Sendhil Mullainathan and Eldar Shafir’s Scarcity: Why Having Too Little Means So Much is full of interesting insight and experimental results. It presents a novel way of looking at scarcity that extends beyond the typical analysis in economics, the original “science of scarcity”, and will certainly change the way I think about it.

But by the time I reached the end of the book, I was not entirely satisfied. I have plenty of new buzzwords and some interesting experiments to talk and think about, but I’m not convinced I have been presented with a coherent new perspective on how the world works. Scarcity reminded me of a Malcolm Gladwell book – I got presented with a lot of cool results, they were spun into an interesting narrative that has made me think, but I don’t buy the authors’ main message.

So first, to the buzzwords, which added to the Gladwell-esque feeling. Scarcity is having less time, money, friends or packing space than you feel you need (Note the subjective element – scarcity could be caused by relative rather than absolute scarcity. And it can apply across a range of domains). Scarcity can have good effects, such as the focus dividend when scarcity captures the mind. It can make us experts in the area in which we are scarce, with poor people better able to judge the value of a dollar saved despite the context in which it appears. However, scarcity also causes us to tunnel, which is to focus single-mindedly on the scarcity at hand, potentially at the neglect of more important or less timely demands. Scarcity also reduces bandwidth, a combination of  cognitive function and executive control. Scarcity imposes a bandwidth tax. When people suffer from scarcity and tunnel, they are also forced to juggle, as they move from one pressing task to the next. The way to avoid scarcity is to have some slack, an untapped budget we  can turn to in times of need.

The most salient example of the negative effects of scarcity comes from a study in a New Jersey mall. Mullainathan, Shafir and Jiaying Zhao presented people with a scenario where they need to fund some car repairs. They then gave the participants tests for fluid intelligence and cognitive control. When the cost of the repairs was $150, high and low-income people scored similarly in the tests. But when the shortfall was $1,500, the performance of the poor plunged – the equivalent of losing 13 or so IQ points. The authors point out that this result has been replicated many times, suggesting it is a robust result. By causing the poor to focus on their lack of resources through a high and potentially unmanageable repair bill, the poor’s bandwidth for completing the tests was taxed. In another experiment, people were effectively made rich or poor by the flip of a coin, with the poor demonstrating greater present bias. In that case, the bandwidth tax was clearly not due to the inherent traits of the poor.

Mullainathan and Shafir take these results to mean that we don’t need talent or inherent trait-based explanations for differences between the rich and poor. Instead, scarcity makes the poor perform poorly, leaving them in a scarcity trap. They suggest that the effect of scarcity on bandwidth is a good explanation for differences as it can make sense of diverse empirical facts across behaviour, time and place. But it leaves open the question why they imply that scarcity induced and inherent trait-based explanations are mutually exclusive, or why inherently low bandwidth can’t explain an equally diverse set of facts.

Part of the difference between their and my interpretation is that they are observing a short-term dynamic and extrapolating that to the longer term. My view is that over the longer-term, inherent explanations play a larger role.

For example, when we look at twin studies, we find that IQ and other traits inherently differ between people. Adoption studies show long-term outcomes are representative of biological and not adoptive parents. Differences in scarcity of financial or other resources due to differences in adoptive parents have almost no effect on IQ, income, obesity or a range of other long-term outcomes.

Greg Clark’s work on social mobility shows the long-term persistence of status across many generations, despite short-term shocks that would affect scarcity. If Mullainathan and Shafir’s thesis were true, a shock one generation would carry through future generations, rather than seeing the next generation reverting to the underlying status (genotype). Under Mullainathan and Shafir’s explanation, we would also see immigrants’ IQ increase when they move country and ease their scarcity (although if scarcity is relative, perhaps their relative position may not have improved). We would see large increases in the IQ of previously poor lottery winners, who would experience a sudden surge in mental resources. In The Son Also Rises, Clark pulls together a few examples of windfalls of this nature and demonstrates the lack of long-term effect. In one case, a lottery of land parcels had no effect on the outcomes of the winners’ children.

As a result, I am not convinced that their arguments truly capture the differences between the poor and the rich, or the rushed and the relaxed. Perhaps someone might eat more from time to time due to the bandwidth tax. They might take an ill-advised payday loan when they are stretched for money. But despite this short-term effect, we see little trace of it in long-term outcomes. Something is allowing some people but not others to break the cycle of scarcity.

