Hello, world!

There’s simply no escaping talk of AI these days, and for a good reason. The ongoing growth in AI development and adoption represents perhaps the most profound technological shift of our time. AI permeates every aspect of society in ways that few appreciate, fundamentally changing our environments and how we interact with them and each other.

AI is both a product of our behaviours, and a shaper of our behaviours. And thus, it is in my humble opinion that we should spend time thinking about AI from a behavioural science (BS) perspective.

But for our first article, we will spend some time clarifying what these terms mean, because:

1. AI is not one thing.

2. BS is not one thing either.

3. Few know enough about both to appreciate how they intersect.

Artificial Intelligence

Peer under the hood of various AI tools, and you find something like one of these (don’t worry, you don’t have to learn/memorise them):

At the most fundamental level, AI is applied math.

But so is physics, and economics, and Dogecoin. What distinguishes AI from other applications of math is its form and function. Here’s one definition of AI:

Simply put, AI is applying math using computers (the form) to solve complex problems (the function). The applications are broad, ranging from building Large Language Models (LLMs), beating world champions at Go and Starcraft, detecting and treating cancer, predicting protein folding, and permitting weed obliterating death rays.

GOFAI vs. ML

There are two ways to programme AI—rule-based and machine learning (ML).

Rule-based AI, also known as good ole’ fashion’d AI (GOFAI), involves human-coded IF-THEN rules, wherein the AI programmer explicitly tells the computer how to make decisions. Suppose your grandma has mild dementia and frequently forgets to turn off the stove. You could install a stove with a GOFAI safety shutoff feature for her—IF stove temperature exceeds X, or IF liquid boils over and makes contact with stove panel, THEN shut off stove.

GOFAI can be applied to tasks of far greater complexity, epitomised by Deep Blue’s triumph over Garry Kasparov in classical chess, and Watson’s triumph over Brad Rutter and Ken Jennings in Jeopardy!

However, GOFAI also performs poorly in unpredictable, uncertain, and indeterminate situations typical of most real-world problems. Even within its trained domain, GOFAI can be fragile when unexpected situations arise.

In contrast, ML involves providing computer with some data and a mathematical framework, then letting it uncover patterns without hard-coded rules.1 Unlike GOFAI, ML produces robust learning under complexity. When ML products fail, they often do so more gracefully (which sometimes make it harder to actually spot failures). ML can also learn high dimensional structures that are difficult to explicitly programme as rules, such as facial recognition. No matter how many features you embed in a GOFAI rule (IF female, short, bob haircut, oversized round glasses…, THEN Edna Mode), it is exceedingly hard to create a specific enough rule for recognising a particular face. Woe to you if Edna wore contact lenses or grew out her hair.

These days, most people refer to ML when they talk about AI. And so shall we, for the rest of this series.

That said, many AI applications today combine both GOFAI and ML together. For example, the strongest chess engine today, Stockfish, combines a neural network (ML) with a decision tree (GOFAI). This will be a recurring theme this series—things don’t always fit into our nice little boxes.

Types of ML

1. Supervised learning
   Supervised learning is like learning with a teacher. We provide the computer with data and the right answers, and it predicts the right answer in new scenarios. For example, we provide the computer millions of labelled pictures of CT scans with or without tumours, then ask it to predict whether a new image contains a tumour. With enough exemplars, the computer is able to distinguish between tumours and non-tumours.

   

2. Unsupervised learning

   Unsupervised learning is like self-discovery. We provide the computer with data without answers, and it figures out the underlying patterns. For example, we are interested in understanding who is at highest risk of developing a particularly aggressive type of cancer. Because we haven’t identified the relevant risk factors, we provide the computer with all plausible risk factors (genomic, lifestyle, clinical history), which the computer organises into groups (clustering), organised by profile similarities. From there, we map cancer incidence rates onto the groups, and realise one group—specific gene mutation, chronic sleep deprivation, high inflammatory markers—has the highest concentration of individuals who go on to develop that specific cancer.

3. Reinforcement learning
   Reinforcement learning is outcome driven. We establish an environment with set outcomes, and allow the computer (or agent) to interact with the environment. Actions leading to positive outcomes are rewarded, while actions leading to negative outcomes are penalised. Over time, the agent optimises its actions to maximise payoffs (here’s a cute illustration). For example, we are interested in teaching an agent to drive a car. The rules: get from Point A to Point B. Rewarded actions: moving closer to Point B. Penalised actions: drifting out of lane, braking hard, colliding with other objects, causing near misses, disobeying traffic rules. With enough practice, the agent eventually learns to drive.

Many AI applications today use a combination of various ML techniques. For example, LLMs like ChatGPT and Claude are developed following this structure broadly.

Phase 1: Unsupervised/self-supervised learning
The agent is fed massive amounts of English text, which they self-organise (e.g., how are you? goes with I’m fine) and self-teach by hiding parts of the data and trying to predict the correct output (e.g., How are ___? I’m fine.). In doing so, they learn the rules (i.e., grammar) of English.

Phase 2: Supervised fine tuning
A human programmer then provides curated examples of instruction/responses that they want the AI to mimic.

Instruction: Explain like I'm five - what is AI?
Response: AI is a computer program that learns patterns...

The AI learns from these examples the appropriate responses under specific prompts.

Phase 3: Reinforcement learning from human feedback
The programmer then scores the LLM's output on its quality. Good scores are rewarded, thus increasing similar future outputs. Bad scores are penalised, thus decreasing them.

Properties of AI

To segue to the next section on the BSes, let us return to our AI definition:

As you may have noticed, the concept of intelligence (specifically of the human variety) is central in the development of AI. An early goal of AI development was to pass the Turing Test—to build AI that exhibits intelligent behaviour indistinguishable from a human. Beyond function, AI form is also deeply inspired by the human brain and learning processes. The class of ML techniques that form the foundation of many modern AI advancements are called neural networks (a sub-type of neural network is called a long-short term memory neural network). Reinforcement learning draws directly from learning theory.

Yet, AI, being built on computers, are fundamentally different from humans. Our underlying structures are different, as is how we perceive and interact with the environment, our motivational states, and how we learn. Accordingly, AI can at once far surpass us in certain problem-solving tasks, and fail spectacularly at other basic tasks (Moravec’s paradox).

Ultimately, we should remember that AI comprises an array tools, in particular, a rapidly developing array. As we progress through this series, I hope this primer has impressed upon you the breadth of AI types and use cases, in hope you exercise some restraint when the urge to lump them together arises. And, for those so inclined, here is a good glossary of common AI terminology, some of which I have intentionally omitted mentioning.

The Behavioural Sciences

The BSes are a collection of theories and methods, including some which bear only passing similarity to each other. What they do share however, are a common focus on understanding and changing behaviours.

Which begs the questions:

1. What exactly are behaviours?

2. Behaviours of what exactly?

The questions are seemingly simple, but the answers are less straightforward than one might think. For our purposes, let’s go with the following definitions:

What are behaviours?

Forget science and philosophy. We all behave, and in our everyday lives, we all interact with others who behave. Given the centrality of behaviour in our phenomenological experience, you would think that we have a commonly accepted definition for it. We don’t. But as is often the case, the most parsimonious definition is also the most useful one:

By this definition, we can talk about the behaviours of neutrinos, neurons, newts, or neutron stars. This leads to our second question:

Behaviours of What?

No sane behavioural scientist will lay claim to neutrino and neutron star behaviour. But of neuron and newt behaviour, things become less clear. For example, ethologist Jerry Hogan defines behaviour as the expression of activity in the nervous system. There exists a top tier academic journal called Behavioural and Brain Science.2 Many also (rightfully) believe studying non-human animal behaviour gains us important insights into human behaviour.

To arrive at a common understanding, let’s borrow an example from a previous article on transdisciplinarity. Here’s the problem:

There are many levels (or units, if you will) of analysis by which we can approach this problem. Here are some possible answers:

• Molecular (aka Omics) Level
  Example: Track small-molecule metabolites to identify distinct diabetes sub-types and tailor medication precisely for optimal treatment response.

• Hormonal & Molecular Signalling Level
  Example: Develop drugs that mimic natural hormones that improve insulin secretion and suppress insulin resistance.

• Cellular & Subcellular Level
  Example: Develop small molecule drugs that fix mitochondrial dysfunction to restore basic cellular insulin sensitivity.

• Tissue & Organ Level
  Example: Perform bariatric surgery to reduce calorie intake and induce hormonal changes in the gut that increase insulin production and sensitivity

• Individual Human Level
  Example: Prescribe diet, physical activity, and sleep modifications to improve glucose control and decrease weight.

• Population/Systems Level
  Example: Re-balance the economics of food (e.g., introduce a sugar tax and/or subsidies for whole/minimally-processed foods).

For our purposes, we can think of the BSes as situated within the last two levels. Said differently,

These phenomena include things like culture, networks, and institutions. There are some important implications in how we defined BSes. Let’s unpack them.

1. Who’s invited to the party?

By our definition, the behaviours of hormones, neurons, and organs, while important, are not the units of analysis the BSes are concerned with (sorry, neuroscience!3). Neither is the behaviour of newts (sorry, ethology!4).

Rather, the BSes seek to understand and influence what whole individuals, and groups of individuals, do. For some of you, our definition may feel uncomfortably broad, and rightfully so. There are several core disciplines typically associated with the BSes, and we can find renowned PhD programmes in BSes sited within public health, business, and social sciences schools.5 But our definition can also include other disciplines that might chafe at being labelled as BS.

Here’s a non-exhaustive list of disciplines that can be included by our definition:6

2. Who’s friends with who?

Folks don’t always get along with others at a party. Particularly when they think they have very important things to say. Doubly so when they aren’t particularly interested in what others have to say. Here are three sources of tension:

1. Methodological differences
   Different disciplines employ different methods to understand behaviours. Psychology relies greatly on highly controlled experiments and questionnaires. Anthropology uses immersive ethnographic observations. Engineering uses… well, whatever it is engineering uses. Increasingly, we are seeing interdisciplinary teams using diverse methods to understand and change behaviours, but such teams are still very much the exception rather than the norm.

2. Conceptual differences
   Empiricism vs. rationalism. Reductionism vs. holism. Positivism vs. constructivism. Universalism vs. contextualism. Idiographic vs. nomothetic. Stochastic vs. deterministic. We can go on. Different disciplines apply different lenses to the same phenomena. Beyond doing things differently (methodological differences), they also see things differently. Sometimes (oftentimes?), even within the same discipline, seemingly intractable differences can emerge.

3. Differences in units of analysis
   There’s a natural tension between analysing behaviour of individual humans vs. groups of humans. What’s true for the individual is not necessarily true for the group. What’s good for group is not necessarily good for the individual. Within the BSes, there has been recent calls toward shifting the focus of policy action away from the individual (i-frame) towards the system (s-frame).7 And policy action targeting individuals vs. systems can look quite different from one another.

3. Downstream phenomena are valid objects of enquiry… including AI?

Our behaviours often produce products. These products are of interest to the BSes because

1. They help us understand the behaviours that produce them.
   We use metrics such as heart rate and VO₂ max to understand physical activity. Traffic flow and density maps help us understand how infrastructure facilitates or constrains movement. In the TV series Adolescence, Detective Inspector Bascombe’s son gives him a tutorial on the link between emojis and incel culture.

2. We interact with these products, which in turn change our behaviours.
   Some runners treat increasing VO₂ max as the end goal of running. We choose an alternate route when our map shows the usual travel path is congested. Sharing, commenting, and interacting with football memes further perpetuates their message.

   

AI, as a product of our behaviours, ticks both boxes.

Regarding the latter, individuals are increasingly using LLM chatbots for all sorts of reasons, including to complete their assignments, compose chart topping music, and as psychological support and girlfriends. Emerging evidence suggests they do so at the expense of critical thinking skills, diversity, and mental health. At the same time, your favourite social media platform is using AI to get you to doomscroll forever.

Regarding the former, on a fundamental level, as we build AI to simulate human learning and problem-solving, we in the process learn more about ourselves and our own behaviours, both in where AI behaviour is similar and different from ours.

Concluding Remarks

AI is not one thing.

BS is not one thing.

In fact, both are many different things which are sometimes at odds with each other. That makes it rather challenging to talk about how they relate to each other coherently. Fortunately, the folks over at the Behavioural Insights Team have put together a framework on how AI and the BSes intersect, organised around the 4As of augment, adopt, align, and adapt. In our subsequent articles, we will be discussing and evaluating this framework. Stay tuned.

After publishing the previous article on the matching law, an idea struck me—why not reach out to the experts? You know, the folks who actually worked extensively on the matching law and are privy to its theoretical nuances and conceptual underpinnings. What did my article get right/wrong? Would they find my lack of understanding disturbing? What additional insights should I (and you) be cognizant of?

First, for those who have yet to read the matching law article, here it is:

And now, the experts.

William M. Baum

Billy Baum was a key early contributor to matching theory, and his 1974 article on the generalised matching law has been cited over 2,000 times1.

Here are excerpts from our exchange, reproduced with his permission (bolding mine):

Thank you for sending the article. It makes the basic concepts clear, particularly as to application. Relative measures are key. My research has gone far beyond the original 1961 law. I particularly avoid the term "value", because it invokes folk psychology, with its notions about free will and autonomy.

When asked how we should characterise what drives matching without reference to “value”, Baum responded

The way I think about the matching law is that the more general relation is the Law of Allocation (aka the generalized matching law), which states that activities of an organism compete with one another for time according to their competitive weight, which depends upon induction—the degree to which an activity is induced by environmental events, particularly those with evolutionary significance and their proxies. Operant activities are induced by their consequences.

It appeared to me “competitive weight” and “value” were merely describing the same mechanism in different words. I prodded further, to which Baum replied

I avoid "value" because of its mentalistic connotations. Competitive weight fits with the idea that activities compete for time, and competitive weight depends on induction. In fact, it forms the basis for a mathematical formulation [shared in the paper linked below].

As Baum explains, activities compete for our time (and attention and effort). The competitive weight of each activity determines if an activity can “win” a share of our time, and if so, by how much. He calls this the Law of Allocation.

This competitive weight is driven by environmental events of evolutionary significance and their proxies (Law of Induction). These events induce the relative rates of activity.

These two Laws, alongside a third Law of Covariance are laid out in this article, kindly shared by Baum.

2026 Behavior Process And Evolution In Mmv

886KB ∙ PDF file

Download

Download

You can also read Baum’s book Introduction to Behavior: An Evolutionary Perspective for a deeper foray. Finally, here’s a video for those who prefer audio-visual information:

John E. R. Staddon

John Staddon was another key early contributor to the matching law, being the first to propose the aforementioned generalised matching law.

Staddon believed that matching should not be taken at face value. He introduces the idea of maximising, a concept familiar to those trained in economics. Individuals allocate behaviour across activity choices in a way that maximises the overall rate of payoffs.

A further distinction can be made between momentary (or local) maximisation and overall (or global) maximisation. Momentary maximisation can be understood through a hill climbing analogy:

Imagine a blind man trying to get to the top of a mountain. Their decision rule is simple—if the next step forward slopes up, take that step; if it slopes down or is level, try another way.

In other words, always pick the best immediately available option.

In contrast, overall maximisation involves taking into account distal contextual factors, analogous to a hill climber with unimpaired vision who is able to see the the lay of the land and orients towards the peak in the distance, even if the next step forward slopes down. Overall maximisation can produce scenarios such as allocating responses to activities that produce little to no immediate payoff, but a large payoff in the future (delay discounting).

Which strategy do we employ?

As Staddon writes in his book Adaptive Behaviour and Learning (a more recent edition can be found here):

Animals seem to take each choice as it comes and respond according to a relatively simple decision rule, appropriate to the particular situation. But in every well-studied situation, the decision rule seems sensitively attuned to global consequences2. Animals do hill-climb, in the sense that they evaluate each choice according to a simple rule that embodies what they know of the probable payoff for each choice. But their rules take into account more than the immediate consequences of a choice.

Essentially, we tend to employ momentary maximisation strategies, but also seem somewhat attuned to the broader context. This leads to a curious bias: we tend to overallocate behaviours (or overmatch) to activities with higher payoffs, and underallocate behaviours (or undermatch) to activities with lower payoffs. As it turns out, this bias tends to improve overall maximisation.

And that’s it from the experts!

One last thing:

Previous attempts to counsel and retrain the nurses have been futile.

Yes, we know how to document and brief patients, they say tiredly. Yes, we know how important it is to do both right. But can’t you just see how busy we are?

Meanwhile, patient satisfaction scores are dropping, clinical errors arise from improper documentation, and readmission rates are creeping up. Grey Sloan’s reputation is at stake.

While pondering how to resolve this quagmire, she bumps into Rei, head of behavioural health, at the water cooler and shares her woes with Rei. After thinking for a while, Rei says, you know, this might be a good situation to apply the matching law.

The Matching Law

If you’ve never met an academic, you might imagine them to be a placid, amicable lot who gathers and discusses ideas with other academics over tea and cakes. Not quite. The behavioural sciences, which span multiple disciplines and sub-disciplines, represent particularly fertile grounds for febrile holy wars to erupt1.

To suggest a law of behaviour is a daring endeavour. Yet here we have one (at least in name), perhaps a relic from earlier days, when the behavioural sciences were not as fragmented as they are today.

The matching law, first proposed by psychologist Richard Herrnstein in 1961, describes how organisms allocate responses among various choice alternatives. To gain insight into human nature, the OGs in those days studied rats and pigeons. The cool way to study them was to get them to press keys under various circumstances. The goal was producing scientific knowledge across 3 levels: description, prediction, and control (we say influence nowadays). First, they described how animals would press keys under certain circumstances. Then, they predicted how animals would press keys under other circumstances. Finally, they influenced the animals’ very key pressing.

Herrnstein had set up pigeons in boxes containing two keys which distributed grain occasionally when pecked. Importantly, pecking one key distributed grain more frequently than pecking the other.

Imagine yourself as one of these pigeons. How would you allocate your responses?

Herrnstein observed pigeons pecked more frequently on the key that distributed grain more frequently, though not always exclusively, as some of you might have guessed. Instead, key pecks corresponded with the relative rates of food distribution associated with each lever. That is, responding matched reinforcement.

Here’s a graph detailing distribution of responses by Herrnstein’s pigeons on Key A relative to rate of reinforcement, which turns out, approximates a Y = X function2. If Key A distributed food 70% of the food, it received approximately 70% of the pecks. If Key A distributed food exclusively, it was pecked exclusively. If Keys A and B distributed food equally, they were pecked equally.

The matching law can be represented mathematically as an equation (or one of its variants), but it would serve us better instead to try and grasp the intuition behind it. (Don’t worry nerds, the equations are all here.) If Herrnstein’s pigeons befuddle you, remember this:

Only a Sith Deals in Absolutes

Imagine your phone only has one app, Substack, which primarily hosts long-form essays. Your phone use would be allocated solely to Substack’s erudite offerings. On Monday, the biophysics of paradigm change. On Tuesday, a contemplation of fame.

One day, you discover to your chagrin a new app, YouTube, on your phone, which must have been packaged with the latest software update. You mean to delete it, but out of curiosity, you decide to check it out first. All at once, a world of DeepMind documentaries (or if you’re feeling frivolous, ants turning blue) opens up to you. At the end of the week, your phone usage report comes in: 75% YouTube, 25% Substack (not 0%! You still very much enjoy the droll musings of Scott Alexander).

