data in economics and policy development. The literature is rich but small.

However, to exploit these opportunities, we need to enhance our datasets with genetic data. We need to build the genetic infrastructure. When economists plan research using longitudinal datasets or experimental panels, they shouldn’t have to think about how to collect saliva samples or the cost of genetic analysis. The genetic data should be there by default.

Enhancing longitudinal data

Longitudinal data sets are valuable for their multidimensionality over time, allowing researchers to track changes and examine the participants’ life paths. Ask an applied economist about their most valuable research resource, and they will often point to longitudinal data.

In Australia, our best-known longitudinal dataset is the Household, Income and Labour Dynamics in Australia (HILDA) Survey. Begun in 2001 with a sample of over 7,000 households, 22 years of data are now available to researchers. Over 1,000 papers have been published using HILDA data on topics ranging from how losing a job affects who does chores at home to the effect of stock market performance on well-being to intergenerational social mobility.

In the United States, perhaps the best-known longitudinal surveys are the National Longitudinal Surveys (NLS) sponsored by the U.S. Bureau of Labour Statistics. These include the National Longitudinal Study of Youth 1979 (tracking subjects born between 1957 and 1964) and 1997 (tracking subjects born between 1980 and 1984). Named for their starting years, both studies are still running today.

What makes these longitudinal resources valuable is the sheer depth of the surveys across topic areas and time. HILDA includes data on ancestry, children, education, finance, health, housing, labour force outcomes, skills and relationships. The challenge, however, is that even with the richness of the resource, it can take some ingenuity to infer what is causing the observed outcomes. Researchers use mechanisms such as the ordering of events, assuming causation must flow forward through time. But this typically has severe constraints.

Again, genetics provides a potential tool. If seeking to examine how parent behaviour affects child outcomes over time, genetic data could be used to disentangle environmental and genetic causes, and where genetic, examine the pathways by which the genetics operate. Even where genetic data does not identify causation, it might provide insight.

Unfortunately, many of our most valuable longitudinal datasets do not have associated genetic data. There are a few exceptions, including the Wisconsin Longitudinal Study and others that underlie the examples in this article. The Twins Early Development Study, which has been tracking 15,000 twins born in the UK between 1994 and 1996, is now augmented with genetic data. The Dunedin study and the National Longitudinal Survey on Adolescent Health also contain genetic data. There are others, some of which underlie the examples in this article.

For every longitudinal dataset, we should augment data collection with genetic data. This does not apply only to new surveys. We could augment existing surveys with that data. Genetic data obtained from participants today could be used to analyse Round 1 HILDA or National Longitudinal Survey data. There are many studies where people have been tracked for decades where participants are still available. If genetic data collection is extended to families, many of the approaches to causation described in this article become available. The Wisconsin Longitudinal Study is an example of this occurring.

The nature of longitudinal surveys makes genetic sampling prospective. Longitudinal surveys have the benefit of a strong relationship with the participants. Participants already provide much sensitive information and may be willing to contribute genetic data. Most longitudinal surveys already have strong privacy protocols in place, controlling access to data based on sensitivity. Similar privacy measures can be applied to genetic data, addressing concerns such as the use of the genetic data to identify individuals. Instead of making the SNP data directly available, a set of polygenic scores and relatedness data within families could be released. If researchers need access to more detailed data, the existing access controls for the more sensitive longitudinal data provide prior art. The Wisconsin Longitudinal Study has such tiered access, with more stringent applications for SNP data than for aggregated polygenic scores.

The sheer volume of research from the major longitudinal surveys makes sampling highly cost-effective. With costs comfortably below $100 per person, the cost of genotyping for, say, the NLS Youth Surveys or HILDA is less than $1 million each. Sample collection can be done by post. The thousands of papers using this data, not to mention its use in government and by policymakers, make this a high-value step.

The cost-effectiveness will only increase. Genotyping and sequencing are becoming cheaper. Soon genetic samples will expand to the whole genome, capturing more rare variants and mitigating problems of population stratification. The polygenic scores that can be constructed with the genetic data will only increase in power.

Enhancing experimental data

While most of the above discussion has been focussed on research using longitudinal datasets, there is also an opportunity to use genetic data to increase insight from experimental work.

Historically, students have been the typical subjects in economics or psychology experiments. They’re cheap and widely available on university campuses. This reliance on a narrow population slice led to an inevitable (at least in hindsight) critique. Behaviour varies across populations. The standard experimental subject - someone Western, Educated, Industrialised, Rich and Democratic (WEIRD) - is not representative. The resulting data skew exists both across populations or societies and within them. College-educated students are not representative of those without a college education. The same holds for the rich and the poor.