I also doubt that Mullainathan and Shafir’s description of the poor as suffering from scarcity is generally true. When it comes to time, the poor watch more television, invest less time in caring for their children, have plenty of free time to think about what they will eat, and yet are more likely to be obese. Their characterisation of the poor having a lot on their mind whereas the rich are relaxed despite their more complex employment does not seem particularly strong.

The book is least satisfying when Mullainathan and Shafir start pulling up examples of scarcity from other domains – the padding to turn an interesting idea into a book length argument. Many of them are banal and simply involve scarcity in the way economists might think of it, with no evidence that people were suffering anything more than a lack of time. For example, they talk of the Benihana restaurant, where the chef cooking in front of people sets a quick pace for the meal, allowing more customers to come through. It is a story of scarce time, but provides no interesting insight into their thesis. The fact the Benihana restaurant management took to the time to solve their problem suggests they were not overly taxed. Mullainathan and Shafir discuss the crash of NASA’s Mars Orbiter, which had to be launched by a certain date, leading to shortcuts. But what is the evidence that there was tunnelling or taxed bandwidth as opposed to a simple lack of time to do all the checks they would have liked to have done? They don’t present the evidence to distinguish.

When we turn to solutions, it is interesting that many of them do not depend on Mullainathan and Shafir’s argument being true. They are solutions that would be equally useful in dealing with inherently untalented or impatient people. Savings reminders could be useful regardless of the cause of the lack of foresight. Presenting pay day loans in terms of dollars rather than interest rates is standard behavioural economics fare and may work for people lacking bandwidth regardless the cause.

The short-term fluctuation of bandwidth does present some interesting possibilities. They suggest that if education fails due to low bandwidth, we should time education when people can best learn. They also give an example of Kenyan farmers failing to use fertiliser and missing out on large gains to their yields. By getting farmers to pre-purchase the fertiliser when they were flush with cash after harvest, more benefited from the yield gains. (Although again, are their decisions because they are inherently shortsighted or taxed?)

Ultimately, the long-term solution to the costs of scarcity is creating bandwidth and a buffer stock of slack. This makes sense, but this point drew me back to an example early in their book. They describe an experiment involving Indian vendors who were provided with money to allow them to escape loans with exorbitant interest rates that consumed much of their income. But despite having their debts cleared, bit by bit they fell back into the scarcity trap. Mullainathan and Shafir accredit their return to the trap to shocks. But why did they not save what they would otherwise have been paying in interest to create a buffer from shocks. Why did the new bandwidth not allow at least some of them to escape? Their income had effectively doubled.

One interesting idea Mullainathan and Shafir leave lying around is whether the bandwidth of whole economies can fluctuate through good times and bad. As a random idea of my own, is the Flynn effect due to the easing of scarcity in the modern world? [No]

Having said the above, the ideas in the book are well worth considering, especially for contemplating how short-term mental capacity might be affected by the environment. But extrapolating the results to the long-term despite evidence from twin studies, adoption studies and social mobility analysis needs far more. You cannot simply throw the effect of inherent talent and traits out of the window.

*As a postscript, after writing most of this review I searched for other reviews of the book and came across this piece by Tim Harford. Many of the same points – Harford notes the buzzwords and provides a Gladwell reference too. I should note that, as it seems for Harford, the Gladwell comparison is at least part praise.

Links this week:

1. An excellent Econtalk podcast with Jonathan Haidt. Just don’t buy his lines about group selection – my reasons here.
2. Steven Pinker’s amusing article on the Ivy League. Pinker also pointed out this oldie but goodie – Bell Curve Liberals.
3. Greg Clark applies his work on social mobility to immigration. Reihan Salam comments.
4. A great swipe at “talent deniers”.
5. Tracking supercentenarians.
6. The agricultural origins of time preference – I’ll blog about this once I digest. HT: Tyler Cowen
7. The cognitive gains from Head Start fade out by elementary school.
8. I’m back into my habit of linking to Andrew Gelman articles every week – this time a great rant about expected utility titled “It’s as if you went into a bathroom in a bar and saw a guy pissing on his shoes, and instead of thinking he has some problem with his aim, you suppose he has a positive utility for getting his shoes wet“

I should start this review of Gerd Gigerenzer’s least satisfactory but still interesting book, Risk Savvy: How to Make Good Decisions, by saying that I am a huge Gigerenzer fan and that this book is still worth reading. But there was something about this book that grated at times, especially against the backdrop of his other fantastic work.