The next phone update brings yet another app, TikTok, and with it, short-form videos of cats chasing their tails, toddlers eating lemons, and hot takes from impossibly hot folks. Wow. Soon, your phone usage report settles around: 60% TikTok, 30% YouTube, 10% Substack.

The matching law is an account of how we allocate behaviours among choice options3.

I repeat, among choice options.

This is the key insight! How much is X worth? and How much effort should I put into obtaining X? should always be answered with It depends! What else is there? How we value options and allocate behaviours always depends on the availability and value of other options.

In the absence of any other apps, Substack represents all the value of your phone and draws all your time and attention. As other apps are made available, the relative value of Substack drops, and so does relative responding. In our case, we can infer from the phone usage that TikTok is valued approximately twice as much as YouTube, and 6 times as much as Substack.

Only a Sith deals in absolutes. For the rest of us, value is always relative. This is a point that seems obvious when stated, but is often underappreciated by those trying to understand and change behaviour.

Description to Prediction

Initial research by Herrnstein and others sought to confirm and quantify this relationship between choice and value of available options—that is, observe and describe phenomena. The ensuing decades of research in animals (humans and non-humans) across diverse settings has confirmed the orderliness and, dare I say, lawfulness of the matching law.

But scientific ideas live and die by their utility. To be more than a concept of a plan, ideas need to have practical relevance—that is, allow us to predict and influence behaviour.

The matching law allows us to make 2 specific predictions:

1. We can predict behaviours from known values.
   If we know the relative value of choice options, we can predict how behaviours will be allocated. If Lever B disburses thrice as much grain as Lever A, we know that a pigeon will peck on it approximate thrice as often. If completing English and Math homework earns gummy bears and mints respectively, and we know that a child values gummy bears twice as much as mints, we know that the child would work twice as hard on the English homework.

2. We can infer values from observed behaviours.
   Conversely, if we know the distribution of behaviours among choice options, we can infer the relative value of each option. As illustrated earlier, we can infer from the phone usage report that TikTok is valued twice as much as YouTube, and 6 times as much as Substack.

After further conversing with Rei, Farah quickly recognises this predictive utility piece as the key to understanding her nurses’ behaviours. She spends a week collecting observational data of her nurses at work, focusing on quantifying the time they spend engaging in various activities and elucidating any observable reinforcement for each activity. She then summarises the data:

Her findings confirmed that her nurses were under-allocating time to clinical documentation and patient education. These tasks receive low reinforcement rates—there was limited immediate feedback (and feedback in general), and any reinforcement or repercussions for doing these tasks well or poorly were distal and rarely contacted.

On the other hand, they were over-allocating time to indirect patient care/logistics, and communication and coordination. Any work performed was highly visible, resulting in frequent and immediate feedback. These tasks are also typically carried out at the nurses’ station, where nurses tend to congregate (and chat with each other), and social contact rates are high.

All told, nurses were spending disproportionately more time on tasks that yielded the most frequent and immediate reinforcement, which meant clinical documentation gets rushed, and patient education gets abbreviated.

Prediction to Influence

All that’s left to do is to solve the problem.

As noted earlier, the usual go-to methods of counselling and retraining were previously employed to no avail. The matching law-informed Farah now understands why—these methods don’t address the mismatch in relative reinforcement rates that is at the root of the nurses’ sub-optimal time allocation.

Farah devises a plan to rebalance reinforcement in two ways: boost reinforcement rates for under-allocated activities, and moderate reinforcement for over-allocated activities.

Boosting Reinforcement for Under-Allocated Activities

1. Clinical Documentation
   Farah understands that quality documentation produces no immediate signal. The work disappears into a system and the nurse receives nothing back.
   
   She decides to create a feedback signal where none existed. She implements a brief end-of-shift documentation review between the nurse manager and the nursing team to track clinical documentation completion rates and quality. The feedback is proximate (i.e., daily), specific, and credible, and creates an opportunity for social reinforcement for good documentation.

2. Patient Education
   Patient education is chronically under-reinforced because outcomes are distal and weakly linked to patient education.
   
   Again, Farah decides to close a feedback loop that normally never closes. She implements a 24–48-hour post-discharge call programme, where outcomes are briefly fed back to the nurse who provided the education. This provides feedback that validates their efforts (the patient you spent time with yesterday managed their medications and wound care well and is well on her way to recovery) or addresses gaps in their service delivery.

Moderating Reinforcement for Over-Allocated Activities

1. Indirect Patient Care/Logistics
   Completing indirect care and logistical tasks provides immediate feedback and relief from an active pressure. Nurses tend to focus their attention here first, and leave documentation for later.
   
   Farah works with her team to redistribute indirect care and logistical tasks to ancillary and administrative staff to alleviate the burden on her nurses. Separately, she implements an indirect care and logistics triaging flowchart, which helps nurses decide which tasks require their direct, immediate involvement, and which can be delegated or deferred.

2. Communication and Coordination

   This is complex—a competent nurse who communicates and coordinates well is genuinely valuable. But over-allocating time to this draws behaviours away from other clinically important behaviours.

   
   Farah works with her team to design structured handover protocols, setting a discrete time boundary on this task, rather than allowing it to sprawl. The feedback loops she creates for clinical documentation and patient education also signals the importance of those activities and redefines which behaviours earn status in the wards. Farah knows that status is a powerful reinforcer—it just needs to be utilised correctly.

3. Social Congregation at the Nurses’ Station
   Again, Farah knows there’s a delicate balance to be struck here. Social interactions between nurses are important. She does not want them eliminated, but relocated. Her goal is for the richest social reinforcement in the ward to flow towards clinical behaviours, rather than away from them.

   
   This means her nurse managers should deliver praise and recognition at the bedside, or during clinical tasks rather than at the nurses’ station. She also restructures breaks so that social congregation has a designated window during lull periods.

Before implementing these changes, Farah sounds Rei out to hear her thoughts.

These are great! Rei says, And don’t forget your nurse managers.

Ah, yes… of course. The nurse managers. Their behaviours conform to the matching law too. And the reinforcement they receive from their superiors is skewed towards scheduling, budgeting, conflict resolution, quality of care, and patient safety. At Grey Sloan, clinical documentation quality and patient education rates are not metrics used to track quality of care, which means, they have received little attention from the nurse managers. In other words, the nurse managers were not negligent, they were matching.

Farah understands now—a systems problem, dressed in a frontline costume. For sustainable change, a reinforcement cascade needs to flow downwards through the entire organisation, from directorial, through managerial, to the frontlines.

She thanks Rei and heads back to her office, where a quieter thought surfaces. Rei did not need to help her. She had a full caseload, a departmental review coming up, and no apparent incentive to spend an hour helping Farah problem solve. She'd done it anyway, but the next time Farah needed her, she couldn't count on goodwill alone.

What does Rei find reinforcing?

Perhaps recognition from leadership, and opportunities to translate theory into scalable implementations. She might appreciate an invitation to be present at Grey Sloan’s leadership meetings, where her knowledge can reach those with the authority to act on it.

Farah smiles.

The matching law, it turns out, is less a solution than a lens. Once you put it on, you can't quite take it off.

For a critical appraisal of this article by matching law experts:

As I’m not too familiar with what goes on in a ward, I sought Claude’s assistance with providing a task breakdown and ideate interventions. I would characterise these items as 70% Claude’s, 30% my input.

At the start of each year, I typically go around making an offer—I guarantee 99% odds of success with your New Year’s resolutions, with the opportunity to earn some money too. Sounds pretty good, right? Here’s the deal1:

Think of an amount of money that you can afford to lose, but would absolutely devastate you to lose it. The worse you’ll feel about losing it, the better. Now send me that amount and I will hold it for you. If you’ve met your New Year’s resolution by the end of the year, I will return you the money, and throw in a bonus 2% on your principal amount as a reward2. If you fail to meet your resolution, I get to keep the money.

Unfortunately, and I can’t figure out why, no one has taken me up on this offer (let me know if you’re interested).

The New Year’s resolution is one of humanity’s grand, enduring traditions. Like clockwork, we begin each year with promises to ourselves of betterment, and resolve to fight the good fight. This year will be the year. Each new start looks promising, until it doesn’t. We say to ourselves, maybe next year, when I have more [insert your noun choice]. Time is a flat circle. One must imagine Sisyphus happy.

Here are some Google search interest trends for diet, gym membership, and save money over the past 5 years:

No prizes for figuring out when those spikes occur. The behavioural economist Katy Milkman calls this the Fresh Start Effect—a burst in motivation for change in association with significant calendar landmarks.

While Selectionist typically focuses on macro-level applications of behavioural science, in honour of the fresh start phenomenon, today’s article will focus more on the individual. The aim of this article is to present practical, actionable suggestions without the fluff. Here goes.

Reviewing My 2025 Resolutions

First of all, some clarification on terms used in this article:

• Values: Guiding beliefs/principles that organise and direct our actions. They are not an endpoint to be reached or attained, but rather an ongoing direction to keep moving towards. Example: I want to be a good father.

• Goals: Outcomes to be reached or attained. Examples: I want to spend more quality time with my children or I want to practice positive parenting.

• Actions: Specific behaviours that lead to attaining those goals. Examples: Spend 30 mins reading to my children every night or model right behaviours when things don’t go our way.

I have had success with my resolutions in the past, and on an ongoing basis. The 2 that bring me the greatest satisfaction are reducing my meat intake to one meal per day (5 years now) and rejecting wedding invitations by default (4 years now). I’ve also had some failures.

Early last year, I set myself 2 goals to be achieved. Spoiler alert: I failed to meet them. By the end of this article, I hope you understand why.

Here’s my objective evaluation of my 2025 resolutions:

1. Don’t use my phone when I’m walking.
   Grade: C
   I was able to adhere to this resolution for a few months, then I wasn’t. I had to respond to work messages on-the-go at times. Once the rule was violated, then came the flood of other non-work-related activities. There was no recovering. That said, compared to 2024, I’m using my phone less when walking, as I’ve a developed a level of self-awareness.

2. Be a better listener.
   Grade: F
   This was a highly-valued goal, but lacking any specificity, an atrocious resolution (we’ll go through why later). I had some idea of what being a better listener meant, but I never got around to practicing those skills consistently, particularly in challenging situations, which was really the implicit goal.

Yikes. As a behavioural scientist, I know I can do much better. Here’s what the science has to offer in that regard.

Setting Better Resolutions

1. Action Thy Goals

I want to lose weight or I want to be a better listener are commendable goals. But it’s sometimes unclear how we can achieve them. Some goals involve a specific attainable outcome (losing 10 kg in 1 year), others are more akin to values, reflecting an ongoing direction (better listening).

Either way, we need a clear understanding of the actions to be performed (consistently!) to attain our goals. To lose weight, we might restrict calorie intake by reducing sugary drinks, going on a low-carb diet, adopting intermittent fasting, or switching to healthier snacks. Or we might increase calorie expenditure by hitting the gym, cycling to work, going on long runs, or picking up a new sport.

Any one or a combination of these actions can help us reach our goal. But many us make resolutions (including my being a better listener resolution) without including well-defined action(s) to be performed. Here are 4 examples of actionable goals, ordered by how well they’re structured:

As a final point, there is a cost-utility trade-off for every behaviour, and each action performed can be viewed as a cost. For an action to be performed consistently, it needs to feel worth it. For an individual whose meals have included a sugary drink for the past 10 years, completely eliminating sugary drinks can represent a prohibitively high cost. Any behaviour change is unlikely to sustain. We can think about how to lower this cost (e.g., gradually reducing sugary drinks or sugar content in drinks3, introducing low-/no-sugar substitutes). Or, we can also think about how to boost the utility.

2. Know Thy Reinforcers

The premise is simple—when the fresh start motivational glow wears off, what will keep you going? 15% concentrated power of will? Without reinforcers, your odds of success are practically 0%.

Here are three aspects to using reinforcers effectively:

First, know thy values. Our values are some of the most powerful reinforcers we have. We don’t seek to lose weight or save money for its own sake. Depending on who you ask, people want to lose weight to gain social acceptance, avoid health complications, or simply to feel better. They want to save money to attain social status, access experiences, or avoid deprivation. Connecting our goals to our values and strengthening that relationship has profound implications for attaining those goals. If your sole reason for a goal is, because all my friends are doing the same, perhaps that’s not the right goal.

Second, find those near-term reinforcers. Distant outcomes don’t function as effective reinforcers. Near-term outcomes do. No one wants to experience a heart attack or a major stroke. We just can’t escape our biological susceptibility to discount those far-removed outcomes. However much I care about the environment and disagree with modern farming practices, these beliefs alone are insufficient to help me stick with my meat-once-a-day practice, particularly at the start. Knowing that being seen as someone who keeps to their word was a powerful reinforcer for me, I told friends and family about this goal. Each meatless meal in their presence spurred me on. I also happen to enjoy many vegetarian food options (even more so 5 years on).

There are endless ways we can structure our near-term reinforcers. Here are some:

1. Use progress charts: Sometimes, progress is not obvious, or linear. For example, gradual weight loss may not be apparent without consistent tracking and it’s common for weight to fluctuate in the opposite direction. Seeing progression towards a goal can function as near-term motivation for many. Additionally rewarding that progression can also be highly effective.

2. Enlist social approval/support: Trace the root of any action, and there’s almost always a social element involved. Recruit your own cheerleaders to support you on your journey. If some actions are better done in the company of others, find a way to make that happen. Some healthy competition can also be great.

3. Create contingent reinforcement: Preferred activities can be used to motivate less preferred activities, if access to those preferred activities is made contingent on doing the less preferred activity (also known as the Premack principle). I have a stack of books on my shelf waiting to be read, but I only allow myself to indulge in a new book after publishing a Substack article.

4. Embed fun: For the beginner runner, running sucks. It might suck less if you’re vibing to Eminem’s Lose Yourself while running, or one of these songs. Or you can join a running club (point 2). Or play Pokémon GO. If you find yourself dreading the activity, you’re not going to see it through.

5. Schedule frequent reinforcement: Achieving our goals is rarely easy, and is often hardest in the beginning. Frequently rewarding that hard work builds initial success, establishing momentum for continued success. Contrive additional reinforcers if you need to. As an activity gets established as routine, you may find that fewer reinforcers are needed to maintain the activity.

Third, reinforcers can also come in the form of avoiding/removing a cost. If you follow competitive sports, you’ll realise that many of the top athletes hate losing more than they enjoy winning (loss aversion). Thus, one option is to create a contingency where you lose something if you fail to meet a stated goal. My opening offer exploits this exact mechanism: it's fundamentally a commitment device leveraging loss aversion (alongside social accountability)3. That said, we don’t particularly enjoy having a sword hanging over our heads, even one we've chosen ourselves. If you do use this approach, balance it with positive reinforcement—hence why my offer includes a 2% bonus for success, not just the threat of loss. Better yet, combine the commitment device with the other reinforcer strategies above.

To sum it up—you know your reinforcers best. Make them work for you.

3. Structure Thy Environment

So you’ve decided to start running 3 times per week, and you’ve recruited a number of reinforcers to aid your cause—you’ve committed to reserving your precious Dubai chocolate as a reward after each run (carefully rationed, of course); you’ve set up a Strava account for all your cheerleaders to track and support your runs; heck, you’ve even taken up my opening offer. What more do you need to do?

Structuring our environment broadly serves 2 functions—they remind us of what action needs performing, and they often also facilitate getting said action done.

For one, there is the matter of timing. When in your busy schedule should you set aside the time to run? Sometimes, the most convenient (e.g., arriving home after work) may not be the most optimal (too tired). Our motivational states are highly sensitive to our environmental conditions. We’ve discussed the Premack principle earlier—perhaps we can slightly tweak our run timing to immediately after work, before the highly motivating event of returning home and unwinding for the day4.

There is also the matter of embedding cues, particularly when we’re up against well-entrenched behavioural patterns. We can set reminders for running days. We can wear our running shoes to work. We can also chain running to another activity that’s already established as routine, such as immediately before your habitual Movie Mondays and Taco Thursdays (Atomic Habits author James Clear calls it habit stacking). We’ve also learnt that simply making a verbal commitment (I will go for my run today after work.) increases our follow-through rates.

There are countless small ways to structure our environments to set ourselves up for success. If you’re stuck, the Internet is often a wonderful resource for getting ideas.

Setting My 2026 Resolutions

Our 3 commandments thus spake, let’s take a look at my 2026 resolutions. You’ll notice that I’ve very publicly announced them.

1. No phone in the evenings between when my children get home and when they sleep

I’m implementing a no phone policy (for myself) for the 1.5–2 hrs I have with my children every evening before their bedtime.

Action:
Put away my phone when I/my children reach home (whichever later) until they are put to bed.

My reinforcers:

1. Spending quality time with my children and wife (near-term reinforcement)

2. Feeling less attached to my phone (value: personal agency)

3. Keeping my word (avoiding a cost: social embarrassment)

Environment structuring:
I’m placing my phone on my piano for this time period. If I urgently need to use my phone, I have to do it at the piano5.

2. Practice 3 mins of mindfulness before/after lunch

I sustained a mindfulness practice for a couple of years some time ago, and benefitted greatly from that experience. Here’s my attempt at getting back into it.

Action:
On weekdays, practice mindful breathing for 3 mins immediately before/after lunch, except when circumstances don’t permit it.

My reinforcers:

1. That reinvigorating feeling of enhanced focus during/after a mindfulness practice (near-term reinforcement)

2. Cultivating a less reactive, more deliberative me (value: personal growth/accomplishment)

3. Keeping my word (avoiding a cost: social embarrassment)

Environment structuring:
I’m stacking my mindfulness practice onto my weekday lunches. Weekday lunches are the most conducive timing for me as I can usually find a quiet space to be alone. I’ve also opted to begin with a highly doable 3-mins practice (which I may increase subsequently).

3. Write 3 publication-ready research manuscripts as first author

The initial goal was to publish 3 research articles as first author. Upon some reflection, I decided to revise it to writing 3 manuscripts, as publishing involve various factors outside my control.

Action:
Set aside Mondays for writing—3 hrs before lunch, and 3 hrs after. Sub goals:

1. end January: first complete draft of manuscript 1 (M1)

2. mid February: first complete draft of M2

3. end February: final complete draft of M1

4. end March: final complete draft of M2

5. end April: first complete draft of M3

6. end June: final complete draft of M3

Why by end June? Because I can under normal conditions, and because by Murphy’s decree, the universe will conspire to screw your plans6.

My reinforcers:

1. Advancing my career (value: personal growth/accomplishment)

2. Gaining social recognition for making important contributions (value: social recognition)

3. Ticking stuff off my to-do list (removing a cost: sense of frustration)

4. Creating a sense of progression (near-term reinforcement)

5. Keeping my word (avoiding a cost: social embarrassment)

Environment structuring:
I’ve blocked out Mondays on my work calendar so I don’t get interrupted unnecessarily. Also, my phone goes on the piano when I’m doing deep work (sorry again, piano).

May your resolutions (and mine) live long and prosper. Remember, if you require an additional boost, my opening offer stands indefinitely.

I recently watched Dear Evan Hansen, a musical that chronicles the life of struggling teenager Evan Hansen, and his central role in the events which unfolded after a schoolmate commits suicide. Later on, it is revealed that Evan had also attempted suicide previously. We also see other characters struggle in their own ways.