One solution is to broaden subject pools. Today, experiments are less often conducted with students (although it’s unclear whether experimental participants sourced through Amazon’s crowdsourcing platform Mechanical Turk are more representative of humanity).

Another approach has been to examine how participants’ responses vary within experiments. Behavioural economists often point to heterogeneity as the future of applied behavioural science. People vary in capabilities, resources, goals and preferences. As a result, behavioural economists need to move beyond a one-size-fits-all philosophy and use personalised nudges. Practically, researchers address this by collecting data on gender, income, education and other demographic variables and studying how responses vary with demographic differences.

Genetics may enhance our understanding of heterogeneity. Per the first law of behavioural genetics, genetics may drive variability in experimental behaviour. Further, many characteristics that we identify as a source of variation may not capture the underlying cause. Genetic data can help us determine whether wealthy people respond differently because they are richer, or because they have characteristics that tend to lead to wealth. Genetic data offers a way to check that randomised controlled trials are balanced - that is, to test the assumption that each group is the same. It also allows us to increase the power of the analysis - our ability to detect an effect - by accounting for some of the variation between the experimental participants.

Integrating genetic data into experiments can let us investigate associations discovered in observational data. For example, the link between polygenic scores for educational attainment and wealth at retirement may be due to differences in the ability to make complex decisions. We could investigate that hypothesis through experiments examining how complex decision-making skills vary with the polygenic score. Experiments could provide a test bed for interventions, or at least inform policy discussions.

Enabling the use of genetics in experimental work requires building panels - collections of people who have registered to participate in experiments - with genetic data available for each participant. Each panel member could be genotyped, with the experimenter provided with polygenic scores for a range of outcomes of interest for each participant. The aggregated nature of these scores makes reidentification near impossible. As the effect sizes associated with polygenic scores are typically as strong or stronger than those for many social science interventions, genetic effects could be detected in experiments with as few participants as the typical lab experiment.

A version of this has been done in the past with panels of twins, although in that case the genetic data is typically limited to whether the twins are mono- or dizygotic. For example, Twins Research Australia maintains a panel of 35,000 twin pairs, which researchers may apply to access existing data or run a new study. One team of researchers developed the Australian Twins Economic Preferences Survey using that data.

Incorporating genetic data into experimental work may be a bigger challenge than for longitudinal data, as there are no ready-made data collection and access arrangements. These would require investment. However, genotyping or sequencing cost are unlikely to be prohibitive. As panel participants typically participate in many experiments, the cost of the genetic tests could be spread over them. Since genetic data doesn’t change - unlike other attributes measured in experimental studies - it only needs to be collected once. Further, genetic data is concrete and not self-reported, so is thereby more consistent and reliable.

Alternatively, existing genetic research resources could be expanded in purpose. The UK Biobank database contains genetic, lifestyle and health data for half a million UK participants (albeit it could be stronger with more family-based participants). Estonia, Iceland and the Scandinavian countries have large genetic databases. These could provide experimental participants together with polygenic scores. Experimental data then forms part of the growing data resource. Commercial providers such as 23andMe with large customer databases could even seek alternative revenue sources by facilitating the provision of participants for experimental studies.

Critical to the value of any future data sets is representative population sampling. However, polygenic scores are typically developed from homogeneous population groups, most commonly with European ancestry. As a result, polygenic scores cannot simply be plugged into analyses of diverse groups. Genetic data itself doesn’t solve the WEIRD problem if research panels remain WEIRD. These are the important but ultimately tractable issues we should be grappling with.

My PhD was on the link between human evolution and economic growth. When I presented a draft of my PhD research proposal, the first comment I received was that I should refuse any grants from men with funny little moustaches and straight-arm salutes. Although that commenter came around, this initial reaction is a typical response to a discussion of genetics in social science.

That ‘fear’ of the implications of genetics, however, is not the only obstacle to its use. Genetic data is simply not available for many studies. Its absence makes it easy to ignore. If the authors don’t mention genetics in their analysis, few peer reviewers will criticise them for overlooking an obvious confound. They can’t ask for further analysis when the data is not there.

The result is that, despite examples of the type I have discussed above, the potential for genetics to inform our thinking on important policy questions is untapped. I trawled policy-focused papers on social mobility in Australia, including Treasury policy briefs, reports by the Australian Institute for Health and Welfare and speeches by the head of the Australian Government’s major economic think tank. Genetics does not get a mention. It is possible to dig up the occasional left-of-centre politician who realises genetics can affect social mobility (albeit that politician was an economics professor who published on social mobility). However, even they are silent on genetics when they move into political mode.