In part, I continue to be perplexed by Gigerenzer’s ongoing war against nudges (as I have posted about before), despite his recommendations falling into the nudge category themselves. Nudges are all about presenting information and choices in different ways – which is the staple of Gigerenzer’s proposal to make citizens “risk savvy”. Gigerenzer’s use of evidence and examples throughout the book also fall well short of his other work, and this is ultimately the element of the book that left me somewhat disappointed.

The need to make citizens risk savvy comes from Gigerenzer’s observation (which matches that of most of Gigerenzer’s faux adversaries – the behavioural scientists) that people misinterpret risks when they are presented in certain ways. If I say that screening reduces the risk of dying from breast cancer by 20 per cent, most people will interpret it to mean that 200 of every 1,000 people will be saved, rather than understanding that it means screening reduces the risk of death from 6 in 1,000 to 5 in 1,000 – effectively saving one out of 1,000.

Gigerenzer’s contribution to this area is to show that if presented in natural frequencies (i.e. tell people about the statistics as proportions of, say, 1,000 people), people are better able to understand the actual risks. This includes doctors, who are equally confused by statistics as everyone else, and who Gigerenzer suggests need training to communicate risks in ways that their patients can understand.

This ability to make citizens and experts risk savvy leads Gigerenzer to argue that people do not always need to be at the mercy of their biases. People can be educated to understand risks and experts can present them in ways that others understand. He advocates risk literacy programs in school, showing that simple decision tools can dramatically increase understanding of probability and statistics, although he spends little time discussing how well this education sticks. In making his point, Gigerenzer takes aim at the behavioural science crowd by claiming that natural frequencies wouldn’t have helped if people are subject to cognitive illusions – a strawman argument. As he does at semi-regular intervals through the book, Gigerenzer clouds an interesting argument with an attempt to engage in a battle that doesn’t really exist.

That said, I did enjoy this part of the book and have found myself quoting a lot of the examples. His arguments about how to present risk are compelling. Further, it is enjoyable to read Gigerenzer’s evisceration of the presentation of risk by various high-profile cancer organisations.

There are parts of the book where Gigerenzer is more pessimistic about the ability to educate the masses, such as when he channels Nassim Taleb and berates the finance industry for not understanding the difference between risk and uncertainty. In a world of uncertainty – where we do not know the probability of events – simple rules often outperform more complex models that are overfitted to past data. This provides a natural entry point to Gigerenzer’s well-established work (and subject of some of his better books) on the accuracy of heuristics. Risk Savvy has plenty of additional advocacy for their use with Gigerenzer arguing that we can be trained to use useful heuristics in making better decisions. Gigerenzer covers areas from marriage (set your aspiration level and choose the first person who meets it) to business to the stability of financial institutions, building on decades of evidence he has accumulated on the accuracy of simple rules.

Gigerenzer’s heuristics don’t always match up with his optimism that we can make people risk savvy. One heuristic he suggests is: “If reason conflicts with a strong emotion, don’t try to argue. Enlist a conflicting and stronger emotion.” He also recognises the limits to education, with heuristics such as “don’t buy financial products you don’t understand.” But given that a lot of people don’t understand compound interest, we might need to rely on the Dunning-Kruger effect to allow people to follow this rule and still make any investments.

One interesting point made by Gigerenzer is that there is still a role for experts (and even consultants) in a world where we use simple heuristics. Suppose we replace our complex asset allocation models with a 1/N rule – allocate our assets equally across N choices. This still leaves questions such as the size of N, what we will include in N, or when you should rebalance. For many heuristics, there may be more complex underlying choices – although I imagine heuristics could be developed for many of these too.

Gigerenzer is also a stout defender of gut instinct – again, as covered in his other books. Gigerenzer suggests (and I agree) that data is often gathered due to a culture of defensive decision-making and not because data is the major reason in the decision. This is, however, the weakest area of the book, as Gigerenzer’s stories reek of survivorship bias. Gigerenzer notes that leading figures in business reveal in surveys that they rely on gut instinct and not data in making major decisions. But how many corpses who relied on gut instinct are strewn along the road of entrepreneurship?

As another example, Gigerenzer talks of a corporate headhunter who had put a thousand senior managers and CEOs into their positions. The headhunter said that nearly all the time he based his selection on a gut decision. He was now being replaced by tests by psychologists. Gigerenzer puts this down to a negative error culture, with the procedures designed to protect the decision makers. But what is the evidence that the headhunter has been good at their job and could outperform the psychologists armed with tests?  Similarly, Gigerenzer suggests listening to those with good track records in business. Again, survivorship bias could make this a useless exercise. When talking of predictions of exchange rates in other parts of the book, Gigerenzer effectively makes this very same point – the successful people you see in front of you could simply be the lucky survivors.