I left the theatre feeling a little weird. Dear Evan Hansen was tragic, funny, heartfelt, and unlike many gravity defying musicals that invite you to suspend belief, it feels painfully real. We’re deep in the throes of a global mental health crisis, particularly with our youth in the wake of the COVID-19 pandemic. A recent meta-analysis of 29 studies involving over 80,000 youth worldwide found that 1 in 4 have clinically elevated depression symptoms and 1 in 5 have clinically elevated anxiety symptoms. These are not trivial numbers. And if the numbers feel too distant, the social psychologist Jonathan Haidt and his collaborators have been doing important work in placing faces and stories to those numbers. Here’s the most recent example:

After Babel

Growing up Online Nearly Killed Me

Introduction from Zach Rausch…

Read more

8 months ago · 248 likes · 39 comments · Roxy Longworth and Gay Longworth

A core aspect of this suffering is the thoughts and beliefs that capable, beautiful, resourceful, perfectly normal youths have of themselves. In the musical, Evan holds deeply flawed beliefs about himself—he sees himself as a nobody, a loser, and not enough. In short, he’s just like many of our youth.

Our gift of language allows us to communicate, collaborate, and create on an unprecedented scale, but has also left us vulnerable to a uniquely human kind of suffering—we suffer because of the things we say to ourselves. But how did these thoughts and beliefs come to be? Thinking is incredibly complex, and we don’t have all the answers, but we have gathered some clues along the way that have allowed us to understand the origins of these inner demons, and potentially to alleviate some of this suffering.

Clue 1: Thoughts are triggered by something

Often, we can readily trace the origins of a thought. An orange cat chases its own tail → I NEED TO POST THIS ON TIKTOK!!; no one signs on Evan Hansen’s arm cast → I don’t have any friends.

Pay careful attention and sometimes we can even trace how one thought leads to another. For example: No one signs on Evan’s cast → I don’t have any friends → No one likes me/I am a loser; hears parents arguing downstairs → They’re fighting again → Is it because of me? → I’m a burden to this family → Everyone would be better off without me.

In all these examples, an antecedent stimulus cues the thought (which cues the next thought, which cues the next thought). This is the stimulus-response (S-R) view. In Pavlovian talk, a stimulus elicits a response1.

We can summarise the S-R view as such:

The parsimony of such a view carries a certain appeal. It is also useful in many situations. We can predict what a youth will do next when their phone buzzes. We can understand a teenager’s suffering as we watch them sit alone in the canteen. We can alleviate some of that suffering by recruiting peer buddies to engage them during recess.

However, the S-R view lacks completeness in some critical ways. Most crucially, it doesn’t seem to fully explain how we come to hold certain beliefs—a set of thinking tendencies that are latent (they seem to lurk beneath, waiting to surface), generalised (they surface under various circumstances, typically as follow-on thoughts), and carry emotional weight. It’s one thing to describe how stimuli induce salivation in dogs (and ourselves). It’s quite another to claim our youth hold deep beliefs that everyone would be better off without them because their parents argued.

Clue 2: Thoughts are motivated

Recall the times you laid in bed wrestling with your thoughts, unable to sleep. What kept you awake? Was it about the social life of pharaohs? Unless you’re an Egyptologist, probably not. Rather, they’re likely related to important events in your life.

Thinking is behaving, and all behaviours are motivated2. The evolutionary social scientist David Pinsof wrote about what some of these motivations are:

What we’re pursuing is not good vibes in our heads, but the (often unflattering) things in the world that evolution programmed us to pursue, like sex, status3, yummies, moral superiority, and high-status offspring. Instead of revealing the unflattering things we actually want, we say we’re pursuing happiness, because it sounds better.

We can probably throw in a few more, including predictability, sensory stimulation, avoidance of aversive stimuli, and such. But on the whole, it’s a pretty good list! Thinking is costly. It makes sense to be spending this energy on events that we perceive as important.

Antecedent stimuli gain the ability to evoke thoughts (and actions) by being predictive of such important events. They guide thoughts with respect to these events. Hunger is what makes the bell chime matter. Without hunger, the bell means nothing. The absence of anyone signing Evan’s cast (except another misfit) cues thoughts of social isolation because social connection is important. These thoughts, accompanied by that palpable, sinking feeling of utter loneliness, move the individual towards resolving (or avoiding) social isolation, sometimes through, alcohol, drugs, or other worse means.

In other words, important events motivate thoughts under conditions that predict these events. An updating of our original statement is warranted:

With that, we’ve brought motivational variables into the equation, somewhat. We still haven’t quite addressed how the same antecedent stimulus can trigger wildly different thoughts for different individuals, or how wildly different antecedent stimuli can trigger similar thoughts.

Let’s rewind the clock: Once upon a time, Pavlov’s dog did not salivate to the chiming of bells; our youth did not think about TikTok when they saw an orange cat; nor did Evan Hansen think of himself as a loser. Something happened in the intervening period that established such S-R relationships. Our final clue ties it all together.

Clue 3: Thoughts are learnt

Important events are important because, well, they impact our survival and reproduction. When stimuli present as predictive of these events, we learn to pay attention to these stimuli—Pavlov’s dog becomes attuned to bell chimes because they predict food; Evan Hansen becomes attuned to silence because it predicts rejection. In the presence of these stimuli, insofar as certain behaviours allow us to contact (or avoid) these events, we learn to replicate these behaviours.

Learning, as I’ve previously written about, is just another name for the process of adaptation by an organism to its environment.

We think the way we think because these thoughts serve some adaptive function, or have done so in the past. We call generalised thoughts that serve particularly important adaptive functions beliefs.

We could update our statement as such:

Which just sounds great. The trouble is, accessing an individual’s historical context is not always possible. Even when we manage to do so, it often through error-prone methods (verbal reports) and often lacks granularity. That isn’t to say such information is useless. Even without granular details, just knowing an individual experienced trauma when young goes a long way in helping us understand how they think.

Beyond that, the true value of taking an adaptive view, in my opinion, it allows us to believe that thoughts can be changed, and new thoughts can be learnt, and offers a pathway for such change to occur.

Confronting These Demons

When I was young, we did a science experiment in school which involved gluing together cardboard strips to create a plant maze. We then placed a seed at the bottom of the maze and watched it germinate. As the seedling grew, it had to navigate the maze to reach the light source at the end of the maze. Thus, its shape would conform to the structure of the maze.

If one were to remove the maze after the experiment was over and allow the seedling to continue to grow, one could guide and reshape the stem4, and importantly, direct its future growth trajectory.

In many ways, each individual is like one of those seedlings, shaped by our environments. Our past experiences shape who we are and how we think today. Our present experiences are layered upon that to shape our future self. With that, let’s consider the two broad ways in which we have tried influencing how individuals think, with a focus on alleviating their suffering.

Approach 1: Harnessing the power of language

Probably to the surprise of no one. Just as language can cause suffering, it has tremendous power to also absolve suffering. My favourite example of this is from the psychiatrist Viktor Frankl’s book, Man’s Search for Meaning—An elderly doctor, struggling with the loss of his wife, sought Frankl’s help:

Frankl: “What would have happened, Doctor, if you had died first, and your wife would have had to survive you?”

Doctor: “Oh, for her this would have been terrible; how she would have suffered!”

Frankl: “You see, Doctor, such a suffering has been spared her, and it was you who have spared her this suffering—to be sure, at the price that now you have to survive and mourn her.”

He said no word but shook my hand and calmly left my office. In some way, suffering ceases to be suffering at the moment it finds a meaning, such as the meaning of a sacrifice.

Psychologists, counsellors, and other mental health professionals employ therapeutic conversations to help alleviate suffering. Through these conversations, the professional identifies maladaptive thoughts/beliefs, their triggers (antecedent stimuli) and motivations, and work toward changing these thoughts, the client’s relationship with their thoughts, or fostering new ways of thinking. To give you some idea of how these processes are like, here’s a conversation between the psychologists Kelly Wilson (who is the co-developer of Acceptance and Commitment Therapy) and Diana Hill:

Approach 2: Changing the environment

Most people imagine therapy to be like this—the client sits in a room on a comfy chair, possibly a recliner, and spends an hour or so talking it out with the therapist.

In fact, therapy often involves a great deal of working towards changing an individual’s circumstances. For an individual presenting with depressive symptoms and who has stopped leaving home, a therapist might prescribe spending time outside (behavioural activation). For an individual presenting with trauma or severe anxiety in the presence of particular stimuli/situations, a therapist might work on gradual exposure towards those stimuli/situations (exposure therapy).

The roles of mental health professionals are incredibly important, and becoming increasingly so. But relying solely on them to solve our youth mental health crisis is woefully inadequate for a number of reasons:

1. Mental health professionals mostly work in 1-on-1 settings, occasionally in small group settings. That’s highly resource intensive and there simply aren’t enough of them to go around.

2. Their influence over the individual’s environments is often limited. They may not be able to intervene in school bullying, and cannot control their client’s social media access. They certainly cannot remove their clients from their rough, impoverished neighbourhoods.

3. It’s just a terrible way of solving a systemic problem. Imagine a car assembly line with faulty machines that do a botched job of assembling cars. At the end of the assembly line is a final station that fixes all the prior manufacturing errors before the cars go out for sale.

We can do better.

Here’s a powerful example of what better looks like:

In the 1980s, researchers in Montreal identified 7-year-old boys with disruptive behaviours from low-income neighbourhoods and enrolled them in a 2-year programme social skills programme. The boys were placed in small groups with other boys identified by their teachers as highly prosocial, and learnt skills such as inviting others to play, giving compliments, offering help, reacting to teasing, and emotion regulation. 30 years later, these boys had significant increases in employment income and marriage, and a decrease in social transfers and criminality as compared to boys in the control group. The estimated return on investment was $11 in benefits for every $1 invested.

These boys came from environments where what we label as disruptive behaviours were adaptive. Simply by teaching them what adaptive behaviours in their new environments looked like, the researchers and stakeholders permanently altered the trajectory of these boys for the better.

Concluding Thoughts

We began with the thought.

We then traced the conditions in an individual’s past and present that led to their formation. In all honesty, if it isn’t yet clear, what we discussed about thinking today applies to all other behaviours5. We think what we think and do what we do because these behaviours are related to important events in our past and present environments.

The modern environments our youth find themselves in today resemble that assembly line in many ways. It is imperative (and way past time) that we fix those faulty machines6. The good news is, unlike cars, our struggling youths are not static objects, but dynamic, resourceful, resilient, and highly capable of healing and adapting. Just like for our little seedlings, we just need to shine some light.

First of all, some words of appreciation. The thought that you, most of whom are random internet strangers, think I have anything worth saying humbles me and fills me with gratitude. And so, from the bottom of my heart, thank you.

Aims

This blog was created with the aim of science dissemination. This broad goal is served by 3 guiding principles:

1. Be accessible.

I treat each article as an ELI5 (explain like I’m 5) challenge to make important but inaccessible concepts and tools readily available to the general audience.

This means I mostly avoid jargon (or explain them simply), keep my articles short, and make them contextually relevant to a broad audience using metaphors and real-world examples1.

At the moment, the majority of you readers are similar to me (some of you have even trained and mentored me). I’m delighted to have you. But it also means Selectionist has not reached the breadth of readership I hoped it would. There’s more work to be done on that front.

2. Be science-based.

What science exactly? My training is in behaviour analysis, a natural science of behaviour. That’s nested more broadly within an evolutionary worldview, and I’m also deeply interested in complex adaptive systems thinking.

Writing by nature involves interpretation. Many of the most popular behavioural science/psychology blogs on Substack are highly interpretative, which means they sometimes go beyond what the science suggests. With Selectionist, I aim to stay close to the science, and keep my interpretations grounded. That takes a lot of work—I spend a considerable amount of time reading the research literature in preparation for each article, even on topics I’m quite familiar with. For my nudge series, I prepared for 4 months (with somewhere between 30-50 hrs of reading, and many more thinking) before I wrote the first article. It is also unsexy, and may not win me readers, but I’m inclined to stay the course for now2.

3. Be useful.

As mentioned, in my articles, I try my best to contextualise my discussions. Selectionist doesn’t operate in the realm of BIG IDEAS and theory development. There’s value in that, but we’re more pragmatically focused on solving real-world problems. You can say we’re more akin to engineers than theoretical physicists. You may find that many of the motivating examples I include in my articles are from healthcare and environmental sustainability. That’s because these problems interest me the most. I also happen to be a population health and implementation science researcher.

On Writing

What do I choose to write about?

The simple boring answer is—topics that interest me or that I’m currently thinking about (typically both). I have an idea bank with notes on topics I would like to write about (currently >10 ideas). Some will fit in standalone articles, others will require 2 or more articles, like my nudge series. These multipart series require a lot more work (and frankly, daunt me), so they won’t be that frequent.

How do I write?

When I decide to write an article, I always begin with reading. I probably spend as much time (if not more) reading as writing for each article. Writing is usually done during my commutes on my phone, or in the evenings after my kids have slept, and stillness reigns. After completing a draft, I typically pass it through an LMM, and incorporate the feedback I find relevant. I estimate about 10-20% of each article has been refined by LLM inputs.

Initially, I committed to writing an article a month, but that quickly fell through with the arrival of my second child. Writing is hard, especially with 2 young children whose raison d’être is to increase entropy. In one instance, I forced myself to write and publish an article to meet my monthly target, and ended up with a piece which I deeply dislike (I’ll let you guess which one that is).

Looking Forward

Currently, Selectionist has close to 100 subscribers. While I expected more (and a broader) readership when I started this publication, considering my negligible social media presence and refusal to play LinkedIn/Substack algorithm games, I’d say 100 is a pretty good number! I’ll be happy to have added another 100 in a year’s time. The main outcome I’m focused on for Selectionist is creating content that I think is useful and am proud of.

Recently, an ex-professor and mentor of mine (hi Ben!) asked what’s my end goal with Selectionist. I don’t have an answer. Near-term, my priorities are family and work, so I’m happy for Selectionist to just chug along. Long-term, I think there’s some payoff to be found somewhere, but I don’t know when that’ll be, or if it’ll happen. Fortunately (or not), I’ve a pretty low delay- and uncertainty-discounting rate3.

To one more year!

Stats for the Nerds (as of September 26)

Article Stats

Number of articles: 10 (1 every 1.3 months)

Median article length: 2,366 words

Shortest article: What’s in a Nudge? Addendum (1,390 words)

Longest article: Living for the Moment (3,376 words)

Median article views: 187

Least viewed article: Solving Diabetes (125 views)

Most viewed article: A Very Profound Misunderstanding (1,062 views)

Reader Stats

Most views of an article by a single individual: 31 (!!)

No. of countries subscribers come from: 21 countries

Countries with most subscribers:

1. United States

2. Singapore

3. United Kingdom

4. India

5. Germany

What’s the first answer that came to you? I’ll bet that answer came rather quickly. Chances are, diabetes is a (at least somewhat) familiar phenomenon to you.

But ok, if the answer came so easily, why have we not solved it already?

Well, you might say in a patient lecturing tone, just because we understand the cause of something doesn’t mean we have the solution for it. Having the solution doesn’t mean we can implement it. Being able to implement it doesn’t mean we can do it in a sustained, effective manner. Yes, of course, you are completely right. But I want to take our argument one step back today. Underlying all these difficulties is a common denominator. My proposition is this—we do not understand the causes of diabetes, at least not well enough.

Why I’m Wrong

I suspect at this point, some of you are up in arms.

Hol’ up, you’ve got to be kidding, right? What gives you the authority to say that?

Diabetes is one of the most prevalent and well understood diseases. Ask Pete from the street, and he’ll probably be able to tell you that you are much more likely to develop diabetes if both Uncle Georgie and Aunty Georgette (and the rest of the family tree) have it, walking to the car constitutes your daily exercise, and McDonald’s tops the most ordered list in your food delivery app.

And that’s not just folk wisdom. There is plenty of science to back that up. Just look at the number of articles showing up on a Google Scholar search:

The sheer number of articles tells only part of the story. Diabetes has been studied by scholars from a multitude of disciplines, who have uncovered multitudes of causal factors ranging from genes to behaviours to the environments and systems we are embedded within.

An individual diagnosed with diabetes stands before a group of scientists. Here are some of their responses:

1. Their diabetes is the result of their poor dietary habits, physical inactivity, poor sleep, and stress.

2. It is a function of systemic forces that promote poor dietary habits, physical inactivity, poor sleep, and stress.

3. It is caused by a cascade of biological events resulting from fat accumulation in the liver and pancreas, leading to insulin resistance and impaired insulin production.

4. They have a specific variant of the TCF7L2 gene, and a specific variant of the SLC30A8 gene.

5. They are from an impoverished neighbourhood and experience healthcare inequalities.

6. It is rooted in a fundamental mismatch between our evolutionary history and our current environments.

7. Pah… pay those myopic geneticists and sociologists no heed. Of course it is caused by a complex interaction between their genes and environment.

Now, which of these correspond most closely with your own answer? Each of these answers is, of course, correct. Some of those scientists may say that some answers are more correct than others (their proclivity to do so likely correlates with how much time they’ve spent chipping away at a particular answer), but we can find robust evidence for each of these views among the 3.8 million articles scattered throughout the research cosmos.

Which is great! We truly do have a deep, multifaceted understanding of the disease. So what on God’s obesogenic earth am I talking about?

Why I’m Right

If you look closely at each scientist, you will see the faint outline of a barely visible adornment over their head. What’s that, you ask. Why, a bucket, of course. Painted on each bucket are some words. This one says “genetic risks”; that one says “liver and pancreatic function”; another one says “ultra-processed food consumption”. You may find some scientists donning two buckets—“lifestyle behaviours” and “health equity”, or “liver and pancreatic function and “genetic risks”. With luck, you may even find one who dons three or four buckets.

The point is this—when I say we do not understand the causes of diabetes, I mean that the causes are deeply complex, and that each of us individually, even the foremost authorities on this subject, only understand some aspect of the problem2.

Thinking in Buckets

Part of that can be attributed to our finite capacity for acquiring knowledge. Unless you’re Terence Tao or Neo from the Matrix, there are architectural limitations to how much knowledge you can acquire on a particular subject over your lifetime3.

Another part can be attributed to the structural limitations of our scientific institutions. Our institutions tend to reward depth, and disincentivise breadth. As a knowledge base develops (i.e., we realise the nature of the beast), scientists begin to specialise, and get rewarded for specialising. Entire careers can be made on the study of a single gene, hormone, or class of behaviours. Paradoxically, in the words of the psychologist Henry Roediger, we study more and more about less and less. Our buckets get deeper, but narrower.

Three Bucket Problems

This is important. Our buckets dictate how we view problems and their solutions. Thinking in buckets helps us make sense of phenomena, but they present us with additional problems:

1. We can’t agree on a solution. We need more drugs! No, we need better targeted drugs based on genomic information! No, we should prioritise better eating and physical activity habits! No, we need to fix access to healthy foods and physical activity promoting environments! No, we need all of the above, and it should be done this way! No, it should be done that way!

2. We design flawed solutions. We design solutions based on what we know, without incorporating information from other knowledge bases. Thus, we develop behaviour change plans around random physical activity targets (e.g., 10,000 daily steps) without understanding the physiological effects (or non-effects) of meeting this target. Thus, we focus on a single metric (blood glucose) without considering contextual mechanisms and lived experiences of the individual.