That said, it is hard for policymakers to engage with genetic questions when the research comes from longitudinal datasets without genetic data. Insightful papers examining the genetics of social mobility or other economic questions are rare. When faced with a particular policy question, it is unlikely that genetic analysis is available, especially one that matches relevant populations or precise policy measures.

That is why enriching our economic datasets is so important. A robust genetic data foundation is crucial for advancing our understanding of policy questions such as social mobility, inequality, skill development, and the diversity of our responses to government interventions. By enabling more studies that incorporate genetics, we can expose the limitations of research that ignores this critical factor. Only then can we answer vital questions and develop more effective policies.

The list is copied below.

This reading list is a balance to the one-dimensional view in many popular books, TED talks, conferences, academic press releases and consultancy sales pitches. For those who feel they have a good understanding of the literature after reading Thinking Fast and Slow, Predictably Irrational and Nudge, this is for you. [In the time since I drafted the first version of this list in 2017, it’s fair to say that the balance has swung a bit.]

The purpose of this reading list is not to imply that all behavioural economics or behavioural science is bunk (it’s not). That said, I did not design the list to be balanced; you can combine this list with plenty of reading lists from elsewhere for that.

Please let me know if there are any other books or articles I should add, or if there are any particularly good replies to what I have listed. I am sure I have missed some good ones. I have set a mild quality bar on what I have included. I don’t agree with all the arguments, but everything on the list has at least one interesting idea.

1. Books

Gerd Gigerenzer, Peter Todd and the ABC Research Group, Simple Heuristics That Make Us Smart: Simple heuristics can be both fast and accurate, particularly when we assess real-life performance rather than conformity with the principles of rationality.

Doug Kenrick and Vlad Griskevicius, The Rational Animal: How Evolution Made Us Smarter Than We Think: A good introduction to the idea that evolutionary psychology could add a lot of value to behavioural economics, but has the occasional straw man discussion of economics and a heavy reliance on priming research (and you will see below how that is panning out).

David Levine, Is Behavioural Economics Doomed?: A good but slightly frustrating read. I agree with Levine’s central argument that rationality is underweighted, but the book is littered with straw man arguments.

Lionel Page, Optimally irrational: The Good Reasons We Behave the Way We Do: We should invest more in understanding why people behave the way they do.

Mario J. Rizzo and Glen Whitman, Escaping Paternalism: Rationality, Behavioral Economics, and Public Policy: An excellent critique of the traditional behavioural economists’ arguments for paternalism.

Phil Rosenzweig, Left Brain, Right Stuff: How Leaders Make Winning Decisions: An entertaining examination of how behavioural economics findings hold up for real world decision-making.

Gilles Saint-Paul, The Tyranny of Utility: Behavioral Social Science and the Rise of Paternalism: Sometimes hard to share Saint-Paul’s anger, but some important underlying points.

Hugo Mercier, Not Born Yesterday: The Science of Who We Trust and What We Believe: A strong argument that we are not gullible and easily manipulated, but rather skeptical and rational in the way we filter information.

Robert Sugden’s The Community of Advantage: A Behavioural Economist’s Defence of the Market: A well balanced critique from someone who has worked in the field for decades.

2. General and methodological critiques

Applied behavioural economics: In The death of behavioral economics, Jason Hreha argues that applied behavioural economics is on the way out. Scott Alexander responds.

Are we biased?: Gerd Gigerenzer debates Daniel Kahneman and Amos Tversky. Gigerenzer tees off (pdf). Kahneman and Tversky respond (pdf - this pdf also includes a rejoinder to Gigerenzer’s later piece). Gigerenzer returns (pdf). I’m a fan of a lot of Gigerenzer’s work, but his strength has never been the direct attack. Kahneman and Tversky get the better of this exchange. My post here.

As-if models: Nathan Berg and Gerd Gigerenzer note that behavioral economics is neoclassical economics in disguise (pdf of working paper). They write that “‘As-if’ arguments are frequently put forward in behavioral economics to justify ‘psychological’ models that add new parameters to fit decision outcome data rather than specifying more realistic or empirically supported psychological processes that genuinely explain these data.” Includes a critique of prospect theory’s lack of realism as a decision-making process.

Critiquing economics I: Ken Binmore argues (pdf) that the claim “economic man” is a failure can be both attacking a position not held by economics and ignoring the experimental evidence of people behaving like “economic man”.

Critiquing economics II: Pete Lunn and Tim Harford debate whether “the idea that the very foundations of economics are being undermined is absurd.”