However, the evidence that Gigerenzer has developed in the past would make it folly for anyone in business to throw gut instinct out the window – or to completely discard Gigerenzer’s arguments. But the way he makes the case through Risk Savvy feels built on anecdote and weak examples.

There is one rule I am going to take away from the book – an extension of my usual habit of flipping a coin for decisions about which I’m indifferent. Gigerenzer suggests flipping a coin and as it spins, considering what side you don’t want to come up. He used this example in the context of choosing a partner, but it’s not a bad way to elicit that gut instinct that you can’t otherwise hear.

Links this week:

1. Two pieces on diet. First, an excellent article on how the poisons in vegetables might be making you stronger. Second, a new study in the fat-carb wars.
2. Andrew Gelman on the strength of statistical evidence.
3. Two excellent podcasts. Gregory Clark on social mobility (and the genetics behind it) and Paul Sabin on the Simon-Ehrlich bet. Some of my thoughts on Julian Simon are here and here.
4. Economists are happier. The reasons? More cash and religion.

John Coates’s excellent The Hour Between Dog and Wolf: Risk Taking, Gut Feelings and the Biology of Boom and Bust tells the story of the effect of hormones on decision making in finance. By the end of the book, the idea that traders are rational calculating machines driven by their brains is torn apart.

As Coates shows, the divide between body and mind is not as Descartes or economists would have us believe. Signals travel both ways. The body can influence the brain. Physiological reactions triggered by pre-conscious regions of the brain affect emotion and mood. New to me was the existence of the enteric nervous system, which comprises around 100 million neurons in our gastrointestinal lining. It can operate autonomously of the central nervous system. Messages flow back and forth between the brain and enteric nervous system via the vagus nerve, the decisions of one affecting the decisions of the other.

The basic dynamic of decision making described by Coates involves the hormones testosterone, adrenalin, cortisol and dopamine. In anticipation of an opportunity on the trading floor, a trader’s hormones kick into action. Testosterone levels increase, bringing with it increased oxygen carrying capacity, confidence and appetite for risk. Adrenalin surges, quickening reactions and tapping into glucose deposits in the liver, which provides energy for the upcoming challenge.

Cortisol is also produced. Unlike the short-term action of adrenalin, cortisol acts over the longer term and stops metabolically expensive functions such as growth, reproduction, digestion or immune function. The initial release of cortisol also stimulates the release of dopamine, delivering a rush. Dopamine rewards us when we take actions that result in an unexpected reward. It makes us want to repeat and crave these actions (as a result, animals would rather work for food than simply be given it). Traders crave the rush of the floor.

When the trader has a win, his testosterone shoots up further. This testosterone infused trader will then take more risks. On average, more risk means more reward, so he earns higher profits. In fact, his testosterone levels in the morning are predictive of his afternoon profit. Hormones also make an appearance when this trader has a loss. His cortisol levels increase, decreasing his risk appetite and causing him to see danger everywhere.

The effects of biology are not only important for the people or firms involved, but can have systemic effects. Hormones exacerbate the market cycle. In a bull market, testosterone surges through the population of traders. Each takes larger and larger risks, pushing markets to new highs and triggering further cascades of testosterone. Irrational exuberance has a chemical base.

Similarly, in bear markets, cortisol levels peak. At the very time it might be best to buy, the market dries up as tentative traders retreat into their shells. Over the longer term, excessive cortisol impairs memory and causes anxiety.

As I mentioned in a previous post on an article by Coates, central banks could take this knowledge and use it to curb market cycles. In a bubble or crash, the population of traders could even enter a clinical state under the influence of pathologically elevated hormone levels. If that occurs, they could become insensitive to interest rates or other attempts by regulators to curb or control their activities.

Coates extends his idea to some interesting speculation on market cycles. Testosterone levels fluctuate over the year. In humans, they rise until autumn and fall through to spring. The drop in testosterone in autumn can cause males to suffer from ‘irritable male syndrome’. Given most major market crashes have occurred in October, is it autumn moodiness that takes stock markets down? Similarly, does ‘seasonal affective disorder, possibly also affected by testosterone, underlie underperformance between the autumn equinox and winter solstice?