3. We implement solutions imperfectly. We develop and push drugs onto patients without considering if they will take them, or recommend specific diets (e.g., Mediterranean diets) for populations that have fundamentally different food practices and access. The prescribers will say, I’ve provided recommendations based on the best available knowledge. It’s up to them to follow my recommendations, or not. But the fact of the matter is, getting individuals to take their prescribed medications or change their lifestyle behaviours continues to be a huge challenge.

Resolving Our Problems

To summarise our earlier points, we collectively possess a comprehensive, multifaceted understanding but individually only possess deep knowledge of some aspect(s) of diabetes. As such, we land on solutions too simplistic for addressing such a complex phenomenon. What then is the solution?

The obvious answer is to form interdisciplinary teams. Each member of the team contributes their perspective from their area of expertise, and we arrive at a comprehensive solution together. However, as those of you who are involved in such collaborations can attest, it’s really hard to do interdisciplinary work!

In a recent article, the psychologist Paul Bloom says, put a group of scientists of different backgrounds together and you’re likely to observe them engaging in parallel play.

Small Potatoes

The only interdisciplinary conversations worth having

(This is a niche post of interest to a relatively narrow community of scholars. I’ll be back soon with something more normal…

Read more

10 months ago · 101 likes · 17 comments · Paul Bloom

It’s like gathering a diverse group of musicians together and commissioning them to compose a coherent piece. One is trained in the Western tradition and speaks German; another trained in the Carnatic tradition and speaks Tamil; a third trained in the Gamelan tradition and speak Indonesian; and so forth4. Each musician believes their own musical tradition transcendental, and other musical forms to be a cacophony of sounds. Physically next to each other, but ideologically worlds apart. The outcome is likely not going to be all that good.

The solution, Bloom argues, can be summarised in a quote by the late philosopher Jerry Fodor:

To Bloom, that means one should already be

engaged and interested in the ideas and methods of other adjacent fields. So a neuroscientist who studies colour vision, say, will be conversant with the philosophical literature on the nature of colour. A philosopher interested in the ontology of race will have thought deeply about the debates within social psychology regarding the nature and extent of implicit racial bias. And so on.

I agree, but I think some further unpacking is in order. There are many ways of collaborating, to different degrees, for different purposes. Some like to draw distinctions (yay, more buckets) between cross-disciplinary, multidisciplinary, interdisciplinary, and transdisciplinary approaches. I won’t bore you with the differences, since folks can’t seem to agree on what these terms mean anyway.

Suffice to say, the odds of a successful collaboration correlates with the degree to which a common level of understanding can be established. That understanding requires the team to have a common goal (e.g., solving diabetes, composing a musical work integrating ideas from various traditions), and be engaged and interested in how others can contribute to this goal. It also requires collaborative action over time. These are not easy conditions to achieve.

Catalysis

One way to reduce the time requirements and increase odds of a successful collaboration is, borrowing a metaphor from the evolutionary biologist David Sloan Wilson, to introduce a catalyst5.

The term itself is of course borrowed from chemistry. Here’s the relevant definition from Merriam-Webster:

a substance that enables a chemical reaction to proceed at a usually faster rate or under different conditions (as at a lower temperature) than otherwise possible

Catalysts exist on a continuum—on one end, perhaps some musicians speak and understand a smattering of English, or our classical Western-trained musician has previously briefly acquainted themself with Gamelan music while on holiday in Bali. On the other, we can have musicians who are effectively bi- or multi-lingual, and have spent an extensive amount of time exploring various musical traditions6. Their views and work transcend specific knowledge bases. These individuals, let’s call them transdisciplinarians, serve as bridging agents that bring conversations out of buckets. The best transdisciplinarians even manage to convince others that their arbitrary buckets don’t really exist.

What Fodor speaks of interdisciplinary conversations that go on inside a single head, I imagine those who do it best not only see how knowledge and practices from two or more disciplines fit together, but are able to envision how emergent knowledge that transcends each discipline can arise.

Solving Diabetes: Towards A Bucketless Approach

Not all problems require such a bucketless approach, but they are particularly important for wicked problems which, by their nature, resist simple solutions. Diabetes is one such problem—messy, indeterminate, and deeply entrenched in many aspects of society. In reality, I do not believe a true bucketless approach is possible, nor do I believe there are true transdisciplinarians in the ideal sense7. But we can hold that as an ideal which we (individually and our institutions) strive towards.

A society grappling with diabetes stands before you. You have to pick a team to solve the problem. You could turn to superstar scientists who shine in their own buckets but rarely venture beyond them. Me? I’ll place my bets on the teams populated by catalysts strong and weak.

5

Here’s one way to think of catalysis:

Successful collaborations require a common goal and engaged collaborators, and collaborative action over time. A catalyst shortens the time required, and increases the odds of success.

Cleese Products

A very profound joke

Listen now

a year ago · 5729 likes · 16 comments · John Cleese

John is not alone. Behaviourism, as the popular consensus goes, is dead. It died in the wake of the cognitive revolution of the mid-20th century. Most introductory psychology courses preach this truth. Behaviourism is no more. It has ceased to be.

Well, not quite. Let me tell you why.

Points of Agreement

First, there are some things John said that I agree with and want to highlight:

[Scientists] tend to take parts of the problem that are very easy to do experiments on and then pretend those are the only ones that matter.

Indeed, we do. This is not unique to behaviourists. We find evidence of this across different disciplines. Case in point—well-being is extremely hard to define and measure. Thus, psychologists come up with questions like these

We pretend the numbers circled mean something real. When the score drops from 6 to 5 three months later, we also pretend that change means something real.

This ridiculously oversimplified idea that rats and pigeons can tell us all about the depth of the human condition.

Certainly, rats and pigeons cannot tell us all about the depth of the human condition. We’ve come a long way from that view. There are fundamental differences between humans and non-human animals, such as our sophisticated use of language and capacity for symbolism2. However, non-human animal studies can tell us a lot about ourselves. They have done so, and will continue to do so.

Scientists love doing science. They are sometimes remarkably uninterested in the philosophy of science.

It is entirely possible to have a scientific career without a grounding in a philosophy of science. I know this for a fact. More tragically, it is entirely possible to have a (distinguished) scientific career without creating any real-world impact whatsoever.

On The Scientific Method

While clever, I think the joke John shares misrepresents the workings of scientists. All analogies are imperfect, but here’s one that better approximates what scientists do.

Instead of trying to find a key on a patch of land, scientists seek to understand the lay of the land beyond our current established boundaries. Really, we are more like explorers and cartographers than what the Brits might call muppets or plonkers.

As to how we chart our paths through these unknown territories, it is perfectly reasonable of course to find the easy ways, the navigable paths, rather than hacking through brambles. Sometimes, these lead to dead ends, but there are always lessons to be gained from these journeys. Sometimes, we encounter new features (blimey, a lake!) that challenge our fundamental assumptions (there’s only land yonder) and force us to invent new technologies to progress further.

Broadly, we carry one of two types of flashlights on this mission. Some carry flashlights that cast a diffuse light over a large area, allowing us to observe a wider swath but with poorer fidelity. Others carry flashlights that produce concentrated narrow beams of light, allowing us to observe a smaller area very well. Behaviourists tend to fall into the latter category. Two very different methods, but both allowing us to gradually peel back more of the unknown.

On Behaviourism

What Behaviourism Isn’t

In talking about behaviourism, it is important to first be clear what we are referring to. Behaviourism is a philosophy of science, or more precisely, a group of related philosophical traditions. The philosophical tradition most non-behaviourists (including John) associate behaviourism with focuses solely on the relationship between environmental stimuli and observable behaviours.

Few behaviourists today embrace this position. Instead, most behaviourists believe that thoughts, feelings, and other internal processes are also things that we do, and thus behaviour. A full account of behaviour must include them.

What Behaviourism Is

Behaviourism centres on a core belief—our environments influence our behaviours, profoundly so.

A mother who scolds her child for misbehaving; a supervisor who calls attention to the outstanding work done by her supervisee; a judge who shows leniency to a hardened gangster after hearing about his rough upbringing; a school in an impoverished locality that provides free lunches to all its students—these are all people who have informally adopted a behaviourist worldview. According to this worldview, present outcomes shape future behaviours; past outcomes shape present behaviours. The psychologist Pat Friman calls it the circumstances view. Here’s him talking about this view.

To some, this view sounds remarkably unremarkable. Well, that’s just plain common sense, innit? Yet, this is not the dominant worldview. That worldview attributes the causes of behaviour to an individual’s personality, psyche, or some other innate quality. The child misbehaves because he has a flawed character. Gangsters commit atrocities because they are vile and evil. We take descriptive concepts and imbue them with explanatory powers3. For most of us, this is the default way of thinking, with the behaviourist worldview only surfacing occasionally.

In psychology, while behaviourism and its associated scientific practice (behaviour analysis) is no longer the dominant mode of enquiry, in many respects, its ideas have been successfully assimilated into mainstream thinking and practice. To understand behaviours, psychologists today observe observable behaviours (including self-reports using questionnaires). To change behaviours (including thoughts), excepting rare circumstances, we do so by changing environments. The psychologist Henry Roediger goes as far as to argue for the following possibility:

[Behaviourism] actually won the intellectual battle. In a very real sense, all psychologists today are behaviourists.

I’m not the president of the Association for Psychological Science so I won’t make similar bold claims. What I will say is that behaviourism and behaviour analysis has made significant contributions over the past decades and continues to do so. These aren't just theoretical abstractions, but practical successes that have improved millions of lives. To convince John of the value of a behaviourist worldview, here’s a list of 8 such contributions.

1. Alleviating Psychological Suffering

Therapies rooted in behaviourism are widely used in clinical settings to treat various psychological conditions. Behavioural activation is as effective as cognitive behavioural therapy (which also uses behavioural technologies by the way) in treating depression, at a much lower cost4. In recent years, we also witness the emergence of the “third-wave” of behaviour therapies, which are philosophically aligned with behaviourism. These therapies have been applied successfully to a wide range of conditions, including those previously considered untreatable5.

2. Teaching Skills to Individuals with Developmental Disabilities

Applied behaviour analysis is widely used in addressing skill deficits for individuals with profound disabilities, including cognition, language, social communication, adaptive, and daily living skills, and reducing severe problem behaviour which are exhibited by many of these individuals. Globally, there are currently more than 300,000 certified practitioners employing behavioural technologies to improve the quality of life of these individuals.

3. Building Quality Education Systems

Positive Behavioural Intervention and Supports (PBIS) is a tiered framework for improving learning, social, emotional, and behavioural outcomes in schools. PBIS favours teaching and reinforcing desired behaviours over punitive action for undesired behaviours, and provides different levels of supports based on the needs of each student. PBIS has been widely adopted, including in more than 25,000 schools in the US.

4. Supporting Positive Child and Adolescent Development

The Teaching-Family Model and Triple P – Positive Parenting Program are examples of programmes that support families and caregivers in raising children successfully. They are person-centred, strengths-based, cost-effective programmes drawing from behavioural principles to support child and adolescent development, including those who have experienced trauma, abuse, and loss. These empirically supported (see here and here) programmes have benefited millions of children, adolescents, and their families/caregivers to date.

5. Treating Addictions

Turns out, one of the most effective ways to stop individuals from abusing drugs is simply to pay them not to. This method, known as contingency management, has been around for decades, but has not been adopted widely, mainly due to ethical concerns—as the dominant worldview would argue, why should we pay them to do the right thing? However, with robust evidence for its effectiveness, simplicity, and cost-effectiveness, contingency management programmes are increasingly being implemented.

6. Enhancing Employee Performance and Workplace Culture

Behavioural principles have been applied to the workplace to help organisations meet their goals. Such work, termed organisational behaviour management, involves developing or improving processes to enhance employee performance, employee retention and satisfaction, creating optimal workplace cultures, and improving quality standards. This is highly important. Many organisations have incredibly toxic workplace cultures, and their leaders read bestsellers that make claims with zero empirical basis and covertly believe in rank and yank practices6.

7. Improving Workplace Safety

One particular application of behavioural principles in the workplace is in safety improvement, especially in environments with high occupational health risks. Behaviour-based safety (BBS) involves systematically tracking and improving safety-related behaviours in such environments. In some cases, the effects of BBS initiatives have been shown to persist even 14 years later.

8. Improving Decision-Making

Folk psychologists have it that delayed outcomes has less bearing on our behaviours than immediate outcomes. And they are right. I’ve written about this in a previous article.

Thanks to decades of research, we now know precisely how so. The difference in subjective value between an outcome today vs. a week from now is much greater than between 10 years vs. 20 years in the future. These insights have allowed us to develop methods to help individuals make better choices, including some pretty interesting ones. Similarly, we also know a lot about how people make choices with probabilistic outcomes.

BONUS: Advancing Artificial Intelligence

One of the major catalysts for the cognitive revolution was the advancement of computer technologies, creating the metaphor of the brain as a computer and the mind its processes. And so it is with supreme irony that many advances in artificial intelligence today are deeply inspired by behaviourism. I will probably do a deep dive on artificial intelligence and behavioural science sometime in the future, but for now here are two examples:

1. How do you teach a computer agent to play games or drive a car? Well, you would first establish what are good and bad outcomes—increase/decrease win probability, increase/decrease successful commute (without killing anyone) probability—then run simulations and allow these outcomes to shape the agent’s behaviours. Do this enough, and we get highly sophisticated agents that can perform feats of miracles, such as this7:

2. When using ChatGPT or any other Large Language Models, occasionally, you may see it provide two responses to your prompt and ask you to select the one you prefer8. For example, you provide a picture of a bird and ask, what is this? Answer A: It’s a bird. Answer B: It appears to be a Satanic Goatsucker. These birds are found in… Your feedback goes into training the next generation of models, thus shaping their future output.

The Profound Missed Stakes

In many ways, we live in the most behaviourally engineered world that has ever existed. We know more about influencing behaviours than ever before. With this knowledge, we can help individuals eat better, save more, treat others more kindly. We can also make them eat worse, spend more, and turn them against each other. All these achieved through subtle manipulations in the individuals’ environments, possibly without their notice.

In his article, Roediger predicted that a strong form of behaviourism would make a comeback in mainstream psychology in this century. In many ways, it already has.

To John and my readers, I hope to have convinced you of that behaviourism is alive and thriving (albeit with a few flesh wounds).

But in truth, the label hardly matters. What matters is pursuing a psychological science that helps us understand and solve our greatest challenges, whatever we decide to call that. Whether a behaviourist worldview brings us closer to that goal, I’ll let you be the judge.

Typically after publishing an article, I would publicise on Reddit, which leads to the occasional discussion with the various good folks who lurk there. These discussions often prompt me to think deeper about what I’ve written, and even when we disagree (especially so), I often find that I’ve learnt something new.

After publishing Part III, I received this comment on the r/BehavioralEconomics Subreddit1:

I was always under the impression that the practice of a nudge is to manipulate the recipient to act in their own best interest. They are not being manipulated to do something outside of their control, instead they are being assisted to make healthier or beneficial choices.

I'll use the example of requiring employees to opt out of contributing to a retirement account. Those who do not believe or feel strongly against contributing to their retirement will make the extra effort to opt-out. Those that stay in likely had the intention to enroll or don't mind enrolling.

I welcome examples of nudging that manipulate the recipient into an action they wouldn't otherwise take.

Well, turns out this Redditor had not actually read the article. Oops. Still, I realised I did not get into the thinking behind designing an actual nudge. Let’s remediate that briefly today, using the retirement account example provided by our Redditor. I’m by no means an expert in designing interventions for retirement savings, but I think there are key considerations that even a non-expert can ponder to help them design a more effective intervention.

Clarifying Two Common Misconceptions

There are two points raised in the Redditor’s comments that I would first like to address. I’ve covered them briefly in Part III, but I think they’re important and bear repeating.

The first pertains to their own best interests. The idea of a best interest assumes there’s a singular, rationally optimal choice. In reality, preferences vary from individual to individual, situation to situation, and rarely follow neat patterns. Some frame it as the choice that we are inclined to make when the time gap between choice and actual consumption is large (choosing low-fat yoghurt) vs. small (choosing ice-cream). Well, many would exclusively choose one over the other either way.

For those of us who fall in between, what gives the large time-gap choice power over the small time-gap choice? Experience of regret? I would very much regret my low-fat yoghurt while my friends enjoy their dark chocolate and pistachio cream soft serves during our weekend hangout2. In fact, I might choose to eat the low-fat yoghurt now and save those calories for that soft serve when I meet with my friends this weekend. However you look at it, justifications of their own best interests quickly fall apart under scrutiny.

The true justification for us meddling in the private affairs of other individuals is doing so aligns with society’s best interest. Overall, society would be better off if we consume less ultra-processed foods. This is rarely explicitly acknowledged, but without doing so, policymakers and behaviour change practitioners risk designing interventions that result in negative unintended consequences.

The second is that behaviour change techniques are value-free tools. They’re merely means for actors to influence the behaviours of individuals. Social media platforms are designed specifically to maximise engagement. For some, they provide a wonderful source of entertainment. For others, they create behaviours typical of addiction and problematic use. The same tools, same mechanisms, same understanding of human behaviours, applied to different ends result in very different outcomes.

The Design of a Nudge

There are countless ways to encourage employees to increase their retirement savings. Obviously, context matters. So, if you are looking for a magic formula, I’m sorry to disappoint, look elsewhere. But I think it’s important to understand designing an intervention goes beyond simply cutting and pasting from elsewhere. The process is often messy—that’s not always apparent from reading research articles, but behind every successful intervention is a tangle of constraints, trade-offs, and unresolved questions.

I intend to keep this article short, so here’s a non-exhaustive list of considerations that I’ve grouped into 3 broader categories:

1. What is the Problem We’re Solving?

This shouldn’t be a surprise to anyone who has read Part III. Well yes, we want to employees to save more for their retirement. How much exactly should they save? Should they all save equally (young vs. old employees, low-wage vs. high-wage earners)? Should their savings rate be constant?

Also, saving more is what we want—is that what the employees want? Not everyone has sophisticated (or even basic) financial literacy skills and see the value in saving. Should more be done to address this gap and how can we do so meaningfully?

There typically exists a power imbalance between the designers and the recipients of the interventions. Who benefits from the increase in retirement savings? The institutions or the employees? What if the incentives are not aligned?

2. How Should We Design Our Solutions?

As previously discussed, there are many ways to design a default. How should we present the default? We can implement an automatic enrolment process, where employees can fill some paperwork to opt out. We can present the opt-out option buried in pages of legalese. We can have an expert sit down with the employee and provide choices and recommendations. And so forth.

For those already enrolled in a retirement savings plan, how hard is it to opt-out? If you’ve ever tried to cancel a credit card, you know what a pain in the ass that is.

People’s contexts and needs change. Is our intervention a once-off thing or should we get individuals to re-evaluate their choices each year (e.g., when they receive their annual increment)?

In conjunction with presenting the default, should we also provide information about the value of saving? This can include infographics that show how much they can expect to have in 20 years for the amount they choose to save.

3. What are the Downstream Effects?

Most people would be better off if they saved more. But not everyone can afford to, especially those struggling just to stay afloat. If our interventions exclude them, over time, these individuals will fall further behind those who can afford to save. So, in a sense, our interventions further perpetuate inequity. Are we OK with that? Should we remediate that?