The effectiveness of nudging: Mertens et al “found” that choice architecture interventions promote behavior change with a small to medium effect size. Andrew Gelman responds. Three articles in reply argue that most of the pooled effects in Mertens et al. are overestimated and hence unrepresentative, there is no evidence for nudging after correcting for publication bias, and there is no reason to expect large and consistent effects of nudge interventions. Some of the garbage in the meta-analysis led to a correction, although the papers from Brian Wansink remained (more on Wansink below).

Evolutionary theory I: Owen Jones proposes that “… Behavioral Economics, and those who rely on it, are falling behind with respect to new developments in other disciplines that also bear directly on the very same mysteries of human decision-making.”

Evolutionary theory II: Douglas Kenrick and colleagues argue that many of our biases are in fact deeply rational. (My post).

Ergodicity: Ole Peters proposes The ergodicity problem in economics. “[B]y carefully addressing the question of ergodicity, many puzzles besetting the current economic formalism are resolved in a natural and empirically testable way.” See also David Meder and friends’ Ergodicity-breaking reveals time optimal economic behavior in humans. My posts here, here and here.

Humility: In Aren’t we smart, fellow behavioural scientists, I suggest that “As applied behavioural scientists, we need to inject some humility into our assessment of other people’s decisions. … We need to stop making glib assumptions about what other people want and how they can best achieve their objectives.”

Lab experiments 1: Ken Binmore and Avner Shaked urge experimentalists to “join the rest of the scientific community in adopting a more skeptical attitude when far-reaching claims about human behavior are extrapolated from very slender data”. Fehr and Schmidt respond, as do Eckel and Gintis. Binmore and Shaked wrote a rejoinder.

Lab experiments 2: Steven Levitt and John List note that, while economic models can benefit from incorporating insights from psychology, “behavior in the lab might be a poor guide to real-world behavior.” (pdf).

Lab experiments 3: Steven Levitt and John List suggest that caution is required when attempting to generalise lab results out of sample.

Many co-authors: Emerging from the Francesca Gino frauds (see below) was the Many Co-authors project. For all studies in which Gino was involved, was Gino involved in data collection? The truly underwhelming element of this project is how rarely data has been made publicly available. Further, it once again highlights that shenanigans by people like Gino are only the tip of the iceberg. Here’s one outcome, a retraction of Don’t stop believing: Rituals improve performance by decreasing anxiety for which Gino was a co-author but not involved in data collection for most of the studies. Missing data and questionable data management all round. It’s best to retain the default of disbelief.

Megastudies: Do megastudies improve the impact of applied behavioural science? Katherine Milkman and friends argue so. My initial take and a later reflection suggest there are trade-offs and problems in execution.

Preferences: Gerardo Infante, Guilhem Lecouteux and Robert Sugden argue that Behavioural welfare economics does not model human psychology as it really is, but rather as “faulty Econs” (pdf). Daniel Hausman responds. Infante and friends provide a rejoinder (working paper pdf).

Pre-registration: Protzko and friends argue that rigour-enhancing practices such as confirmatory tests, large sample sizes, preregistration and methodological transparency increase replication rates. The problem: they didn’t preregister their own analysis. Jessica Hullman discusses here and here. My two cents. Andrew Gelman provides a nice summary following the retraction.

The need for theory I: David Levine and Jie Zheng propose that (pdf) Economic theory makes strong predictions about many situations and is generally quite accurate in predicting behavior in the laboratory. “In situations where the theory is thought to fail, the failure is in the application of theory rather than the theory failing to explain the evidence.”

The need for theory II: Michael Muthukrishna and Joseph Henrich argue that the replication crisis in the psychological sciences is a problem of lack of theory.

Replication: The Open Science Collaboration found that Thirty-six percent of psychology replications had significant results (pdf). Effect sizes were halved in magnitude. Social psychology fares particularly poorly.

Self criticism: Ariel Rubinstein notes that “[f]or Behavioral Economics to be a revolutionary program of research rather than a passing episode, it must become more open-minded and much more critical of itself.”

Too many biases: I argue that instead of building a messier and messier picture of human behavior, we need a new model.

WEIRD people: Joseph Henrich, Steven Heine and Ara Norenzayan propose that “we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity.” But see Cremieux on weirdness and two papers in response (1, 2).

3. Counterpoints to famous biases, effects and stories

The backfire effect: Daniel Engber reviews the evidence. I first saw doubts about the effect on WNYC.

Choice overload: Mark Lepper and Sheena Iyengar’s famous jam study (pdf). A meta-analysis by Benjamin Scheibehenne and friends (pdf) - the mean effect size of changing the number of choices across the studies was virtually zero (although note the Brian Wansink studies in the meta-analysis!). Other studies point to conditions where it might occur, such as Chernev and friends who identify some factors that facilitate choice overload.