For those in the industry, Coates offers advice on how to apply these findings, some relatively futuristic. We  can already record a range of physiological features, including hormone levels. Why not test them in the morning and set traders’ tasks or risk limits based on those measurements? We already have consumer products that perform real-time health monitoring. It is simply adding hormone levels to the suite of measures – although other measures such as heart rate, sweat levels and the like could also be useful indicators.

These physiological measures are probably better indicators than simply asking the traders how they are feeling. Whereas Coates found that hormone levels closely tracked the volatility of trading results and uncertainty in the market, surveys of these same traders about how stressed they were had almost no relationship to trading conditions, volatility or whether they were losing money.

Toward the end of the book, Coates includes some interesting material on how we might train our stress responses. One simple suggestion is exposure to acute stress, with those who have experienced moderate but short-lived stressors being toughened. In one study of rats, those rats exposed to stress when young had larger adrenal glands but a more muted response to stress. This was reflected in trader stress responses, with experienced traders having higher initial stress responses to events, but being able to quickly return to normal. However, once those stressors shift from being acute to chronic, problems begin. Exercise might also offer some protection, with sports science a potential source of new ideas.

One interesting piece of speculation is whether cold weather might provide useful training. Rats exposed to cold water have a quick arousal but quick recovery, with the stress response based more on adrenalin than cortisol. To the extent this occurs in humans, cold weather or water could be part of our training regime. Even more speculatively, Coates asks if the shift to more climate controlled environments has prevented a toughening of our psychological mechanisms, unlike that experienced by our ancestors.

An alternative to training could be to simply hire more women and older men who are less susceptible to testosterone feedback loops. However, I am not sure whether firms would want to implement this solution, with higher testosterone and risk taking leading to higher profits. The costs are across society when the crash comes and government steps in to lend a hand. Coates indicates this misalignment of incentives through the book, which suggests more than hiring policies are required.

One other interesting idea – only loosely linked to the major thesis – concerns fatigue. Fatigue might be seen as simply the result of running out of energy. But Coates points out a new model in neuroscience that suggests fatigue is a signal that the benefit from our current activity has dropped below its metabolic cost. It is a signal to stop the current search and start elsewhere. As a result, the cure for fatigue is a new task, not rest. Coates points to research suggesting overtime leads to hypertension and heart disease if we have no control over our attention, but otherwise it is not a problem. Flexibility in work could be as good as a vacation.

If I were to highlight one weakness of the book (more due to the state of the field than any fault of the author), it is that the foundation of studies on which it is built is fairly small, and largely based on data from a couple of trading floors. It would be great to see longitudinal data across a range of market participants during a number of cycles. Another potentially interesting extension would be to look at the hormone cycle in politics. Politicians can experience rational exuberance or appear to be exhibiting a constant state of panic. Are these the same biologically driven problems that Coates found in traders? Looking in new arenas such as this could provide a substantial contribution to our understanding of how humans make decisions.

Links this week:

1. Daniel McFadden on how people make choices.
2. Not that new but only spotted this week – Gerd Gigerenzer has a great rants on statistics. (HT: Noah Smith)
3. Forty per cent of modern Chinese are patrilineal descendants of only three super-grandfathers from 6,000 years ago. (HT: Carl Zimmer)
4. Anti-marijuana advocates funded by drug companies.
5. “There were no associations between childhood family income and subsequent violent criminality and substance misuse once we had adjusted for unobserved familial risk factors.“

The history of twin studies is littered with attempts to discredit them – such as this bit of rubbish. Yet every challenge has been met, with a couple of newish studies knocking off another.

The basic idea of twin studies is that by comparing the similarity of fraternal twins to the similarity of identical twins, you can tease out the influence of their genes. Twin studies tend to find that most behaviours have heritability of at least 0.2 (that is, 20 per cent of the variation is due to variation in genotype), IQ a heritability of over 0.5 and height around 0.8. However, twin studies require an assumption that identical and fraternal twins have equally similar environments, and this is where the critiques begin. If identical twins have a more similar environment, the estimates of heritability may be too high.

The responses, however, are plenty. There are studies of twins reared apart. Adoption studies find similar results. For those who believe that identical twins are treated differently to fraternal twins, there are studies of misidentified twins – where everyone thought they were identical or fraternal, but they were the other. Peter Visscher and friends took advantage of the differences in relatedness between siblings to generate estimates of heritability consistent with twin studies (You are 50% related to your siblings on average, which means you can test how similarity varies with variation in relatedness . For me, that study should have been the final nail in the coffin of any arguments that twin studies hadn’t told us anything).