If implemented without allowing space for thoughtful, reflecting decision making, how would that affect trust towards the designers and institutions? How would employees feel about the default choice they were influenced to make down the road, when the misalignment between outcomes and their needs become more apparent?

4. What are the Available Resources and Constraints?

The first 3 categories pertain more to the design aspects of an intervention. The last category of considerations, and in many ways the most important, pertains to the operational and implementation aspects.

Sometimes, we have fantastical ideas that just might work beautifully, if only we had a bit more time, manpower, financial resources, or support from the project sponsor. Yeah, if only. What works under our given set of circumstances and resources? Will my intervention have legs after we’ve wrapped up its implementation?

Final Thoughts

These considerations are not exhaustive, but rather are representative of the types of questions one must think about when designing a behaviour change intervention. No single intervention perfectly addresses all these considerations. In fact, a crucial part of design involves balancing trade-offs between them.

Behaviour change interventions are not just about influencing individual choices in a vacuum. They are embedded within the broader contexts of the systems in which individuals are (differentially) located, and their lived experiences and values. That just means we must work harder to design well. The recipients of our interventions deserve no less.

To recap, in Part I, we analysed the core tenets of Nudge Theory, discussed the issues with them, and decided we should discuss nudge-type behaviour change techniques (BCTs) separately from Nudge Theory. We then proposed an operational definition of a nudge. In Part II, we examined the various nudge-type BCTs, and found not all of them met the conditions of our definition. We then proposed a framework for understanding their mechanisms.

At last, we turn our attention to the most important (and clichéd) question of all—should we nudge?

There are essentially two considerations when deciding on behaviour change:

1. What is the nature of the problem?

2. Is my solution right?

As we shall see, successfully changing behaviours (through nudge-type BCTs or otherwise) hinges on a nuanced, thoughtful treatment of these two considerations.

Maladies in Gluttonsbury

First, let us make a detour into the fictional town of Gluttonsbury. The inhabitants of Gluttonsbury, or Gluts as they affectionately call themselves, take immense pride in their food culture. Lunch breaks are three hours long to allow “proper nourishment”. Dinners are often social events till the early hours of the morning at the various excellent food establishments lining the main street. The town is also home to one of the world’s biggest food processing plants, and the fabled Gilly Glinka’s Chocolate Factory. Highly affordable ultra-processed foods are staples in Glut’s diets at home, but not fruits and vegetables.

As you might imagine, obesity is a hefty issue in Gluttonsbury. Mayor Glover understands that Gluts’ propensity to overeat during meals is perpetuating this weighty problem. He commissions you, a behavioural scientist of the highest calibre, to run a campaign to discourage overeating during meals, in hopes of lowering obesity rates. You run a smart, effective campaign with the slogan “NOT TOO MUCH, NOT TOO LITTLE, JUST RIGHT!”, provide salient, targeted information on the importance of refraining from overeating, work with food establishments and school canteens to reduce plate sizes, and start a “EATING JUST RIGHT!” challenge leveraging on social norms to reduce overeating.

After 6 months, the average Glut is consuming 14% less calories on average each meal and obesity rates have started to fall, decreasing from 62% to 60%. Mayor Glover is glowing with glee. And so should you. Right?

What’s the Problem?

Sometimes, the simplest questions are the hardest ones to get right.

Often, we assume problems are as they are presented to us. Often, that assumption is wrong. Good design is problem-centric, not solution-centric, but we rarely spend the necessary time and effort to understand the true nature of problems. Instead, we often tend towards simple, reductive views of problems. This tendency presents in the form of two thinking traps.

Trap 1: Focusing on Individuals Not Systems

At the heart of the recent discourse on nudging (and the behavioural sciences more broadly) is a fundamental mismatch in how problems are scoped.

If your focus was on reducing Gluts’ meal time calorie intake, your campaign was a resounding success by all accounts, given the average nudge intervention results in far more modest changes (more on this later). If you view obesity as a function of Gluttonsbury’s food-centric urban design, industries, and imbalanced food economics, then you would view your campaign as woefully inadequate. No amount of nudging will reduce obesity to an acceptable level. In fact, we can’t quite be sure these changes will even persist a year from now. Seen in this light, the campaign only serves to deflect attention from what should really be done—redress the imbalance in food economics and restructure Gluttonsbury to facilitate healthier relationships with food.

In short, there is a tension between scoping problems at what the behavioural scientists Nick Chater and George Loewenstein term the i-frame (individual level) vs. s-frame (systemic level). In the former, problems are viewed as issues of individual responsibility—thus solutions should target individuals’ behaviours. Traditionally, this is understood to be the domain of nudge-type BCTs. In the latter, problems are viewed as systemic or structural, and behaviour change is effected through changes in the systems. Traditionally, this is understood to be the domain of policy action such as regulation and taxation.

Chater and Loewenstein argue that by focusing on nudge-type BCTs, behavioural science tinkers around the edge of fundamental problems without truly addressing them. To them, not only are nudge-type BCTs less effective, they also draw resources away from the more impactful s-frame alternatives.

In my opinion, Chater and Loewenstein’s argument is compelling, but rests upon a false dichotomy. Individuals don’t exist in vacuums, but as part of systems. Systems are not monolithic structures, but are composed of individuals and individual actions. You simply cannot change one without changing the other. Understanding problems require us to consider both and identify who (e.g., end users, policymakers, corporations) and which of their actions are at the heart of the problem1.

Trap 2: Focusing on Individual Behaviours Not Extended Patterns of Behaviours

We also tend to focus narrowly on individual problem behaviours. Nothing wrong with that sometimes. Men really ought to aim better in the toilet. More frequently, the problem behaviour in focus is but one part of what the behavioural scientist William Baum describes as temporally extended pattern of behaviours. Smokers don’t just smoke. They also tend to consume more alcohol, have a poorer diet, and exercise less. In short, they display a tendency to discount their long-term health in favour of short-term pleasure. A narrow focus on curbing smoking misses the point—it won’t do to put out the fire on a porch when the whole neighbourhood is burning down.

For Gluts, overeating during meals, while no doubt problematic, is but one piece of an extended pattern broadly encompassing their relationship with food, including the types of food that they eat, when they eat them, and their cultural attitudes toward food. To expand our conceptualisation of the problem further, Gluts socialise through food, and food-related activities dominate Gluts’ leisure time, crowding out health-promoting behaviours such as physical activity.

Real-world problems are complex, with many interrelated and interdependent components. They wouldn’t be actual problems otherwise. Resisting our tendencies toward narrow, reductive conceptualisation of problems is effortful, but those who do so will be well rewarded for their efforts.

With that, we are ready to consider our potential solutions.

Is My Solution Right?

Questions about rightness come in two forms:

1. Is it effective—does it achieve the intended outcome?

2. Is it ethical—are the means of addressing the problem acceptable?

Do Nudges Work?

Let us first examine the evidence. Recently, a research group at the University of Geneva analysed over 200 nudge-type interventions studies and concluded nudge-type BCTs work, to a small-to-medium degree. They were quickly refuted by another group of researchers who reanalysed the same data and claimed that the published studies were cherry-picked2. Separately, two researchers from the University of California, Berkeley, examined evidence from 126 real-world implementations of nudge-type BCTs and found nudge-type BCTs produced slight changes in behaviours (1.4% in absolute terms, or 8.0% in relative terms). Based on these findings, one might conclude that nudge-type BCTs are ineffective, or only incrementally effective at best. Furthermore, it remains unclear how lasting these changes are.

Before we dismiss nudge-type BCTs, it is important to note that many of them were derived from behavioural principles that were developed and validated in animal and human research over the past decades, long before the term nudge was coined and popularised by Richard Thaler and Cass Sunstein. As an example, in a classic experiment published in 1972, the behaviour scientists Howard Rachlin and Leonard Green nudged pigeons to consistently select larger-later rewards (pigeons typically prefer smaller-sooner rewards) by manipulating the duration before choices were presented. As behavioural scientist Marco Tagliabue puts it:

So, what gives?

First, to restate our earlier point: good design is problem-centric, not solution-centric. The effectiveness of nudge-type BCTs depends on correctly identifying problems and fitting our solutions accordingly. The goodness of fit depends on the contexts in which they are used, and a correct understanding of their mechanisms, which as discussed in Part 2, are highly diverse.

Second, much of the evidence is gathered through experiments conducted in tightly controlled environments, for narrowly defined problems, within short time periods, and without due consideration for feasibility nor sustainability. In short, the evidence is mostly based on simplified problems, against the backdrop of a static (and supportive) context. It is naive to think that out-of-box theory-driven implementation of nudge-type BCTs in real-world contexts will work exactly as results from an academic paper would suggest. Rather, real-world problems call for tailored solutions, often a combination of nudge-type BCTs and other tools.

Finally, extending from the previous point, successful implementations need to be data-driven. Even under the best circumstances, things almost always don’t go as expected. Constantly measuring and evaluating our interventions’ impact allows us to refine and tailor our approaches, and adapt our approach as contexts or even problems themselves change (they most certainly will).

Are The Means Acceptable?

From the outset, Nudge Theory attracted a great deal of ethical concerns. We will broadly summarise three of them3.

1. Autonomy

Critics argue that the use of nudge-type BCTs violate the individual’s autonomy, either by limiting choices or imposing costs on certain options. Costs can come in the form of increasing effort required to perform certain behaviours, or adding a costly outcome to a choice. To be clear, nudge-type BCTs that supposedly violate autonomy tend not to place limits on the availability of choices, but rather on how individuals exercise those choices. To use two examples discussed in Part I:

1. Ordering healthier options to appear first in a food app, and placing fast food options in a separate tab preserves the number of options available in principle, but makes fast food options less accessible, and thus, less likely to be selected. Further increasing the effort to access fast food options further decreases that likelihood.

2. Using opt-out defaults to increase organ donation consent rate can bypass the individual’s choice. Here in Singapore, all individuals are automatically enrolled as organ donors upon turning 21 years old by law, which we are presumably aware of (many are not). Individuals can opt out by filling in a form, but few are aware of this option, or know the processes involved.

To be fair, the degree to which nudge-type BCTs violate autonomy varies greatly and depends partly on their design and implementation. In implementing organ donation consent defaults, on one end, Singapore automatically enrols citizens upon crossing an arbitrary age threshold without explicit communication. At the other end, we might imagine a deliberative process, where individuals reflect on their own role, their family’s role4, and the impact of their decision on their wider community before making a choice. They then make a choice during an offical process (e.g., when renewing their passport), that clearly states enrolment by default, but with a clearly indicated option to refuse. Obviously, the latter approach preserves autonomy more than the former.

2. Welfare

For better or for worse, those who design and implement nudge-type BCTs are typically in positions of power, concerned with what they want. Getting adolescents to reduce social media usage is what a policymaker wants, regardless of whether it reflects adolescents’ wants. On the flip side, social media titans want the same adolescents to stay on their app forever5.

Let’s ignore for a moment the fact that actors seek to influence behaviours for all sorts of reasons and focus our attention on those acting in “good faith”. Justifications for nudging come under two pretexts:

1. It’s what they want. Arguably, Gluts really want to lead long, healthy lives (termed higher-order or true preference), not eat the sinful delicacies in front of them (lower-order preference). I find this argument uncompelling. Let’s face it—they want both. It’s their relative preference for both that matters, which shifts from moment to moment, which may differ from what the next person wants. It takes an oracle of extraordinary prescience to know true preferences, if such a thing even exists.

2. It’s for the greater good. Obesity-related diseases come with huge social and economic costs. Thus, Gluts really should consume less calories, whether they like it or not. The degree to which we can agree with this pretext depends on the problem at hand. Nudge all you want if it decreases infant mortality. But intrude upon Gluts’ sacred relationship with food and you might find a less than warm reception. Also, we must question whether Mayor Glover (and you by extension) has the tacit mandate to intervene in Gluts’ eating habits by virtue of being their elected representative, or if this mandate must be separately sought.

3. Manipulation and Opacity

The above two considerations are important to deliberate upon, but are by no means unique to nudge-type BCTs. They apply to all the tools one might use to influence behaviours, including regulation and taxation.

For nudge-type BCTs in particular, one concern goes beyond violation of autonomy to violation of the individual’s identity itself. While other tools such as bans and fines are transparent (the consequences for flouting a restriction are clear), certain nudge-type BCTs can guide decisions in ways that bypasses one’s awareness. It is an uncomfortable thought, frightening even, that some external party can infiltrate our subconscious a la Inception and create a desire to spend lavishly on diamonds without our knowledge. To give two examples:

1. As discussed in Part I, individuals respond asymmetrically to loss frames and gain frames of mathematically equivalent choices. Knowing this, actors can influence individuals to save more, go for their annual health screening, or perhaps, buy a car we don’t need.

2. Our decisions tend to be influenced by reference points (termed anchoring). This makes evolutionary sense—we typically use what we already know or gather information in our environments to make decisions. But sometimes, our references can be tethered to arbitrary values. In negotiations, price setters tend to set arbitrarily high prices, knowing that the quoted price is likely to make buyers pay more than they would have otherwise.

We are constantly subject to such subtle manipulations. Marketing companies today employ many of the same tactics detailed more than four decades ago in psychologist Robert Cialdini’s classic work: Influence: The Psychology of Persuasion. Social media titans influence behaviours through sophisticated algorithms that are opaque even to the engineers that develop and tune them.

As a presumed “good faith” actor, you will have to decide (ideally with the recipient’s input) if the ends justify the use of non-transparent nudge-type BCTs for the problem you are trying to solve. Different folks will have different takes on this.

Summary

In this series, we’ve dissected nudges, explored their mechanisms, and considered how and when we should use them. Where does that leave us?

I hope to impart to the reader three key takeaways:

First, identifying problems and their boundaries should be central in any design process. Having a clear view of problems allow us to identify correctly the target of our behavioural change intervention, including the who and which behaviours. That in turn guides the tools we employ.

Second, nudges encompass a diverse group of BCTs with diverse mechanisms of action. We can do a lot with a Swiss army knife, but only if we are familiar with what its tools are for, and how they work. Sometimes, our Swiss army knife does not have the right tools, or needs to be used in combination with other tools.

Finally, questions of ethics and morality are valid and important, but tend to get sidetracked as adoption takes priority. We do not yet know the long-term impact of the use (or misuse) of nudge-type BCTs on the relationship between the implementers and end users. If the responses to behaviourally-informed COVID-19 policies in the UK are anything to go by, we still have some ways to go in figuring out how to implement such solutions right.

In the end, the question isn't just whether nudges work—but whether we are designing the right nudges, for the right problems, in the right way.

Epilogue

I would like to leave with you one of my favourite examples. Here is a solution to reduce consumption of unhealthy beverages in Singapore, which combines nudge-type BCTs and other policy tools.

Since 2022, all beverages sold in Singapore must include a nutrition labelling, called Nutri-Grade. Beverages are assigned a grade from A to D, corresponding with their sugar and saturated fat levels. Nutri-Grade labels also display the percentage of sugar in the beverage and advertisements were also banned for D grade beverages. As a result, sugar consumption through beverages has decreased, mirroring outcomes of similar initiatives from other countries.

Why did this work?

First, Nutri-Grade was effectively designed. It provided salient information to consumers who would not otherwise be aware of the sugar content of beverages. The information was clear and easily interpretable through colour codes, and provided in a timely manner, directly at the point where the choice is made. Consumers also value reducing their sugar intake from beverages. Taken together, Nutri-Grade shifted consumers’ preference toward healthier options.

Second, and more importantly, we also witness marked shifts in the behaviours of producers, including reducing sugar content in their products and reworking their marketing strategies to feature low- or no-sugar products. This meant that consumers who did not shift their choices toward healthier options were still consuming less sugar by default.

To be clear, we cannot claim Nutri-Grade alone caused these shifts in producer behaviours. In fact, beverage producers were already pledging to reduce sugar content of their beverages before Nutri-Grade was even announced. Nutri-Grade is but one of many solutions being rolled out in Singapore, and in other countries with much larger consumer markets. Insofar as Nutri-Grade might further move the needle, considerations might boil down to whether they sufficiently shift consumers’ preference, and how that enters a complex interaction with brand reputation, demand elasticity, first movers advantage, and so forth to influence producers’ behaviours.

I don’t claim to have the right answers, but I hope I’ve given you the right questions to think about.

Part I:

Part II:

Welcome back to our series on nudges!

In Part I, we examined the core ideas of Nudge Theory, briefly discussed the issues with its theoretical framework, and decided to separate nudges from their troubled theoretical framework.

We then adopted an operational definition of a nudge proposed by Carsta Simon and Marco Tagliabue:

Simply put, nudges influence behaviour through manipulating antecedent stimuli (stimuli that predict outcomes) but not changing the outcomes themselves.

In getting a food delivery app users to make healthier choices, ordering the options such that healthier choices appear first is a nudge. Providing a discount on healthy options or jacking up the price of unhealthy options is not. In getting employees to recycle more, increasing the number and accessibility of recycling bins in an office is a nudge. Putting up a leaderboard showing which teams recycled the most/least each week is not.

We also noted that manipulating antecedent stimuli only works well if the stimuli predict outcomes that are important to the individual. Ordering healthier choices to appear first in a food delivery app is unlikely to make a dent in the eating habits of a fast-food fiend. Increasing the accessibility of the recycling bins is unlikely to increase the recycling rates of a climate change cynic1.

Today, we will examine some behaviour change techniques (BCTs) subsumed under nudges and delve into the mechanisms by which they influence behaviours.

Nudge-Type BCTs

One barrier to understanding and using nudges effectively is that they really are a highly diverse collection of BCTs. In Chapter 5 of Nudge: Improving Decisions About Health, Wealth, and Happiness, Richard Thaler and Cass Sunstein (henceforth T&S) listed 6 types of nudges, including setting defaults, providing information, providing feedback, simplifying choices, error-proofing, making incentives salient, and provided examples of others, including framing, providing/aligning incentives, ordering choices, and recruiting social norms. All these BCTs influence behaviour through very different mechanisms.

Since then, the nudge universe has further expanded to include more than a dozen other nudge-type BCTs, including just-in-time prompts, creating friction, using commitment devices, and so forth. Without a framework, figuring out what all these BCTs are and how they operate on behaviour becomes overwhelming rather quickly.

To assist the reader, keeping in mind our definition, we propose that most nudge-type BCTs fall within 4 categories2.

Only the first 2 actually involve manipulating antecedent stimuli, thus strictly qualifying as nudges under our definition. The third involves manipulating some dimension of the target behaviour or preceding behaviours, which some may argue falls outside our criteria. The last involves directly manipulating outcomes, clearly violating the conditions of our definition. However, given that a fair number of nudge-type BCTs fall within the latter 2 categories, we would be remiss not to discuss them. Let us examine each in greater detail.

1. Establishing/Manipulating Cues

Each day, we are bombarded with a relentless cascade of stimuli. Given our limited attentional capacity, not all pass through our attention filter to influence our behaviours.

After a long day at work, you enter your home—the lights are low and warm; the couch is soft and inviting; the smell of food is irresistible; the TV beckons with a new season of The Last of Us—the perfect set up for dinner in front of the screen. You end up forgetting about that pre-dinner HIIT workout you planned last night.

Cues work by triggering specific behaviour-outcome relationships. In this case, all the wrong cues and none of the right ones were present. Just-in-time prompts establish the right cue at the right moment to increase the likelihood of the right behaviour occurring. In our example, it can take the form of setting a 6 PM phone reminder, or pre-placing a written note or workout clothes on your couch the night before.