Depletion of willpower: Daniel Engber summarises the state of affairs. The meta-analysis referred to by Engber. And the failed replication that triggered the article.

Disfluency: The original N=40 paper (pdf). The N=7000 replication (pdf). Terry Burnham tells the story. (And interestingly, Adam Alter, author of the first paper, suggests that the law of small numbers should be more widely known).

Dishonest bankers: Cohn and colleagues argue that “When their professional identity as bank employees is rendered salient, a significant proportion of them become dishonest”. But look at the data more closely, and primed bankers cheat no more than the student controls. See also Rahwan and friends for a failed replication.

The Florida effect: The poster child for the replication crisis. Ed Yong catalogues the story nicely.

Grit: Daniel Engber reviews Angela Duckworth’s book. I review. (I like the way Angela Duckworth deals with criticism. Also listen to this Econtalk episode.)

Growth mindset: The Wikipedia summary. Scott Alexander’s initial exploration and clarification. A pre-registered study and meta-analysis both showing a tiny but apparently real effect. A more recent meta-analysis concludes that “Across all studies, we observed a small overall effect … which was nonsignificant after correcting for potential publication bias. … We conclude that apparent effects of growth mindset interventions on academic achievement are likely attributable to inadequate study design, reporting flaws, and bias.”

The hot hand illusion: The original Thomas Gilovich, Robert Vallone and Amos Tversky paper arguing people are seeing a hot hand in basketball when none exists. Work by Joshua Miller and Adam Sanjurjo (working paper pdf) shows the original argument was based on a statistical mistake. The hot hand does exist in basketball. (Although I will say that there is plenty of evidence of people seeing patterns where they don’t exist.) ESPN explores. My post here.

Hungry judges: Shai Danziger and friends find that favourable rulings by Israeli parole boards plunge in the lead up to meal breaks (from 65% to near 0). Andreas Glockner suggests this might be a statistical artefact. Keren Weinshall-Margela and John Shapard point out that the hearing order is not random (Danziger and friends respond). And Daniel Lakens suggests we should dismiss the finding as simply being impossible. My post here. A similar analysis of judges during Ramadan (working paper pdf) finds the opposite effect - they are more lenient when hungry.

Hyperbolic discounting: Ariel Rubinstein points out that (pdf) “the same type of evidence, which rejects the standard constant discount utility functions, can just as easily reject hyperbolic discounting as well.”

Illusion of control: Francesca Gino, Zachariah Sharek and Don Moore note that illusion of control experimental results can be statistical artefacts (pdf). “[B]y focusing on situations marked by low control, prior research has created the illusion that people systematically overestimate their level of control.” My post here.

Loss aversion I: David Gal and Derek Rucker claim that (working paper) “current evidence does not support that losses, on balance, tend to be any more impactful than gains.” E. Tory Higgins and Nira Liberman respond, as do Itamar Simonson and Ran Kivetz. Gal and Rucker rejoinder (working paper pdf). My post here. Mrkva and friends also add to the debate.

Loss aversion II: Eldad Yechiam makes a related argument in Acceptable losses: the debatable origins of loss aversion (pdf). My post here. Also see Scott Alexander.

Money priming: Doug Rohrer, Harold Pashler and Christine Harris find that subtle reminders of money don’t change people’s political views (pdf). Kathleen Vohs fights back (pdf). Miguel Vadillo, Tom Hardwicke and David R. Shanks respond. Analysis of the broader literature on money priming suggests, among other things, massive publication bias.

Moral reminders: The original (N = 229) paper co-authored by Nina Mazar, On Amir and Dan Ariely (pdf). The (N=5,786) multi-lab replication by Verschuere and friends: “This small effect was numerically in the opposite direction of the original study.” More recently, an investigation into the data provenance has led to an Expression of Concern. Relatedly, here and here are posts analysing the “shredders” used in some of Ariely’s honesty experiments.

Organ donation: Does Austria have a 99.94% organ donation rate because of the design of their driver’s licence application? No.

Overconfidence: Don Moore and Paul Healy address the many concepts tangled up in the word “overconfidence”” (pdf). [My post]/overconfident-about-overconfidence/).

Power pose: Jesse Singal on Dana Carney’s shift from author of the classic power pose paper (pdf) to skeptic. Carney’s posted a document about her shift on her website.

Priming mating motives: Shanks and friends on Romance, risk, and replication: Can consumer choices and risk-taking be primed by mating motives? (pdf): A failed replication, plus “a meta-analysis of this literature reveals strong evidence of either publication bias or p-hacking.” (I have cited some of these studies approvingly in published work - a mistake.)

