Daily Tao

The fatal issue is that logic always gets you to exactly the same place as your competitors. At Ogilvy, I founded a division that employs psychology graduates to look at behavioural change problems through a new lens. Our mantra is ‘Test counterintuitive things, because no one else ever does.’ Why is this necessary? In short, the world runs on two operating systems. The much smaller of them runs on conventional logic. If you are building a bridge or building a road, there is a definition of success that is independent of perception. Will it safely take the weight of X vehicles weighing Y kg and travelling at Z mph? Success can be defined entirely in terms of objective scientific units, with no allowance for human subjectivity.* This may be true when you are building a road, but it is not true when you are painting the lines on it. Here, you have to consider the more complex component of how people respond to informational cues in their environment. For instance, if you want vehicles to slow down, painting parallel lines across the road in the approach to a junction at increasingly smaller intervals will help, since the narrowing gaps between the lines will create the sensation that the car is slowing less than it really is. Americans aren’t terribly good at designing roundabouts, or ‘traffic circles’ as they call them, simply because they don’t have much practice.* In one instance, a British team was able to reduce the incidence of accidents on a traffic circle in Florida by 95 per cent by placing the painted lines differently. In one Dutch town traffic experts improved traffic safety by removing road markings altogether.* So there are logical problems, such as building a bridge. And there are psycho-logical ones: whether to paint the lines on the road or not. The rules for solving both are different; just as I make a distinction between nonsense and non-sense, I also use a hyphen to distinguish between logical and psycho-logical thinking. The logical and the psycho-logical approaches run on different operating systems and require different software, and we need to understand both. Psycho-logic isn’t wrong, but it cares about different things and works in a different way to logic. Because logic is self-explanatory, our preference is to use it in all social and institutional settings, even where it has no place. The result is that we end up using inappropriate software for the operating system, neglecting the psycho-logical approach.

I’m guilty of being one of those who take pride in being “logical” or a rational person. I like to think that I rely on reason to justify my decisions. Relying on logic even becomes something of a comfort zone for me in the sense that I use to make myself feel better whenever making decisions with uncertain outcomes.

The biggest issue is, you can pretty much find a logical reason for anything. In fact, relying on conventional logic becomes less effective when it comes to solving problems for humans, as we are are all highly complex beings with different motivations, emotions and are perfectly capable of contradicting ourselves.

Thus, its important not to sometimes let the allure of logic trap us from finding these counter-intuitive solutions. I have realised before that I have used logic as a tool to shut down suggestions from co-workers that I did not like. In fact, I probably used my ability to construct logical arguments to support the those who I did like. On reflection, I find that my personal feelings or insecurities end up subconsciously driving our behaviour more than I realised at those moments.

What I have been finding to really help is to always be open-minded and be aware of our own personal biases. Be willing to consider the opposite and counter-intuitive at times. If you’re constantly trying to imitate what “competitors” do,  you’ll essentially be blending in. You’ll become more average, and will be much less likely to be exceptional. Of course, this isn’t some folk wisdom to take heart for all aspects of your life as if you never copy what others do, you might also end up being far worse than average.

Imagine that you are sitting in the boardroom of a major global drinks company, charged with producing a new product that will rival the position of Coca-Cola as the world’s second most popular cold non-alcoholic drink.* What do you say? How would you respond? Well, the first thing I would say, unless I were in a particularly mischievous mood, is something like this: ‘We need to produce a drink that tastes nicer than Coke, that costs less than Coke, and that comes in a really big bottle so people get great value for money.’ What I’m fairly sure nobody would say is this: ‘Hey, let’s try marketing a really expensive drink, that comes in a tiny can . . . and that tastes kind of disgusting.’ Yet that is exactly what one company did. And by doing so they launched a soft drinks brand that would indeed go on to be a worthy rival to Coca-Cola: that drink was Red Bull. When I say that Red Bull ‘tastes kind of disgusting’, this is not a subjective opinion.* No, that was the opinion of a wide cross-section of the public. Before Red Bull launched outside of Thailand, where it had originated, it’s widely rumoured that the licensee approached a research agency to see what the international consumer reaction would be to the drink’s taste; the agency, a specialist in researching the flavouring of carbonated drinks, had never seen a worse reaction to any proposed new product. Normally in consumer trials of new drinks, unenthusiastic respondents might phrase their dislike diffidently: ‘It’s not really my thing’; ‘It’s slightly cloying’; ‘It’s more a drink for kids’ – that kind of thing. In the case of Red Bull, the criticism was almost angry: ‘I wouldn’t drink this piss if you paid me to,’ was one refrain. And yet no one can deny that the drink has been wildly successful – after all, profits from the six billion cans sold annually are sufficient to fund a Formula 1 team on the side.