Importantly, for maximal effectiveness, these cues must be introduced at the moment the decision is made. A phone reminder at 2 PM, hours before you were to return home would not be very effective, nor would placing your workout clothes on the bed if you will not enter the bedroom until after dinner.

Here’s an example of a cue that misses the moment. The painted-on words clearly evoke a behaviour-outcome relationship (taking the stairs = healthier you), but is only clearly visible after you enter the stairwell, presumably already with the intention of taking the stairs. Right cue, wrong moment.

Now, the same behaviour often links to several different outcomes. Using a different example, smoking produces immediate pleasure and can serve a social identity function (Big Bill and all my brodies do it). Smoking also represents a financial cost, is detriment to long-term health, can be a source of conflict with a loved one, and can produce social disapproval in public. Not all outcomes are salient at any given moment, particularly if they are distant. Out of mind, they are unlikely to influence behaviour. Increasing outcome saliency brings attention to specific behaviour-outcome relationships to influence behaviour. To reduce smoking, we might highlight its detrimental effects on your health (or worse, on your child’s health).

A specific example of manipulating outcome saliency is the framing of information presented to an individual as a gain or a loss, leveraging on our tendency to respond more forcefully toward negative outcomes (known as loss aversion). We discussed an example of this in Part I, where a doctor has a choice between presenting a treatment in terms of its success or failure rate (90% survival rate vs. 10% mortality rate). Making success or failure rates more salient can lead to very different patient choices.

To stress again, for antecedent stimuli to be effective, their associated outcomes must be important to the individual. Establishing/manipulating a cue works best if there is already a clear relationship between the cued behaviour and a valued outcome. However, sometimes the relationship is unclear—I know using plastics is bad for the environment but I don’t know exactly how bad. Other times, there is a fundamental misunderstanding of the relationship between behaviour and outcome—I don’t know why I should buy this washer than clearly costs more. In other words, an information gap exists that needs to be bridged.

2. Providing Information

With our capacity for culture, much of what we know results from information provided by others rather than solely through direct experience. It seems somewhat disingenuous to call providing information a BCT, a nudge, or more recently, a boost. Still, sharing the right information at the right time can influence behaviour—especially when there’s a clear information gap.

I know using plastics is bad for the environment but I don’t know exactly how bad.

Most of us agree that using plastics bears an environmental cost. Most of us do not have a clear idea of that cost. A video showing a turtle with a plastic straw stuck in its nose is a powerful (not to mention, highly salient) way to get the message across. Here’s the video—hopefully it makes a small dent in your plastic straw usage.

I don’t know why I should buy this washer than clearly costs more.

Returning to an example from Part I, buying energy-efficient electronic appliances typically costs more upfront but saves you money (and reduces your carbon footprint) in the long run. Without information about the long-term costs, we are likely to base our decision solely on the upfront cost, thus opting for cheaper, less efficient appliances. Providing information in the form of energy labels containing information about the annual expected energy consumption, cost, and an energy efficiency rating can guide individuals toward making cost-effective (and climate-friendly) choices3. These labels work by making incentives salient, particularly, by aligning behaviours with long-term incentives. For me personally, they have led to thousands of dollars in estimated savings on my large appliances over the past few years.

A final note before we move on. As all parents can attest to, people sometimes do not rarely do as they are told. To influence behaviours, the provided information needs to connect behaviours to important outcomes. At the risk of oversimplification, providing information effectively essentially comes down to 2 things:

1. What information we communicate. If plastics in the ocean doesn’t resonate, perhaps the messaging should focus on plastics in our brains.

2. How we communicate the information. There is a whole art and science to this. But briefly, consider why a video of a turtle with a plastic straw in its nose is more impactful than one with statistics about the amount of plastic waste in the oceans.

However, even the clearest message often falls flat. The main culprit? Effort—it takes effort to change behaviours.

3. Manipulating Effort

Some of the greatest successes in nudge implementations come from reducing effort (also called reducing friction) to encourage behaviours4.

While not always immediately apparent, most behaviours don’t directly lead to desired outcomes—we usually take several steps to get there.

Additionally, each behaviour can vary in its difficulty of execution—the registration process can be straightforward or highly confusing; the waiting time can be 5 min or 1 hr. Therefore, to reduce effort, we can:

1. Reduce the number of steps required to reach the desired outcome.

2. Reduce the difficulty in executing one or more behaviours in the sequence.

Tuberculosis kills more than a million people annually, disproportionally impacting individuals residing in low- and middle-income countries. Treatment typically involves taking a cocktail of oral medications up to several times a day for 3–9 months. Because of its virulent nature, patients usually have to take their medications at a treatment facility under the supervision of a healthcare professional (i.e., directly observed treatment [DOT]).

As you can imagine, the constant effort required to schedule time and make a trip down to the treatment facility impacts adherence rates. Failure to adhere to treatment has important downstream implications, including the development of multidrug-resistant tuberculosis.

As an alternative, video-observed therapy (VOT) enables patients to have their medication taking observed through live videos (synchronous), or video recordings (asynchronous), greatly reducing the effort required for treatment adherence. The result? In a randomised controlled trial in the UK, more than double of participants assigned to receive asynchronous VOT successfully completed ≥80% of treatment observations in the first 2 months following enrolment compared to participants assigned to receive DOT. Owing to the success of these trials, asynchronous VOT was subsequently adopted by the National Health Services in London.

Another way to reduce effort? Simplifying choices. Have you ever scrolled through countless dinner options on your phone and found yourself unable to make a choice?

We love to have choices, but not too many. Especially if there isn’t a clearly superior outcome. Being presented with too many options of similar quality overloads decision making and induces dissatisfaction, termed the paradox of choice by the psychologist Barry Schwartz. Most of us would do a lot better in arriving at a decision (and with less stress) if someone simply offered us 3 solid dining options to choose from.

Our final application is helping individuals plan for hard decisions in advance (termed implementation intentions). As previously written, we tend to discount larger-later outcomes (e.g., long-term health) in favour of smaller-sooner outcomes (e.g., the pleasure of consuming salty, fatty food).

Queuing at McDonald’s, with the smell of cooked fats cutting through your olfactory defences, choosing a corn cup over the upsized fries seems like an insurmountable task. Planning in advance, in the absence of immediate temptation, is much easier:

When I’m at McDonald’s, I will get the corn cup instead of the fries.

That brings us to our final category—BCTs that directly manipulate outcomes to influence behaviours.

4. Manipulating Outcomes

Many nudges programme some change in outcomes, either by modifying existing outcomes, or adding/removing outcomes. Let’s examine some of them.

A commitment device adds a cost to not acting in accordance with a stated behaviour or achieving a stated goal5. The cost can be financial—in their book, T&S elaborated on a website, StickK, in which users set a goal (e.g., run 20 km a week) and pledge a sum of money toward meeting that goal. Failure to do so would result in the money being forfeited. There’s even an option to forfeit the money to an antithetical cause (e.g., the Manchester United Fan Club for a Liverpool FC fan). Beyond financial incentives, the pledger can also nominate referees to keep them accountable, introducing elements of social affirmation for meeting their goals, and disapproval otherwise.

In fact, recruiting social norms features heavily in nudge-type BCTs. One prominent example is providing households information about their energy use in comparison to their neighbours’ to reduce household energy consumption. One such effort reduced energy consumption by 2% on average in 600,000 households6, with high energy use households reducing their energy consumption the most. Interestingly, the use of social norms can sometimes backfire, signalling that it’s OK for the most energy-efficient households to be a tad less so. A quick fix for this—include an affirmation for their energy efficiency (a ☺️ or a “Great!”).

There’s also a more subtle influence at work—many nudge-type BCTs change how we interpret stimuli by pairing them with emotionally powerful imagery or ideas7. It is no accident that the warnings on cigarette packages are accompanied by extremely graphic pictures such as shown above, specifically designed to induce a direct physiological aversion.

Bringing It All Together

In discussing nudge-type BCTs, we aimed for breadth, not depth. However, it should be clear by now that the term nudge is too broad, lacking sufficient precision and clarity to be useful.

BCTs subsumed under nudge are not a homogeneous collective. Even within the same category, different BCTs influence behaviours through different mechanisms. Furthermore, the boundaries between nudges and non-nudges are unclear, and the nudge universe encompasses a broader set of BCTs than what our adopted definition allows.

Behaviour change practitioners are increasingly cognizant of these issues. As the Chief Behavioural Scientist of the Behavioural Insights Team Michael Hallsworth noted recently,

To use nudges effectively, at the very least, we need to go beyond

I’m going to change [target behaviour] by [insert random BCT I heard about].

to

I’m going to change [target behaviour] by [insert specific BCT] as it influences behaviour through [specific mechanism of action].

Beyond that, several key questions remain to be answered:

1. When and on whom should we use a nudge-type BCT?

2. Who should implement them?

3. Are they actually effective?

4. If not a nudge-type BCT, then what?

We’ll be back with more answers (and more questions) in Part III. Till then.

Part I:

Part III:

As a population health researcher, the word nudge pops up often at my workplace. And understandably so. We’re in the business of changing (health) behaviours. Health outcomes are inextricably linked to behaviours—an individual’s health is directly impacted by their behaviours, and a population’s health is a function of its systems, which are themselves products of interlocking behaviours of individuals within the systems. Trouble is, no one seems to have a concrete grasp on what a nudge really is. Rather, it is most often used as a generic catch-all for a broad, unspecific collection of processes for changing behaviours.

We can hardly blame them. The immense success of T&S’s book resulted in nudge entering the everyday lexicon, diluting its original meaning.

As psychologist Gerd Gigerenzer notes,

Since the publication of Nudge, almost everything that affects behaviour has been renamed a nudge, which renders this concept meaningless.

Spoiler alert: I agree with Gigerenzer. Without precision and clarity, the concept lacks explanatory power—it pretends to explain a behaviour change process without really doing so. As we shall see, even its original meaning as defined by T&S leaves room for plenty of questions.

In Part I of this two-parter, we unpack Nudge Theory and attempt to operationally define a nudge. This will set the stage for understanding the types of behavior change techniques (BCTs) that are claimed to be nudges in Part II. Let’s dive in.

What is Nudge Theory?

At the risk of increasing confusion, I should first caution that there isn’t a consensus definition of a nudge, with some even arguing that nudges should be three separate concepts. This is due in no small part to its initial conceptualisation by T&S, which aimed to integrate several themes into a coherent theory. For T&S, a nudge

1. serves as an operational definition for a collection of BCTs (many of which existed well before nudge was coined) that are conceptually linked in some way,

2. is predicated on an assumption that humans fail to behave rationally, with nudges serving to either remediate these rationality failures, or exploit these rationality failures to change behaviours,

3. is the primary tool for achieving libertarian paternalism, an ideology that deems it possible to influence behaviour positively without limiting freedom of choice—seemingly resolving the tension between the opposing ideologies of libertarianism (maximising freedom of choice) and paternalism (restricting freedom of choice, often for the individual’s “own good”).

In short, Themes 2 and 3 serve as a theoretical framework underpinning nudges (Theme 1). A critique of Themes 2 and 3 goes beyond the scope of this article. For the interested reader, I recommend these 2 papers (here and here). For our purposes, it should be noted neither Themes 2 or 3 are necessary or sufficient conditions for many of the BCTs described as nudges (Theme 1). To briefly illustrate,

1. Not all BCTs described involve rationality failures (if we can even agree on what rational behaviour is). With the oft-cited nudge example of painted-on flies in urinals to reduce spillage, it’s certainly a stretch to claim yup, men aim poorly because they are irrational.

   

   Another nudge-type BCT is to provide information to individuals about the choices available to them. For example, many appliances in Singapore come with an energy label that contains information about the annual expected energy consumption, cost, and an energy efficiency rating. These stickers guide individuals toward making cost-effective (and climate-friendly) choices. The assumption here is that individuals lack information, not rationality.

   

2. Not all BCTs described necessarily meet the conditions for libertarian paternalism. A signature nudge-type BCT is the use of defaults, or a pre-set choice if the individual does not do anything3. One of the biggest success stories for nudge proponents is the use of opt-out organ donation defaults (citizens are presumed organ donors unless they explicitly withdraw their consent) in various countries (including here in Singapore), greatly increasing the organ donation consent rate. However, if you were to survey random passers-by on the streets of Singapore, you would be surprised by how many are unaware that they had consented to donating their organs. The use of such defaults bypasses the individual’s actual choice, and cannot be said to meet the conditions for libertarian paternalism, except in the most liberal sense of the phrase. Indeed, recent evidence (here and here) seems to suggest there is minimal difference in actual organ donation rates in countries that adopt opt-out vs opt-in (citizens must explicitly consent to become organ donors) defaults despite the difference in consent rates, an indication that opt-out defaults probably created artifically inflated consent rates.

For now, let us disentangle nudges from their troubled theoretical framework. In the next sections, we will attempt to operationally define a nudge.

What is a Nudge?

To begin, let us examine the definition provided by the OGs in their book:

Let’s reword that in plain English. To be considered a nudge, three conditions must be satisfied:

1. The context is intentionally altered to influence choice

2. The number of choices available is not restricted

3. The outcomes of those choices are not significantly altered

Let’s examine each condition in greater detail.

Deconstructing T&S’s Definition

1. Intentional Context Alteration

Choosing is behaviour, and it does not occur in a vacuum. It can only be understood in the context it is situated within. There are two aspects to understanding behaviour in context4. First, as previously written, outcomes select behaviour—choices tend to correspond with the best available outcome. Second, stimuli that predict these outcomes become important to us—they act as signals that guide us toward or away from these outcomes (we will refer to these as antecedents). These can be events or features in our environment, or our thoughts, feelings, and sensations. Crucially, antecedents are only relevant to us if they predict important outcomes—that is, they gain their power to influence behaviour through their associated outcome. Installing a recycling bin in a lift lobby can increase recycling of climate-conscious individuals, but will have little effect on those who are indifferent to climate change5.

In evaluating choices, we are constantly looking for such signals to guide our choices. Here’s an example provided in T&S’s book. Imagine you are suffering from a serious heart condition, and the doctor proposes an operation. When you enquire about the odds of survival, she says:

“Of 100 patients who have this operation, 90 are alive after 5 years.”

In an alternate universe, the doctor skipped her morning coffee and is feeling particularly sullen. She says instead:

“Of 100 patients who have this operation, 10 are dead after 5 years.”

You will be quick to realise that these two sentences are mathematically equivalent. However, the way each statement is framed signals very different outcomes. Unsurprisingly, individuals presented with either statement choose quite differently.

It is important to emphasise that this condition encompasses the entire universe of BCTs and even regulations, which most don’t think of as behavioural in nature (of course they are) and does not distinguish nudges from non-nudges. Here’s where most people get it wrong. To be considered a nudge, Conditions 2 and 3 must also be satisfied.

2. Choice Preservation

At first glance, this condition is straightforward. If we don’t restrict choices, we are good. But what does that truly mean?

There are 2 possible ways of thinking about this. First, choices are not concealed from the individual. If one were looking to influence food delivery app users to order healthier options, completely removing the fast-food options would violate this condition. Showing the healthier options first, followed by the fast-food optios would not. Better still, we can place the fast-food options in a separate tab requiring an additional response to access (e.g., clicking on a see more button). Of course, the more hoops one must jump through to access the fast-food options, the less clear it is that this condition is satisfied.

Second, choices are not restricted by coercion or aversive control through imposing costs substantial enough to influence behaviour. Most would agree that a total smoking ban would clearly violate this condition6. Creating and demarcating smoking and no smoking zones within a district would not. Again, as it becomes harder to access a choice, we become less certain that the condition is satisfied. If designated smoking zones are a short walk away, smokers might feel that their choice is not restricted. I have to walk an additional 5 mins to smoke, that’s fine. It’s probably good for me too. What if they had to walk 10 mins, or 20 mins, or 30 mins to the nearest designated smoking area? At what distance can we agree that the degree of inconvenience is effectively a restriction?

3. Outcome Neutrality

First, we should clarify that T&S used the term economic incentives rather than outcomes. For some, economic incentives can be narrowly defined as financial incentives or costs. Fines for smoking are precluded. Invoking social disapproval for smoking is fine (haha).

Even so, many (including the OGs themselves) still refer to interventions that directly programme financial incentives/costs as nudges. In their book, T&S describe a “dollar a day” programme, in which teenage girls already with children are paid a dollar for each day they are not pregnant. T&S also describe a cap-and-trade system to reduce greenhouse gas emission, in which companies who pollute below a threshold (the “cap”) are allowed to sell their remaining emissions rights (the “trade”) to companies who pollute more. This creates a financial incentive for companies to pollute less as they can profit by trading their remaining emission rights.

That said, narrowly defining economic incentives as those that involve financial gains/losses fails to acknowledge the diverse range of outcomes that influence behaviour, of which financial incentives are but one. For many, experiencing social disapproval can be more aversive than getting a fine, and some would readily pay to avoid such experiences. T&S acknowledged as much, stating in a footnote in their book:

Alert readers will notice that incentives can come in different forms. Some of our nudges do, in a sense, impose cognitive (rather than material) costs, and in that sense alter incentives.

A second less easily resolved issue in the wording is the inclusion of the word significantly. What counts as a significant incentive/cost? In the same footnote, T&S added:

Nudges count as such, and qualify as libertarian paternalism, only if any costs are low.

Obviously, in modifying the outcomes, if the incentives/costs are too low, the likelihood of influencing behaviour is minimal. No (or very few) smoker will quit if you pay them a dollar for each month they abstain from smoking. So, the change in outcomes must be in a sense, significant enough to influence behaviour, but not too significant.

In terms of providing incentives, one might say the “dollar a day” programme qualifies as a nudge as the monetary gain is trivial. However, a cap-and-trade system for sulphur dioxide in the US has led to estimated cost savings of US$700–$800 million annually on the back of strong financial incentives for companies to pollute less.

When looking at costs, with our smoking zones example, what travelling distance to the nearest smoking zone would be a low cost? At what point along the continuum can we distinguish a nudge from a ban?

We either accept that nudges include changing outcomes, and navigate those fuzzy boundaries, or limit nudges solely to BCTs that influence behaviours through altering the antecedent context.

Towards an Operational Definition

At this point, you might be feeling more confused than ever. Weren’t you supposed to provide clarity? you ask. First, take a deep breath. We’ll get through this, I promise.

In analysing the 3 nudge conditions proposed by T&S, we realise that they occasion more questions to be answered. For us to continue, some simplification is in order. In keeping with an interpretation broadly compatible with both T&S’s definition and a selectionist view, we will adopt a definition proposed by behavioural scientists Carsta Simon and Marco Tagliabue:

In this definition, the antecedent context is altered (Condition 1) but not outcomes of behaviours (Condition 3, partially), thus avoiding the use of coercion or imposing costs to restrict choice (Condition 2). To avoid troubles with determining what incentives/costs are acceptable for nudge-type BCTs, we will conveniently omit all BCTs that involve programmed changes to outcomes, whether financial or otherwise.

In summary, we began with an overview of Nudge Theory. We jettisoned its troubled theoretical framework and focused on the nudge concept. We scrutinised the 3 conditions contained within T&S’s definition and examined some of its conceptual issues. Finally, we proposed a new definition that keeps to the spirit (we hope) of T&S’s definition, but offers better clarity.