Prospect theory: The prospect theory model, the centrepiece of behavioural economics, has us as loss averse and risk seeking when facing losses, and risk averse when considering gains. Ryan Oprea proposes that most of the evidence underlying theories of risk, such as prospect theory, actually reflect mistakes under complexity.

Safety signs kill motorists: Hall and Madsen proposed that dynamic signs that reported Texas road fatalities - “1669 deaths this year on Texas roads” - caused more accidents and fatalities. I argue that we shouldn’t take too much from this single paper.

Scarcity: My review of the book. Reanalysis of the original scarcity paper (pdf) without dichotomising income eliminated the effect. The original authors managed to resurrect the effect (pdf) by combining the data from three experiments, but once you are at this point, you have well and truly entered the garden of forking paths. Leandro Carvalho and friends found that “participants surveyed before and after payday performed similarly on a number of cognitive function tasks.” Then, in a replication of scarcity papers by O’Donnell and friends: “Of the 20 studies that were significant in the original, four of our replication efforts yielded significant results.”

Signing at the top, part I: Lisa Shu and friends report in PNAS that “signing before—rather than after—the opportunity to cheat makes ethics salient when they are needed most and significantly reduces dishonesty.” Ariella Kristal, Ashley Whillans and the authors of the original paper report a failed replication. A discussion of what this means in Scientific American. That, of course, is only the beginning of the story (see the fraud story below).

4. Fraud and misconduct

The Cornell Food and Brand Lab’s catalogue of eating biases (led by Brian Wansink): Jesse Singal catalogues the events. Stephanie Lee’s reviews emails from the lab. Corrections and retractions are flowing. It’s fair to say that we shouldn’t place any weight on results out of that lab. (Although somewhat amazingly, Wansink’s experiment with a bottomless soup bowl replicated! I didn’t believe the original experiment ever existed - and am still doubtful that it did.)

Diederik Stapel: For a long-time, the most salient fraud in social science. The NYT tells the story. My favourite (now retracted) study of his was on how trash-filled environments make people racist. For a long time I thought of Stapel as an extreme but rare case of fraud. I now believe fraud is common, but most people don’t leave such a trail.

Francesca Gino: In a series of four posts (1, 2, 3, 4), the Data Colada team document a series of frauds in Francesca Gino’s work. Failing to recall Barbara Streisand’s experience, Gino sued the Data Colada team. The lawsuit was later dismissed (although as at the time of writing, Gino’s claim against Harvard remains ongoing). Fortunately, the Harvard investigation was made public as a result of the court proceedings, allowing even more analysis by the Data Colada team into how the fraud was perpetrated.

Signing at the top, part II: The field trial data from the signing at the top study (noted above) was completely made up. This led to the paper being retracted and an investigation into Ariely (that ultimately reached no adverse findings). That, of course, was only one of two frauds in this paper. The other, also uncovered by the Data Colada team, was that the data in experiment 1 had been manipulated. Absent the manipulation, there was no effect.

5. Applications of behavioural economics (and nudging)

Government failure I: In Homo economicus or homo paleas?, John Cochrane states that “The case for the free market is not that each individual’s choices are perfect. The case for the free market is long and sorry experience that government bureaucracies are pretty awful at making choices for people.” Noah Smith responds.

Government failure II: Ted Gayer writes that “the main failure of rationality is not with the energy-using consumers and firms, but instead the main failure of rationality is with the regulators themselves.” Two related papers by Gayer and W. Kip Viscusi are Overriding Consumer Preferences With Energy Regulations (pdf) and Behavioral Public Choice: The Behavioral Paradox of Government Policy (pdf)

Implementation: DellaVigna, Kim and Linos find that a nudge trial with a negative result is almost as likely to be implemented as a positive result.

A manifesto for applying behavioural science: Michael Hallsworth writes a manifesto for applying behavioural science (longer and ungated BIT version here). A few observations from me.

More than nudging I: Reuben Finighan looks beyond nudging (pdf), stating that “Policymakers often mistakenly see behavioural policy as synonymous with”nudging”. Yet nudges are only one part of the value of the behavioural revolution—and not even the lion’s share”

More than nudging II: George Loewenstein and Nick Chater put nudges in perspective, writing that “This paper aims to remind policy-makers that behavioural economics can influence policy in a variety of ways, of which nudges are the most prominent but not necessarily the most powerful.” Richard Thaler responds. Chater and Loewenstein later took this critique further, arguing that the belief that society’s problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates, is a mistake.