Just thought I’ll go with this opening story for one, as I think this passage is symbolic of the book’s key message. Many times, intuitive logical reasoning fails us when we’re trying to get people to buy your shit. The reasoning behind is that none of us truly behaves in logical ways, and so why would relying on reason to make marketing decisions necessarily yield the best results?

The anecdote in question, was something that I have always struggled with when I wanted to figure out ways to market a product. Sometimes, giving your customers what they seemingly want doesn’t actually result in better results. This also reminds me of the story where Coca Cola tried to change the taste of their product in 1985 and supposedly had a superior product based on blind taste tests. However, when they launched the new recipe, it resulted in thousands of complains, lawsuits and they eventually switched back to their old formula in just a couple of months.

The main reason why we choose to buy certain products sometimes can be down to emotion, loyalty, or some other intangible factor that cannot easily be explained by logic. In the case of beverages where taste is a subjective experience to every user, I believe the emotional factor is ever more salient. This book aims to shed more light on that and how we can possibly build great brands.

In 2017 a sociologist at Virginia Commonwealth University named Tressie McMillan Cottom published a book titled Lower Ed that helps to illuminate the choices facing Orry and Taslim and Alicia and millions of others like them. The book’s nominal subject is the business of for-profit colleges, a sector of the higher education economy that experienced an enormous boom in the first decade of this century, increasing in size from four hundred thousand students in 2000 to two million in 2010, during the same period that so many state governments were making drastic cuts to their higher education budgets. The sector includes everything from strip-mall cosmetology colleges to online PhD programs in business administration at the University of Phoenix, and its remarkable growth came about despite its notoriously poor outcomes for students: high tuition, low graduation rates, and high levels of student debt. In 2012 for-profit colleges were educating just 12 percent of the nation’s college students, but those students accounted for 44 percent of the nation’s student-loan defaults. The question Cottom sets out to answer in her book is: Why? Why would such a manifestly lousy product sell so well? Her answer is that for-profit colleges, during their boom years, figured out how to exploit a new and pervasive gap in the relationship between the education system and the labor marketplace: a job market that demanded more skills, a public education system that didn’t reliably deliver them to the nation’s young people, and a higher education ecosystem that wasn’t equipped or inclined to offer the education and training those young people were being told they needed. In order to make money amid those anxious circumstances, for-profit colleges didn’t need to deliver an actual education; they needed only to deliver the promise of one. And during that period of rapid growth, for-profit colleges became very good at making beguiling promises; the industry spent more than twice as much on marketing and profit taking as it did on actual student instruction. In her book, Cottom takes pains to consider the for-profit sector (which she refers to as Lower Ed) as part of a broader economic and educational landscape. “For-profit colleges are something more complicated than big, evil con artists,” Cottom writes. “They are an indicator of social and economic inequalities and, at the same time, are perpetuators of those inequalities … The growth and stability of Lower Ed is an indication that the private sector has shifted the cost of job training to workers, and the public sector has not provided a social policy response.” At the opposite end of the prestige spectrum from Lower Ed is, of course, Higher Ed: selective institutions like Princeton University and Trinity College and the University of Texas. Our instinct, often, is to consider those elite institutions as part of an entirely separate sphere from the world of Lower Ed. Cottom encourages us to think of them as two sides of the same coin. “Lower Ed can exist precisely because elite Higher Ed does,” she writes. “The latter legitimizes the education gospel while the former absorbs all manner of vulnerable groups who believe in it: single mothers, downsized workers, veterans, people of color, and people transitioning from welfare to work.”

What drives the huge demand for private colleges? It is stated that most for-profit colleges allocate far more resources to marketing their classes than actually improving their product, the classroom experience. It also reminds me of a previous book that I have covered (The Case Against Education), where the main idea was that the main value education provides to us isn’t really about learning, but about signalling.

If we view it from the perspective of people wanting to purchase the perception of having a quality education rather than the actual education itself, then everything makes sense. Regardless of public or private, most colleges would choose to spend tons of money in facilities, marketing and PR to look more legitimate to the public eye. For most of us, not much of the knowledge we learned in our actual university education actually gets used in our careers anyways. The question is whether that spending has gone too far and for me, whether the government should be subsidising those funds that go towards “marketing”.