One critique still floating around is that people who look more similar are treated similarly (although the misidentified twin studies deal with this to a degree). And the New York Times has reported two studies that take on that argument. In the first, Nancy Segal assessed the similarity in personality of 23 pairs of unrelated lookalikes. The similarity – effectively zero. Then in a replication, Segal got a skeptic, Ulrich Ettinger, involved in the project. They found the same result – no resemblance – unlike Ettinger’s expectation that people who looked alike would have similar personalities as people would treat them the same.

As Razib points out, these studies involves a small sample. However, they are yet another piece of evidence pointing in the same direction as all the rest.

Links this week:

1. Side effect warnings increase sales by building trust. Similar effects for disclosing conflicts of interest (ungated pdf).
2. Absorbing information on paper versus kindle. Even without digital search, I often find it easier to find favourite passages in the physical form.
3. Humans aren’t the only ones fighting wars.
4. I pointed out a couple of weeks ago that Geoffrey Miller had joined forces with Tucker Max to give sex and dating advice. Their reading list is very good, even if you’re not after any advice. Their suggestions as to which movies might provide insight is quite amusing.
5. Twin research.

Links this week:

1. An awesome looking conference – Complexity and Evolution: A New Synthesis for Economics
2. Hybridisation – what does it mean for conversation and the future of species?
3. Picking up in a Lamborghini
4. Status wars
5. How far are we from designer babies?
6. Neuroscience and marketing

I linked to this interview with Robert Sapolsky a couple of weeks ago, but after glancing through it again, I felt it worth highlighting two paragraphs (both for your interest and so I can find them again). First, on the evolutionary purpose of the teenage brain:

What I’ve been thinking might actually be going on is that adolescence is something unavoidable that emerges not because it’s so cool and adaptive, but because the adaptive thing is wait a long, long time before you have fully wired up your frontal cortex. Why might that be the case? Alright, so we’re born with our genome, the combination of your mother and father’s genes, that wind up in that first fertilized egg and that’s it. That’s your genetic legacy. Every cell in your body is destined to have that exact same genome. That turns out not to be true in all sorts of interesting ways, but what that also means is that when you’re thinking about what genes have to do with the brain behavior, by definition critically, if the frontal cortex is the last part of the brain to develop it’s the part of the brain least shaped by genes, and most sculpted by the environment and experience. And I think basically the only way you can have a species that is as complex and socially resilient and socially context dependent and all those amazing things we do, the only way you can pull that off is to have a frontal cortex whose development just bears the imprint of everything you experienced along the way—in effect, that’s been freed from whatever extent the genes are deterministic, which is not very. I think ironically what the evolution of the frontal cortex has been about is genetic evolution to free it as much as possible from the straight jacket of genes.

Second, on reductionism in neurobiology:

[R]eductionism doesn’t actually tell you a whole lot about how this stuff works. I mean reductionism is perfect for like telling you why your clock is broken. What you do is you break it down to its component parts. You find the part that’s got a tooth missing from the gear. I guess there’s not a clock on earth that works this way anymore, but your Renaissance clock. You fix the missing tooth, you put it back, you add the pieces back together and it works. The way to understand a complicated system is to understand its component parts. The way in which that steps away from the ideology is the component parts of the genes and the nerve transmitters and the hormones and the early experience. Okay, so that’s a more sophisticated version of reductionism. You got to be reductive about lots of different domains. But nonetheless, even that more multidisciplinary version of reductionism isn’t going to work because that’s not how complex systems work and humans are a complex system. You got these emergent non-linear chaotic properties. What’s that another way of saying? If you knew every individual’s genome and exactly which gene was active at which point, are you going to be able to predict who’s going to do what next? Absolutely not. If you added in knowing the levels of every hormone in their body at that point, if you added in… it doesn’t work that way. The reductionism breaks down because the reductionism breaks down in the same way that like a cloud that isn’t producing enough rain during a drought or something, the solution isn’t to study half the cloud and then get a research grant to study a quarter of the cloud and smaller, smaller pieces and finally understand the reductive basis of the non-rain and add it up together. That’s not how clouds work when they don’t rain. Humans are more like clouds than they are like clocks. We’re not reductive in that way, which is the case for any complex system.

And if you haven’t read the full interview, do it.