Paternalism: Robert Sugden writes that (pdf) “The claim that the paternalist is merely implementing what the individual would have chosen for herself under ideal conditions is a common theme in paternalistic arguments, but should always be viewed with scepticism.” Also see Sugden’s Do people really want to be nudged towards healthy lifestyles?, Sunstein’s response (pdf) and Sugden’s rejoinder.

Policy failure I: Philip Booth notes that “We seem to have gone … to a situation where we have regulators who use economics 101 supplemented with behavioural economics to try to bring perfection to markets that simply cannot be perfected and perhaps cannot be improved.”

Policy failure II: Tim Harford writes that “The appeal of a behavioural approach is not that it is more effective but that it is less unpopular.” (Google the article and go through that link if you hit the paywall.)

Policy failure III: George Loewenstein and Peter Ubel argue that “behavioral economics is being used as a political expedient, allowing policymakers to avoid painful but more effective solutions rooted in traditional economics.”

Policy failure IV: In a Behavioural and Brain Sciences target article, George Loewenstein and Nick Chater argue that focussing on interventions at the individual level is inadvertently preventing systemic change. There are many responses, but I’ll highlight those by Michael Hallsworth, David Gal and Derek Rucker, Cass Sunstein, Richard Thaler and Ralph Hertwig.

6. If you want some background

I know this list is of critiques, but here are a few books I would recommend if you want a basic background.

Daniel Kahneman’s Thinking, Fast and Slow is still the best popular overview of behavioural science. However, it is not standing the test of time particularly well. Here is a fantastic analysis of the priming chapter, and Kahneman’s response to that review in the comments. A review of the estimated replicability of all the chapters is similarly damming. It’s unfortunate that something better hasn’t yet emerged. Just pair it with this reading list!

Erik Angner’s A Course in Behavioral Economics is a good and readable academic presentation of the core principles of behavioural economics.

Cass Sunstein and Richard Thaler’s Nudge: The Final Edition is not my favourite book, but it’s a useful to understand the mindset of many nudge proponents.

Richard Thaler’s Misbehaving is a pretty good (although very US-centric) history of behavioural economics.

Michael Lewis’s The Undoing Project is an accessible overview of Kahneman and Tversky’s work.

Michael Hallsworth and Elspeth Kirkman’s Behavioral Insights is a solid book on translating behavioural science into applied public policy.

1. The illusion of evidence-based nudges: The evidence from nudge trials doesn’t affect whether the nudge is adopted.

2. Subject notes on behavioural economics: My teaching notes for my undergraduate behavioural economics class.

3. A comment on the manifesto for behavioural science: I pull apart three proposals from Michael Hallsworth’s A manifesto for applying behavioural science.

4. The human benchmark is typically unimpressive: It often doesn’t take much to beat the human.

5. What we learn when we test everything: My take on megastudies. You can watch a video of the talk this post was developed for here.

6. Human-AI collaboration: is it better when the human is asleep at the wheel?: Some people are concerned we don’t pay attention when we have a competent AI. I argue that might be a good thing.

7. Books I read in 2024: My reading for the past year, noting a couple of favourites.

As always, comments and feedback are welcome.

In future, I plan to send some full posts via this newsletter rather than the sporadic link updates I do now. My former source of most short-term traffic - Twitter (X) - delivers a small fraction of the views it used to, so I’m going to be more direct in getting these posts out there.

Cheers

Jason

1. Books I read in 2023: My book reading trended up for the first time in many years.

2. The preregistration halo: What happens when you state your study is preregistered, but your analysis doesn’t match the preregistration?

3. Bryan Caplan’s The Case Against Education: A Review: I’m generally on board with the signalling model of education.

4. The psychological and genes’ eye view of ergodicity economics: My plan for a presentation on ergodicity economics, questioning some of the psychological and evolutionary underpinnings.

5. Using generative AI as an academic - July 2024 edition: I’m still astounded by how many of my colleagues aren’t using generative AI as a tool every day.

6. Humans 1, Chimps 0: Correcting the Record: Correcting a 2012 post that was off the mark even when I wrote it.

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As always, comments and feedback are welcome.

Cheers

Jason

But I suppose the year’s end is a good time to share what I wrote in that time:

1. In a paper in Science, Jonathan Hall and Joshua Madsen proposed that dynamic signs that reported Texas road fatalities - “1669 deaths this year on Texas roads” - caused more accidents and fatalities. I don’t believe it.

2. The best books I read in 2022. A bit late, given I’ll write a “best books I read in 2023” post in a couple of weeks. However, since my reading tends not to involve many recent releases - the best books I read in the year, not the best books of the year - the list doesn’t age too poorly.