This pretty much sums up the book on how education eventually becomes a tool that further divides us than a tool of social mobility that we might idealise it to be. More could be done in bridging the information deficit between those who happen to be born into the “right” families. While not intentional, it happens that most resources or new schemes tend to get distributed to those who don’t need it anyways or are the most privileged. From a policy perspective, it then might make sense to dedicate public resources more broadly as most private initiatives tend to already go towards those whom are advantaged already. Proponents of meritocracy might find this a hard pill to swallow, and there indeed.

I’ve been having a few conversations with a few friends recently about our education system, and I hope to have a more in-depth conversation or research on this. What I do find funny, is just realising that as compared to other topics such as say crypto or tech, everyone seems to have very strong opinions about education.

To be hired by one of these elite firms, Rivera was told, it was not enough just to have played a sport. It also mattered which sport you played. Recruiters were mostly unimpressed by students who took part, even at a high level, in easily accessible sports like wrestling or basketball or soccer. Instead, they preferred candidates who played sports with a high barrier to entry, either because of specialized equipment or expensive club fees or both—sports like lacrosse, field hockey, tennis, squash, and rowing. Of course, these sports, as Rivera notes, are played almost exclusively by rich and upper-middle-class white kids. They generally require a serious commitment in time and money, not just from students but from parents as well, often beginning in middle school or even earlier. This created a system that was apparently open and meritocratic but that actually strongly favored young people from high socioeconomic backgrounds and eliminated the rest from consideration. “If you’re not playing the right sport when you’re fourteen years old, it’s going to be really, really hard to get a job at Goldman Sachs after college,” Rivera explained. “And who is playing the right sports? People whose parents know that this stuff is not just fun and games, people who have the money to pay for the equipment, people who know that lacrosse is this important insider thing.” People, in other words, with not just financial capital but also cultural capital, young people whose parents somehow intuited, in middle school, precisely which extracurricular activities their children’s investment-banking recruiters were going to be looking for a decade later, and who signed their kids up and shuttled them to and from practice accordingly. Meanwhile, low-income students at elite colleges mostly didn’t understand the rules of the game; they didn’t understand that in some cases, the starting whistle had sounded years earlier. “In contrast to students from upper-middle-class backgrounds,” Rivera wrote, “less affluent students are more likely to enter campus with the belief that it is achievement in the classroom rather than on the field or in the concert hall that matters for future success, and they tend to focus their energies accordingly.” They still believed in “the work,” in other words, in the version of the American meritocracy they had been taught as children to respect and put their faith in. And their chances to land a lucrative job after college suffered as a result.

Does cultural capital matter as much in a place like Singapore? For a relatively younger nation of immigrants, perhaps there isn’t that much “legacy” in terms of the stereotypical rich “behaviours”. However, I’ll wager that the cultural norms of the “affluent” will grow over time and set in. For me, I would say that the disparity will grow over time. Parents in Singapore who have the financial capability  and send their children to “Montessori” like preschools and various enrichment classes will begin setting a different culture against those who don’t.

At the stage, then, it is no longer about how intelligent or capable one is, but about how they fit in towards a certain set of “expected behaviours”. While our gut reactions might be to decry this, we also have to wonder what we can really do about it and if it is really “un-meritocractic”. Most prestigious jobs such as investment banking are really about client relationships, and an employee that fits in better with the expectations and behaviours of the affluent will probably do better in fitting in and building relationships.

Is there anything wrong on the end of the individual? As humans, we tend to stick with people who have things in common, and logically there should be nothing wrong with that. Regardless, each individual preference eventually cascades into a systemic bias. Policy wise, its why there is value in ensuring certain representation of various groups, even if that might be be “unmeritocratic” as we see it.

Personally, I would also prefer not to have such expectations that meritocracy in classroom results are all that matter be indoctrinated into the young. Various factors such as relationships, how you fit in, your cultural background and many other things out of your control matter towards your desire to get that job or be hired. Some might see it as a reason to be fatalistic. However, I’ll prefer to see it that we understand that competency on the job isn’t the only factor and perhaps we might be each be able to something more about it.