Armed with our refined definition, we are finally ready to evaluate actual nudge-type BCTs, which we will do so in Part II. We will also evaluate the evidence for their effectiveness, and discuss the role of nudge-type BCTs in a behavioural scientist’s toolbox. Stay tuned.

Part II:

Part III:

In 2015, I came across a clip of Michael League, bandleader of jazz fusion band Snarky Puppy, effusively praising Kendrick Lamar. What did a jazz dude see in a relatively unknown rapper1? I thought. Then, I listened to Kendrick’s To Pimp a Butterfly, and I understood.

Fast forward to this year, when Kendrick gathered a group of West Coast musicians together and delivered a show titled The Pop Out on Juneteenth, a day commemorating the end of slavery in the United States. At the end of the show, Kendrick busted out his Drake diss track/summer party anthem Not Like Us, and repeated it another 4 times. Kendrick had been openly feuding with Drake for several months prior to the show, and his message, as self-declared biggest Drake hater, could have focused on his personal vendetta. Yes, he did that, and more.

To close the show, he welcomed the musicians and audience onto the stage for a group photo, including members from two Los Angeles-native gangs, the Bloods and the Crips, intractable enemies engaged in a lethal decades-long gang war. Kendrick had transformed his personal vendetta into a show of peace and West Coast unity. This was remarkable enough that Snoop Dogg took a jaunt from smoking joints to declare Kendrick King of the West.

Today, we examine the dark arts by which Kendrick ascended the throne—the invocation of Us vs. Them.

Us vs. Them

Us/Them-ing is rampant in social animals, from chimpanzees, to wolves, social birds, and humans. The process is rapid and automatic, with two interlinked parts—favourable responses toward Us, and unfavourable responses toward Them. In humans, these Us/Them dichotomies operate in complex ways:

1. Us/Them can be evoked in arbitrary groups. Flip a coin to randomly assign people into two groups, and folks will favour others in their group. These types of groupings, minimal group paradigms, have been replicated in countless studies with similar results, a emphatic demonstration of the universality of Us/Them-ing.

2. Us/Them can be evoked by arbitrary stimuli. Humans have a wonderful capacity for assigning meaning to the most random stuff, endowing them with rich symbolism and deep meaning. Flags are a powerful example of this—rectangular coloured pieces of cloth that represent something sacred for many2.

3. We each belong to multiple Us-es. I am Singaporean, Chinese, a father, a behaviour scientist, Lord of the Rings nerd, and deeply love the mountains. I listen to Bach, Snarky Puppy, and Kendrick Lamar. Each is a category by which I can relate to another individual as an Us, or a Them.

4. Our Us/Them categories are fluid. We simultaneously share as many commonalities as differences with the next person. Sometimes, we find the most obscure bit of commonality with a Them (They rear isopods too!) which instantly transforms Them into an Us.

5. We often try to conceal our Us/Them-ing. Two things are true in large-scale cooperative societies—we all engage in Us/Them-ing and we mostly pretend that we don’t. A functional large society requires cooperation with those we view as Thems, and cultural norms reflect this, exerting pressure on the individual to deny overt Us/Them-ing.

6. Our evaluation of Us/Them dichotomies can vary wildly. Some us/them dichotomies are completely inconsequential. Yeah, they are vegetarian, cool. Others are tethered to core beliefs. THEY ARE EATING THE DOGS AND CATS!!

Kendrick’s Dark Arts

These processes are woven into all human interactions. We only need to pause for a moment to reflect on any of our interactions to realise how these processes operate on our behaviours, often inconspicuously. They certainly featured heavily when Kendrick brought the West Coast together, Bloods, Crips, and all.

Kendrick preached peace, love, and unity repeatedly throughout the show (points 2, 3, 4), and invoked West Coast symbolism and its legends (points 2, 3, 4). He put members of the feuding gangs together on the same stage (point 1), enlisting their cooperation for a group photo (point 5). To Kendrick’s credit, his consistency in promoting this vision of Us probably lent him a legitimacy that went a long way.

However, above all, I believe there is one process by which Kendrick effectively used to unite the West Coast (we’re straying into the forbidden arts territory here):

7. There is no Us without Them. I repeat, there is no Us without Them.

Strong altruistic tendencies toward Us are often accompanied by strong hostilities toward Them, termed parochial altruism. Converging evidence suggests our vast albeit selective (hence parochial) capacity for cooperation with Us folks co-evolved with intergroup conflict. A summary of this evidence is presented in an article by economist Samuel Bowles, aptly titled Being human: Conflict: Altruism's midwife.

Crucially, increasing the threat posed by Them (perceived or not), can lead to increased in-group cooperation. We see an example of this in increased popular support for national leaders and their causes during periods of crisis or war. There is even a term for this phenomenon—the rally 'round the flag effect. For example, George Bush’s approval rating increased from 51% on September 10, 2001, to an unprecedented high of 90% a week later, post-September 11 attacks. More than increases in presidential approval ratings, the attacks also catalysed support for the subsequent War on Terror, the ramifications of which are still felt today.

Throughout Kendrick’s very public feud with Drake, he spared no pains to portray Drake as literally not like us. In calling him an imitator, scam artist, paedophile, and (most damning of all) coloniser, Kendrick transformed his personal war into a cultural war with Them.

What Now? Was John Lennon Wrong?

So, there’s no Us without Them. There’s no cooperation without conflict. Can the world ever live as one, or are we bound by our evolutionary legacy of Us/Them-ing?

Bowles seems to think we can escape that fate. Jews, Christians, and Muslims have had a long and bitter history of conflict, and the many ongoing conflicts today provide a stark reminder that their differences will not be resolved soon. Yet, there are many instances, past and present, where people of these faiths live together in highly tolerant, cooperative societies. Singapore today, with sizeable Christian and Muslim populations, ranks highly on religious tolerance. In the past millennium, the Ottoman Empire, while engaging in external conflict with Christian empires, was relatively tolerant (by the standards of its time) of the Christians and Jews within its own empire3.

Thus, as Bowles articulates, our legacy need not be our fate. To end, I leave you with a powerful lesson from one of the most famous psychology studies, the Robbers Cave Experiment4.

In 1954, social psychologist Muzafer Sherif involved 22 boys aged 11–12 in a field study conducted at Robbers Cave State Park, Oklahoma. The boys were randomly assigned to one of two groups (a minimal group paradigm). First, they engaged in cooperative activities within their own groups, then competitive activities between groups. Soon, each group developed strong in-group favouritism, matched only in intensity by hostility toward the other group. There was name-calling, burning of flags (yup, flags again), and physical altercations between members of opposing groups. Effectively, Sherif had created an analogue virulent strain of the Us vs. Them-ing that plays out in the real world.

The final phase, and the validation of Sherif’s hypothesis, was to engineer reconciliation by setting up conditions which required cooperation between both groups, such as moving a stalled truck, and restoring their cut off water supply. As between-group cooperation, the distinction between Us and Them was blurred, and the boys began to develop friendships across group boundaries.

Concluding Thoughts

The Robbers Cave Experiment provides a clear demonstration that Us/Them boundaries can be shifted (point 4). More importantly, it demonstrates that we can achieve cooperation without conflict—neither the stalled truck and cut off water supply were Thems, but represented very real threats to the interests of the collective Us. What’s needed is a superordinate goal that requires the cooperation of the collective Us.

An amendment is in order:

Finally, recall our unique ability to tether meaning to arbitrary stimuli (point 2). We can frame the threats we face as Global Warming rather than Big Oil, Rising Healthcare Burden rather than Big Pharma, Poverty rather than the Man. This is not simply an exercise in semantics. Behind Big Oil, Big Pharma, and the Man exists not an amorphous evil, but individuals making their way through this tricky thing called Life, not unlike Us (point 3). If we are to have any hope of solving the many intractable problems we face, we need all of Us. This reframing is not an easy thing to do, we absolutely LOVE having a Them to blame. But damn if we don’t at least try in the spirit of Bowles, Lennon, and Lincoln.

We have previously introduced a selectionist view of the world. To briefly recap, traits vary, adaptive traits are selected and replicated and non-adaptive traits are not. In an organism’s lifetime, behaviours that lead to good outcomes replicate, and behaviours that do not are suppressed or undergo extinction. Many of our behaviours can be explained by these processes.

Often, each behaviour is the product of multiple selection pressures. Sometimes, these selection pressures come into conflict with one another. We have discussed one process by which this occurs in delay discounting. Sometimes, we eat too many brownies because they are too damn delicious, or we find ourselves still awake at 3 AM watching our 5th consecutive episode of The Last of Us the night before an important meeting. In both instances, the present self makes choices that the future self regrets. Analysing behaviour solely at the individual level typically suffices.

Yet, there are cases where we must go beyond analysing behaviour at the individual level. A passer-by leaps into a freezing river to save a drowning child. Mother Teresa forsook her comforts to serve the poor. Aragorn led a suicide charge so Frodo has a shot at destroying the One Ring.

Less heroically, a colleague of mine regularly donates blood. I restrict my meat intake to one meal per day to lower my carbon footprint. In each instance, the individual makes choices at their own expense for the benefit of others. That’s strange! If selection favours adaptive traits, why would these seemingly maladaptive traits persist? To answer, we must expand our unit of selection from the individual to the group.

Today, we will unpack group selection through three analogies—Monopoly, psychopathic chickens, and cancer—borrowed from the evolutionary biologist David Sloan Wilson. I hope I articulate them at least half as eloquently as he did. Needless to say, I hold Wilson in the highest regard.

Monopoly

Imagine you are playing a game of Monopoly with 3 others. The goal is simple—bankrupt them before you yourself are bankrupt. You acquire properties, manage your cash wisely, plot, scheme, and do anything to build and hold an advantage. You may form temporary alliances to take down an opponent who is ahead, but you will just as quickly turn on your allies if it advances your own position. Ultimately, your actions are completely self-serving.

Imagine now that the rules are slightly tweaked to create a 2 vs. 2 game—bankrupt the opposing pair before both you and your partner are bankrupt. Pause for a moment to consider the kinds of behaviours you will engage in now. If you are anything like me, your goals and actions will radically differ from the single-player version. In place of me goals, you now have us goals. How do I get ahead is replaced by how do we get ahead. If you cared to notice, other behaviours tangential to the game itself also emerge. You find yourself smiling at your partner, giving them high fives, then fist bumps, then secret handshakes. A warm feeling of fellowship spreads through your body. At the right moment, a blasphemous thought might even enter your head—I’ll sacrifice myself and go bankrupt if it means my partner crushes them.

We have arrived at our first axiom:

When group selection pressures are sufficiently strong, some behaviours that appear maladaptive at the individual level surface. Conversely, other behaviours that appear adaptive at the individual level are suppressed (the plotting and scheming continues, but not at my homie). Taken together, we have the rudimentary beginnings of complex group phenomena such as cooperation, altruism, and morality.

Psychopathic Chickens

Imagine you are a poultry farmer, and you want to increase egg production in your brood. What is your strategy? Conventional wisdom has it that you will pick the most productive hens, use them to breed the next generation, and so forth. After a few generations, you should expect to possess a brood of egg laying machines. In the 1990s, biologist William Muir and his colleagues did just that. In the span of a few generations, the conventional wisdom hypothesis was thoroughly refuted. Instead of laying more eggs, each subsequent generation laid fewer eggs. Whatever the cluck happened here?!

Turns out, the most productive hens were also the biggest bullies. They achieved their productivity by aggressing towards other hens. When the bullies were put together, they went to town on each other, leading to a steep decline in egg laying. Each subsequent generation grew more psychopathic than the last. You can see an image of one of the cages after five generations of breeding below. Of the 9 hens the cage originally housed, 6 were murdered, and the remaining 3 had stripped each other of their feathers.

In a parallel experiment, Muir and his colleagues monitored the productivity of entire groups of hens and selected the groups with the highest productivity. As you can by now guess, productivity increased greatly after a few generations, the hens did not murder each other, and they sang Kumbaya in Henglish. Here is an image of one of the cages after five generations, with all hens alive and fully feathered.

With that, here’s our second axiom:

The similarities between our tale of hens and our own organisations and network systems might be immediately apparent to some of you. Some of you may be in groups similar to the psychopathic chickens, groups that select for individual achievement and selfish traits. Often, there is intense within-group competition, resulting in a few winners (our three unfeathered hens), many losers (our six unfeathered and dead hens), and a severely dysfunctional group. Other groups are like our Kumbaya chickens, selecting for group-level achievement. We don’t see many winners or losers in these groups, but we see a strong, cooperative group.

In reality, most groups fall somewhere in between—individual achievement is rewarded, as is group success. No one wants their own achievements to go unnoticed, and the fate of a group is determined by outcomes of the collective efforts of its members. Further, placed in an environment promoting individual achievement, not everyone turns into a psychopath. Some folks absolutely have good intentions, and individual success does not always have to come at the expense of others. The interplay between individual and group selection is complex. It is important for groups to critically evaluate and evaluate often their processes and the corresponding selected behaviours.

One final point before we move on. A while back, I was having a discussion with some friends on diversity in government. One of them believed that competency should be prioritised over all else, including voter representation. In short, governance should be based on competence, not populism. I fully agree that our government should comprise the most competent individuals. The challenge lies in defining competency. To some, we should select high status males. I suspect, for my friend, the selected group will be mostly male, Chinese (the majority ethnic group in Singapore), likely to have attended the same elite schools, worked in the same places, and have very similar lived experiences. I am inclined to disagree with this selection, but he may be right. Either way, our axiom holds true—be careful what you select for.

Cancer

First, a short detour before we discuss cancer. In our Monopoly and psychopathic chicken examples, we examined selection at two levels—the individual and the group. Life is far more complicated than that. Groups can be nested within bigger groups, themselves nested within even bigger groups, so forth. When I served in the army, I was in a team, within a platoon, within a company, within a battalion, within a brigade, within a division, within the army, within the Singapore Armed Forces. Zooming in, each of us consist of smaller groups of cells (including many not our own) that comprise yet smaller groups down to the most basic self-replicating unit of the gene.

At each level, new selection pressures are introduced that compete with selection pressures at lower and higher levels. In short, group selection occurs recursively across multiple levels. For our third axiom:

Onward to cancer.

Each of us consists of trillions of cells that work together to sustain us. Our cells routinely divide and differentiate to replace other cells that die. Occasionally, mutations occur that allows a cell to grow rapidly, avoid programmed cell death, and even increase the rate of subsequent mutations. Cancer provides a fascinating insight into the tug-of-war between selection pressures at different levels. These adaptations are obviously very good for the mutated cell and its lineage, and bad for us. Our bodies react to nullify these cells, failing which, we die.

Our cancer analogy applies for across multiple level-pairs, including cell-individual, individual-small group, and small group-larger group. At the lower level, the goal of the unit is to outcompete other units by behaving selfishly. At the higher level, the goal of the group is to reduce selfishness and ensure cooperation among its constituent units. After all, a group in shambles has scant hope of outcompeting other groups.

We have arrived at our final axiom. In the words of David Sloan Wilson and E O Wilson (no relation):

Once we understand this, we begin to see it everywhere. We have seen an example of this with our psychopathic chickens. Within group, the three remaining hens produced more eggs than their dead counterparts (and they didn’t die), but the Kumbaya group beat the psychopath group handily. An interesting example is unfolding today in the United States. Like mutated cells, fringe groups with their own agendas can be found in both major political parties. In the case of one, a fringe group has seized control over the party. It remains to be seen if this party can still effectively compete for votes.

In Summary

We have journeyed through Monopoly, psychopathic chickens, and cancer. In the process, we have learnt how different behaviours are selected at individual and group levels, why we must be careful what we select for, and how selection occurs recursively across multiple nested levels. Perhaps most crucially, we've learned that while selfishness may triumph within groups, altruistic groups ultimately outcompete selfish ones.

These insights offer more than just explanations for observed behaviors—they provide a powerful framework for shaping our future. As we design our institutions, craft our policies, and build our communities, we have the opportunity to consciously consider the selection pressures we're creating, and how selection pressures at different levels interact with each other.

Thaler concluded that failure of self-control was to blame. Humans more often find ourselves mastered by our impulses than masters of them, a simple fact that many economists, in the grip of theory-induced blindness, had failed to grasp. With the availability of immediate gratification, even Thaler, as versed in the arts of utility maximisation as he was, let alone his unsuspecting guests, was not able to make the choice he really wanted to, to save his appetite for the delayed but greater pleasure of dinner.

Living for the Moment

Every human has on some occasion (or many, let’s be real) thought or uttered aloud I should… and proceeded to do the opposite. Given two behaviours that produce outcomes of different values, we often choose the behaviour that produces the less optimal outcome. We choose strawberry ice-cream over strawberries, scroll through TikTok videos at 2 AM on the eve of an important presentation, splurge on new clothes we don’t need, or, you know, climb some really big walls without protection.

Perhaps the most famous example (and possibly most entertaining) of failures of self-control is the marshmallow test, led by Walter Mischel at Stanford University. The basic test is simple—young children were offered a choice between eating a marshmallow immediately or waiting 15 mins to receive two marshmallows instead. Here is a replication of the study:

Just as in the video, the success rate was abysmal. Perhaps the more surprising finding was that some children succeeded, often in agonising fashion. While these findings were intriguing, Mischel led a series of follow-up studies spanning four decades that elevated his status from an accomplished academic to a bona fide superstar. In those studies, Mischel and colleagues found positive correlations between being able to delay gratification and a diverse range of outcomes, including higher SAT scores, higher sense of self-worth, lower BMI, and lower drug use1.

Common among these examples is a systematic devaluation of delayed consequences. When given a choice between a smaller-sooner reward and a larger-later reward, we gravitate towards the former. Conversely, when given a choice between a smaller-sooner aversive outcome and a larger-later aversive outcome, we gravitate towards the latter. In technical terms, we call this delay discounting. Colloquially, impulsivity or failure of self-control. Delay discounting has been observed in numerous studies, in various animals, humans included, across a variety of choice paradigms. Everyone exhibits delay discounting2. It is as close to a principle of behaviour as one can get. But wait, you say, none of this is anything new. Those sages of old, from Aristotle to Marcus Aurelius, knew it, and so did my grandma. In broad strokes, you are certainly not wrong.

We know that at the heart of many of the problems we face today, from the trivial to the existential, lies choices between smaller-sooner consequences and larger-later consequences. However, to have any hope of addressing these problems, we must go beyond knowing as your grandma does (no disrespect to her wisdom), to knowing in a deep sense of the word, based on carefully accumulated evidence. In the following sections, we will outline the current state of knowledge of delay discounting, explore some of its possible functions, and finally, consider some possible applications of this knowledge. Let’s dive in.

What We Know of Delay Discounting

First, a brief summary of how delay discounting is typically studied in humans using hypothetical choice tasks3. Imagine you are offered a choice between $1,000 in a month and $1,000 today. Which will you choose? The latter, duh. What if the choice is between $1,000 in a month and $990 today? What if the choice is between $1,000 in a month and $980 today, or $970? Through a series of such choices, we arrive at a value (let’s say $950) where you are equally likely to accept the $950 today and $1,000 in a month. This value is called the indifference point. In simple terms, the value of the reward has decreased by $50 due to the 1-month delay. The choice task is repeated at different time delays (e.g., 3 months later vs. today; 6 months later vs. today). A picture emerges of how you value rewards at different times in the future. As a final note, you can substitute the rewards for losses (losing $1,000 in a month vs. losing $990 today), or for other stimuli (e.g., food, cigarettes).