3. Some notes on how I use large language models as an academic. This is already becoming outdated as generative AI increasingly supports my workflow.

4. A review of John List’s The Voltage Effect. Lots of interesting ideas, but I’m not sure many of the arguments hold.

5. A fact check on the opening of an Adam Grant article in Behavioral Scientist. Unlike the opening claim, there is little evidence that experts are worse teachers.

6. A brief grumble that an analysis of a single paper on how behavioural science could inform policy is being used to claim that “Behavioral science policy recommendations early in the pandemic were LARGELY CORRECT”.

As always, comments and feedback welcome.

Cheers

Jason

My major output for the last couple of months was in Works in Progress. Back in 2015 I gave a speech lamenting the accumulation of biases in behavioural economics and the deficit of work developing a unifying model. I wanted to have a second stab at the topic, and this article on Works in Progress is it.

Otherwise, posts on Jason Collins blog in June and July were:

1. A critique of the evidence that I used in that 2015 speech. I did not reference one experiment that I would reference today (nor that I referenced in the Works in Progress article).

2. Some notes on the Works in Progress article, including links to other academic articles that call for the need for more theory in the behavioural sciences.

3. I also released a new set of course notes for a subject I teach on Consumer Financial Decision Making.

As always, comments and feedback welcome.

Cheers

Jason

A quite stretch for posting on Jason Collins blog, but posts in April and May were:

1. Explaining base rate neglect: Over the last couple of months I delivered a series of lunchtime seminars at a wealth manager. I botched my explanation of base rate neglect, so wrote this post as a follow up.

2. Megastudy scepticism: Late last year Katherine Milkman and friends “introduced” us to the megastudy. It landed in Nature with a fair degree of fanfare. My take: a lot to like but “Even in a world of megastudies, we are still in a world of poor theory, questionable generalisability and inadequate statistical power.”

Teaching is over for the semester, so you’ll see an increased flow of posts over the coming month or two.

As always, comments and feedback welcome.

Cheers

Jason

A quieter month on the posting front that last. On deck for March were:

1. A collection of podcast episodes in which I have appeared, triggered by my discussion with Phil Agnew on the Nudge podcast. Other episodes include 42courses, Todd Nief, A Bunch of BS and Rationally Speaking.

2. An update of an old post about an experiment examining the honesty of bankers, with new material on a failed replication. I was underwhelmed by the original authors’ response to the replication: conduct a low sample size study of a noisy phenomena, overhype the result and then complain about a replication because everyone has heard about the overhyped result.

3. A bunch of links to articles and ideas worth a read.

As always, comments and feedback welcome.

Cheers

Jason

This newsletter has just passed 1,000 subscribers, so thanks for signing up to stay in the loop.

February’s posts on Jason Collins blog were:

1. The academic experiment: Some thoughts on my plunge into academia.

2. How big is the effect of a nudge?: I examine a recent meta-analysis of the size of nudges. Is an average of this type even meaningful?

3. Replicating scarcity: I report on an (awesome) exercise to replicate a bunch of experiments relating to “scarcity” The replication exercise included one result I have long considered to be one of the most unreliable findings in the behavioural sciences.

4. The outsider to the narrow-minded profession: There is no shortage of outside critiques of economics coupled with stories that economists are too narrow-minded to listen to them. I dredge up some classic Paul Krugman to show that for at least one of those critiques the story is “an object lesson in journalistic gullibility”.

As always, comments and feedback welcome.

Cheers

Jason

Welcome to my new email newsletter.

You are receiving this because you previously signed up to receive email notifications for new posts on Jason Collins blog.

Most of you signed up when I used Wordpress, receiving a copy of each post by email as soon as it was published (and yes, it’s been a while). I’ve since moved blogging platforms and don’t have an automated system to send you the posts. As a result, I am testing a substitute of sending a monthly newsletter with links to the last month’s posts.

If you’d rather get immediate notification of new posts, subscribe to the feed through a good feedreader (I use Feedly) or follow me on Twitter.

So, here are January’s posts:

1. Best books I read in 2021: I’ve also listed all books I read during the year.

2. A default of disbelief: We should be treating many published results in behavioural science as exploratory or as untested hypotheses. Start from a position near “unlikely to be true”, and update to a stronger belief in the presence of replications or other supporting evidence.

3. The 1/N portfolio versus the optimal strategy: Does a simple heuristic outperform?: Looking deeper into a famous story about Harry Markowitz’s failure to use his own theories when he invested.

4. A critical behavioural economics and behavioural science reading list: First published in 2017, I’ve updated with more recent books and articles.

As always, comments welcome.

Cheers

Jason

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