Now, let us look at some important findings that have emerged from these studies:

1. Delay discounting follows a hyperbolic function

When we have obtained several indifference points, we can plot a graph of your various indifference points, which looks something like this:

The graph typically follows a hyperbolic function. In simple terms, the subjective value of an outcome falls quickly in the early delay period but more gradually for longer delay periods. If your discounting rate follows the graph above, the subjective value of the reward after a 3-month delay is $770. Remember, this means you are equally likely to choose between $770 today (T₀) and $1,000 in 3 months (T₃). This represents a steep 23% drop in subjective value! However, the difference in subjective value between the indifference point at T₃₀ and T₃₃, which also happen to be 3 months apart, is much smaller ($250 vs. $233, or a less than 10% drop in subjective value).

For the math-inclined folks, the above graph can be obtained by applying the following formula:

V_T is the subjective value, A is the reward amount, k is the discounting rate (0.1 for the above graph), and T for the time delay4.

2. Hyperbolic delay discounting predicts preference reversals

In a perfectly rational world populated by economists, everyone’s preferences should remain consistent over time. Alcoholics, gambling addicts, binge eaters never experience regret. There is no morning-after clarity after a night of excessive indulgence, and solemn vows of never again, all swiftly forgotten as the next opportunity to sin presents itself. What gives?

Consider these two discrete choices:

1. A choice between $100 today and $110 in 1 year

2. A choice between $100 in 10 years and $110 in 11 years

If, like many, you find yourself choosing $100 today in the former and $110 in 11 years in the latter, you’ve expressed a preference reversal. This is odd! After all, the choices are of identical values ($100 vs. $110), as is the time window between choices (1 year).

Preference reversals are predicted by hyperbolic delay discounting. Recall that the decrease in subjective value of an outcome is steeper in the beginning, and flattens out over time. Compared to the present, $110 in a year’s time is discounted steeply such that it is valued less than $100 today. Or so the thinking goes, I can buy a lot of popcorn with $100 today. Compared to a decade from now, $110 in 11 years’ time is discounted much less steeply such that it is still valued more than $100 in a decade’s time. I’m already waiting 10 years, what’s another year?

3. We discount less steeply when the stakes are high

Consider these two discrete choices:

1. A choice between $5 today and $10 in 1 year

2. A choice between $5,000 today and $10,000 in 1 year

You might find yourself choosing $5 today in the former and $10,000 in 1 year in the latter, even though the durations and comparative difference in values (a 2x return) are identical. Up to a point, as the stakes get higher, we tend to discount less steeply—a case of might as well take the $5, it’s just pocket change vs. $10,000 is A LOT of money.

4. We discount losses less steeply than gains

Consider these two discrete choices:

1. A choice between gaining $500 today and $600 in 1 year

2. A choice between losing $500 today and $600 in 1 year

You might find yourself choosing the $500 gain today in the former and $600 loss in the latter, even though the values and time window are identical. That’s because we tend to discount losses less steeply than gains. This is also known as gain-loss asymmetry or sign effect—a case of losing $500 is too painful to think about, let’s kick the can down the road. Again, adding an equivalent and sufficient delay to both loss outcomes (e.g., losing $500 in 10 years vs. $600 in 11 years) can produce a preference reversal.

5. Delay discounting ≠ impulsivity

It is tempting to equate our preference for smaller-sooner over larger-later options with impulsivity. Some scholars of delay discounting have done just that.

However, we should note that impulsivity describes a cluster of behavioural tendencies, while delay discounting describes one possible underlying mechanism that gives rise to such tendencies. There are other processes at work. To illustrate one, we go back to Mischel’s original marshmallow tests. In one of the experiments, the children were again presented with a marshmallow and the same choices, but with a slight tweak. For some of the children, the marshmallow was hidden under an opaque tin. Those children were able to wait longer, with many successfully waiting for the full 15 mins. Many of us will be able to relate similar experiences we had. Turns out that temptations have their greatest influence not within reach, but within sight5.

Also, delay discounting does not just describe those I should scenarios. A good number of smokers take no issue with their smoking habits. Most people do not consider the environmental impact of their single-use plastics usage, or have briefly considered it and moved on with life. Many nations and organisations seem to have no qualms about making present-centred choices that can lead to catastrophic delayed outcomes.

Clearly, we must go beyond equating delay discounting with something those other weak-willed folks do, to understanding that it pervades many aspects of our lives.

Why We Discount the Future

There are several posited reasons why smaller-sooner outcomes dominate larger-later outcomes. Here are some:

First, unlike in most delay discounting experiments, real-world delayed outcomes are rarely guaranteed. A promised future return on an investment does not always bear fruit. Not all smokers develop lung cancer. Insofar as uncertainty and probability is involved, greater uncertainty and lower probability regarding the future outcome should lead to greater discounting. This potentially explains why low-income populations, with cloudy prospects, seem to exhibit higher rates of discounting (more on this in the future). Sometimes, a bird in the hand is truly worth two in the bush.

Many larger-later outcomes result from the aggregation of smaller-sooner choices. One cigarette does not give you lung cancer, nor does one porterhouse steak herald the climate apocalypse. And so we slide down that slippery slope.

Choosing larger-later outcomes can sometimes represent an opportunity cost. If a chimpanzee chooses to receive food earlier rather than wait, it can invest this energy to put itself at a competitive advantage for acquiring food later. An individual who chooses the sooner monetary reward can invest it in tools or education, that can create value greater than the delayed outcome.

Perhaps unique to humans, our complex social organisations can sometimes result in contingencies that favour smaller-sooner outcomes at the group level. Often, far-sighted CEOs who choose to invest in long-term initiatives rather than maximising immediate profits and shareholder value find themselves swiftly replaced, as investors’ patience wear thin. Governments that push painful but necessary policies (e.g., austerity vs. further deficit spending) find themselves voted out come the next election. Solving these challenges require the exceedingly difficult task of restructuring complex group level contingencies.

What We Can Do

Many popular books have been written about improving our self-control and managing our lives better, including dealing with smaller-sooner vs. larger-later choices. Instead of rehashing the many brilliant ideas offered in these books, let’s turn our attention instead to how our newfound knowledge of delay discounting can address some societal level problems. I will use the problem of obesity (in particular, our relationship with food) as a motivating example, but you can start thinking about other kinds of problems where these strategies can apply.

1. Create near-term consequences for choices

Recall how outcomes lose their subjective value over time such that sizeable but delayed outcomes (e.g., type 2 diabetes, heart disease) have little influence relative to immediate choices. It makes sense then that engineering near-term consequences can have a far greater impact on choices. A simple yet robust intervention that has emerged from addictions treatment research, called contingency management, is to provide near-term (and often small) rewards for making desirable choices.

As an example, here in Singapore, the Health Promotion Board has rolled out a healthier choice program, where grocery purchases that carry a healthier choice symbol (including foods lower in salt, sugar, and fat) allow the buyer to earn points that can be exchanged for cash vouchers.

Conversely, one can also provide near-term aversive outcomes for making undesirable choices. One such example is stickK, a platform for users to commit to a well-defined near-term goal (e.g., cook my own meals twice a week) and stick with it. Users pledge a sum of money towards attaining their goal, and failure to attain their goal results in the money being forfeited to a charitable organisation of your choice. One of the most wicked and therefore probably most effective feature is the option for the forfeited money to be donated to an anti-charity, or a cause that the user abhors (e.g., the NRA Foundation for a gun control advocate).

2. Create effective messaging

As a sucker for all things chocolate, when presented with an opportunity to indulge in a delectable, gooey brownie, I can assure you I often do so without even a second thought. Sometimes, despite our ardent New Year resolution to forsake gluttony, the delayed outcome choice doesn’t even enter our consciousness. We fall victim not to a failure of willpower, but to a failure of attention.

In such moments, cues that draw our attention to the delayed outcome (and our goal) can be useful, key being that the cue must be present at the moment of choosing. Other than the healthy choice symbol, the Health Promotion Board has rolled out another nutrition cue for beverages, called Nutri-Grade. Beverages are assigned a grade, printed on their packaging, corresponding with their sugar and saturated fat levels. The goal of such labelling is to increase the salience of the delayed outcome, and hopefully also create an immediate, aversive condition for choosing beverages with poor grades.

As discussed earlier, we discount losses less steeply than gains. A loss influences our behaviours more than an equivalent gain. Consider two frames of identical values:

1. If you maintain a healthy weight, you can expect to live up to 10.3 years longer

2. Being obese can reduce your lifespan by up to 10.3 years

For many, the second message will have a greater impact on your food choices. Again, the proximity of the message to the moment of choosing is key in determining its effectiveness.

3. Be on the right side of preference reversals

One common policy that accounts for preference reversal is the implementation of cooling-off (or free-look) periods, typically for big-ticket purchases. A cooling-off period is a time window following a purchase when the consumer can choose to cancel and refund a purchase. Lured by the siren call, consumers are prone to making big purchases only to experience a gut-wrenching preference reversal when they wake up the next morning (I know this feeling too well). Cooling-off periods thus serve to protect consumers from the aftermath of their fundamentally unsound choices.

For food, cooling-off periods are not feasible. You can’t uneat food. Creating conditions for being on the right side of preference reversals are trickier. So, here’s a little thought experiment, indulge me for a moment.

Imagine being at McDonald’s and choosing between 2 food options, a side French fries (the unhealthy option) and a cup of corn (the healthy option). Recall how, all else being equal, we sometimes prefer the French fries and other times the corn, depending on the delay to the choices. Specifically, the immediate gratification of salty, fatty goodness has an outsized influence on our choices. One solution is to introduce a delay between our choices and their outcomes. The longer the delay, the greater the effectiveness6.

Let’s assume that the average waiting time at a McDonald’s outlet is 5 min. Here in Singapore, there is an array of McDonald’s meal options categorised as Eat Light Under 500 Calories. Now imagine a separate priority queue for choosing one of these options compared to other menu options. As the kitchens divert their resources toward fulfilling eat light orders, the average waiting time for the non-priority queue orders creeps up to 10 mins. As a result, more diners switch to eat light options, creating a self-reinforcing loop. The average waiting time for the non-priority queue creeps up further to 15 mins. Ah what the heck, you mumble to yourself, might as well get the healthier option. I really should eat healthier anyway.

In this scenario, we’ve not only introduced a delay between the unhealthy choice and its outcome, but also reduced the delay between the healthy choice and its outcome, creating a compelling set of circumstances to engineer a preference reversal. Of course, such ideas must be tested for effectiveness and feasibility before making any conclusive claims.

Conclusions

By now, we should begin to recognise that many choices we face in our everyday life involve trade-offs between smaller-sooner and larger-later outcomes. In the many cases where smaller-sooner and larger-later face off, smaller-sooner tends to have the prettier footwork and sharper jab. We’ve learnt a little about why this is so, and some things we can do to give larger-later a decent fighting chance.

Thanks for reading! Keep fighting the good fight.

Evolution in a Nutshell

Consider these three environments and three species of birds that inhabit them, all of which descended from a common ancestor. Each species has come to possess traits uniquely adapted to their environment. The greater roadrunner has a brown plumage (great for camouflaging), excretes excess salt through a special gland near their eye (loses less water than urine), flutters the unfeathered area beneath the chin (dissipates heat), and drastically reduces metabolism at night (conserves energy and reduces heat loss). The ptarmigan has brown feathers in summer and white feathers in winter (great for camouflaging), possesses large fat reserves (provides energy and insulation), and has feathers around its nostrils (warms the air it breathes) and on its soles (insulates against the cold ground). The black-capped lory has a strong, hooked beak (perfect for cracking open seeds and nuts), hair-like structures on its tongue (maximises nectar uptake), and sturdy feet (excellent for perching on trees). Like many other tropical birds, it is also strikingly coloured. As to why, if you wonder, possible explanations include in a highly biodiverse environment, there is a greater need to stand out, and the more favourable climate and greater availability of food allows greater investment of energy into colourful plumages.

The discovery of the processes of variation, selection, and replication, are attributed to Charles Darwin (and to a lesser extent Alfred Wallace), who as a young naturalist and geologist, embarked on a multiyear voyage around the globe beginning 1831 aboard the HMS Beagle. On the expedition, he collected flora and fauna specimens and meticulously documented his observations of similarities and differences between the wildlife he encountered. From his systematic observations, Darwin acquired an insight into how species change over time, which he developed into a theory of evolution through natural selection, culminating in his seminal work On the Origin of Species, published in 1859.

Evolution theory continued to develop and gain acceptance, bolstered by the discovery of genes and DNA. By the 1970s, evolution theory had reached the apex of biology as a grand unifying theory to explain all biological phenomena, leading the geneticist Theodosius Dobzhansky to declare that nothing in biology makes sense except in the light of evolution. At the time, the en vogue train of evolution thought was a gene-centric view, best exemplified by the (still) influential work by Richard Dawkins, The Selfish Gene. The gene-centred view, as eloquently argued in The Selfish Gene, posits that variation, selection, and replication occurs solely at the level of the genes. Organisms exist to as vessels for genes to replicate. A chicken is an egg’s way of making another egg. In other words, whatever we think, feel, or do, is determined by our genes for this sole purpose.

From Genes to Behaviour

Evaluating the truthfulness of a gene-centric view would require a whole article on its own. For now, let us refocus our attention on the organism. Organisms do not just possess physical traits. They also interact with their environment, operate upon it. In other words, they behave. Our behavioural traits are subject to the same selection processes of variation, selection, and replication. In our evolutionary history, behavioural traits varied. In our ancestors, adaptive traits such as languaging and social behaviours were selected for and replicated. Non-adaptive traits were not replicated, and traits that previously were but have ceased to be adaptive such as tree climbing ceased to be replicated (just don’t tell this guy).

Certain behaviours are so critically adaptive in our evolutionary history that they are innately wired in us. We come preloaded with them, so to speak. We flinch when an object moves rapidly towards our face. We salivate when we smell food. We display a preference towards food, sex, warmth, social affiliation. These innate repertoires of behaviours require no learning and are largely stable throughout our lifespans, although modifiable to some extent (individuals from different cultures can react very differently to a whiff of fermented shark meat or durian). 

However, the vast majority of behaviours in adult human repertoires (and many other species) are learnt within our lifespans in response to environmental events. To understand why, let us consider two games—tic-tac-toe (or noughts and crosses), and Go. In the former, the game is relatively simple. With a few programmed rules, the game is essentially solved and you can never lose. Similarly, in a theoretically simple environment, organisms can successfully thrive with a few pre-programmed behaviours. Go, on the other hand, is an exceedingly complex game, with the number of possible moves thought to surpass the number of atoms in the universe by many orders of magnitude. Unlike tic-tac-toe, it is impossible to programme rules for every single position in Go. In fact, it was widely believed mastery of the game required human intuition, and that computers could never defeat a professional Go player. Incidentally, in 2016, the computer programme AlphaGo developed by Google DeepMind managed to do so, defeating one of the game’s GOATs, Lee Sedol. The match was captured in a beautiful documentary (watch it, it will be the best use of your time this week). The complexity of our environments is analogous to a game of Go, except at random intervals, the board size changes, pieces are removed, positions are swapped, new opponents are substituted in, and new rules are added.

Complexity demands dynamic and highly sophisticated levels of adaptability. By the time children are a few years old, their acquired repertoire of behaviours will have vastly exceeded their innate repertoire. Consider again the three environments earlier, to which we add a fourth, familiar to many of you. These environments exert very different selection pressures on the humans living in them. What sort of behaviours and products of behaviours are required to survive in each environment?

Unsurprisingly, we see very different behavioural repertoires emerge that are uniquely adapted to each environmental context. More importantly, these behavioural repertoires are dynamic rather than static. Take a white-collar executive from urban, tropical Singapore and place him in the Arctic tundra, and we will observe near-instantaneous, dramatic shifts in his behaviour, or a quick death. In his book The Secret of Our Success: How Culture is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter, evolutionary biologist Joseph Henrich recounts the fate of early European explorers who found themselves stranded in inhospitable environments, including tundras, deserts, and swamps. Many perished, but a few survived, made possible only through great adaptations in their behaviours to their new environments. It is important to also note that the survivors were often aided in no small part by indigenous populations and their vast stores of cultural knowledge (more on this in the future), who not only survived, but thrived in those environments. Inhospitable, as it turns out, just means pre-adaption. 

A Selectionist View of the World

In contrast to the gene-centric view, here we have evidence of behavioural selection at the unit of the individual organism (selection can occur still on other levels, a concept known as multilevel selection—again, more of this in the future). Behavioural selection was studied extensively in animal models, then with humans beginning with the pioneering work of Edward Thorndike, John B. Watson, and later, B. F. Skinner. The study of variation, selection, and replication of behaviour in response to environmental conditions, also known as operant learning, coalesced into a natural science discipline in the behavioural sciences, known today as behaviour analysis. The core tenet of behaviour analysis is simple—the outcomes (or consequences) of our behaviours select for our behaviours, powerfully so. When a behaviour leads to a desirable outcome, it is more readily expressed. When it leads to an undesirable outcome, it is less readily expressed. The influence of these consequences can be attenuated by physiological processes such as sensitisation, habituation, or attentional constraints, but make no mistake—just about everything we think, feel, do, is bound by the same selection processes, often beneath conscious awareness. To understand any given behaviour, we need to understand the conditions which select for it, both in the organism’s present and its past.

Although Thorndike and his mentor William James were very much influenced by evolutionary thought, behaviour analysis was largely separated from evolution theory in its early development. Later in his career, a growing realisation of the consilience between behaviour analysis and evolution theory led Skinner to publish perhaps his most influential paper Selection by Consequences in 1981, integrating the principles of behaviour with a selectionist view. At this point, it is important to disabuse the reader of an idea that has taken hold in some—that while our great capacity for such dynamic, sophisticated adaptions was selected for in our evolutionary history, it has given rise to systems of knowledge and practices that now allow us to transcend evolution in some way. We haven’t. If anything, these continually advancing systems of knowledge and practices (evidence of continued selection) gives us some slight hope for the survival of our species against the many extinction-event selection pressures that will certainly come our way.

What promise does a selectionist view hold in our curious world? At this moment, Simone Biles is (again) living her Olympic dream at the ripe old age (for a gymnast) of 27; other dreams are being crushed in conflict zones; groups of people are arguing about climate change action, or whether climate change is real at all; some countries are grappling with soaring obesity rates; others are still grappling with malnutrition. It is the hope of this newsletter to persuade you that these outcomes of actions by individuals or groups of individuals can be better understood if we adopt a selectionist view.

As evolutionary biologist David Sloan Wilson repeatedly puts it, the theory decides what we can observe. By extension, the tool decides what we can change. Both are needed. A chisel is no more useful in the hands of a baker, than yeast and flour in the hands of a sculptor. In this newsletter, we will review theories (all nested within the grand unifying theory of evolution) and tools to understand and change behaviours. These theories and tools have withstood the great selection pressures of vigorous scientific research. They are necessary, but by no means sufficient to solve many of the complex challenges great or small mankind grapples with. My hope is that they serve you well (if not, in keeping with a selectionist view, discard them) as you navigate the intricacies of human behaviour in whichever direction you endeavour. Stay tuned.