In contrast to that small, ambitious group, the majority of high-scoring low-income students had aspirations that seemed much more constrained. They followed the same pattern as lower-scoring low-income students, applying to only one or two institutions, often including a local community college or a nearby nonselective public university. Most didn’t apply to a single selective college. Hoxby and Avery referred to members of this cohort as “income-typical” students, their college decisions defined by their socioeconomic status and not by their academic ability. Compared to their achievement-typical peers, these students were more likely to live in small towns or rural areas in the middle of the country and to attend schools where they would be one of only a few high-achieving students. They were also significantly more likely to be white: 80 percent of them, in fact, were white, compared to just 45 percent of the achievement-typical low-income students. Hoxby’s theory was that the main obstacle standing in the way of those income-typical high achievers was an information deficit: they simply didn’t know much about elite colleges and how to apply to them. They didn’t know, for example, that they were eligible for fee waivers that would allow them to apply to college free of charge. They didn’t know that with test scores as high as theirs, they would likely be admitted to selective colleges. They didn’t know that if admitted, they would likely get lots of financial aid—so much aid, in fact, that it might actually be cheaper for them to attend an excellent private college halfway across the country than to go to the decent public university nearby. And they didn’t know these significant facts, Hoxby hypothesized, because there was no one around to tell them. No one from their family or their high school—or maybe even their entire town—had ever attended a selective out-of-state college. And institutions like Harvard weren’t telling them this story either, at least not in an up-close and personal way. Elite colleges almost never sent recruiters to the high schools attended by these income-typical students, in part because the schools were usually in the middle of nowhere.

Income is 1 factor. But that other really noticeable factor is about “information deficit“. Generally, the environment we grows up in usually determines the opportunities that we are exposed to. A child born in a middle income family but with parents who have knowledge of how to maximise the opportunities for their child. Perhaps one best such instance for this can be the movie “King Richard”, which is about the story of how Richard, father of the Serena and Venus Williams, tried his utmost best to ensure that his kids would become stars.

Parents play a super huge role, positive or negative, in maximising the opportunities of their children after all. That is where I think that more effort can be done systematically to bridge that information deficit. In another passage of this book, Tough talks a lot about how a super disproportionate sum of money are going to kids whom already have access to greater opportunities. Ideally, most funding and resources should be dedicated towards bridging and counselling, ensuring that every kid gets the advice that they need to further optimize their career choices or outcomes.

So how much is too much intervention? And how much should the state interfere in every family’s parenting? A lot of that is down to your own personal beliefs and values of how much the government should intervene. For me, I think that resources definitely can and should be put towards bridging the gap and ensuring that everyone is able to achieve their potential, and not just dedicating resources over-proportionately towards the ones whom already have the best opportunities.

The report was centered around four important discoveries. First, using the IRS data, Chetty and his team found that students who attend ultraselective colleges in the United States are much more likely than other students to become very rich as adults. Young people who attend “Ivy Plus” institutions—meaning the Ivy League colleges plus a handful of other institutions with similarly elevated selectivity rates, like the Massachusetts Institute of Technology, the University of Chicago, and Stanford—have about a one in five chance of landing, in their midthirties, among the top 1 percent of American earners, with incomes over $630,000. People who attend “other elite” four-year colleges (including Davidson) have about a one in eleven chance of hitting the top 1 percent. Students at community colleges, meanwhile, have about a one in three hundred chance. (Students who don’t attend college at all have about a one in a thousand chance.) The kind of college you attend, in other words, correlates strongly with what you’ll earn later on. Second, Chetty and his collaborators found that outcomes for poor kids and rich kids who attend the same institution are remarkably similar (the definition of “poor” here being that your family’s income is in the bottom quintile, or bottom fifth, of all families nationwide, and the definition of “rich” being that your family’s income is in the top quintile). Poor students who attend Ivy Plus colleges wind up with household incomes of about $76,000 a year, on average, as young adults. Rich students who attend Ivy Plus colleges wind up earning about $88,000. That’s more than the kids who grew up poor, but not a ton more. There is a similar effect at almost every college: kids who grow up rich earn only a bit more than their college classmates who grow up poor. Attending the same college eliminates almost all the advantages that those who grow up with family wealth have over those who grow up in poverty. Third, the researchers found that attending an elite college seems to produce a greater economic benefit for students who grow up poor than it does for students who grow up rich. If you’re a rich kid, attending an Ivy Plus college rather than no college at all increases your odds of making it into the top income quintile as an adult earner by a factor of four. So you do get an economic boost from your college education, but it’s not a huge one. If you’re a poor kid, though, attending an Ivy Plus college rather than no college is truly life-changing. It increases your odds of making it into the top income quintile by a factor of fourteen. So far, these results suggest a pretty happy story for fans of economic mobility. Higher education actually works! It can propel students from all backgrounds into the upper reaches of the American economy. Sending poor students to elite colleges is an especially good investment—they benefit more than their wealthy peers do. And when rich and poor students attend the same college, the education they receive there actually does create a fairly level playing field for them as they head off together into the job market. But that is where the happy story ends. Because the fourth major discovery made by Chetty and his colleagues was that rich and poor students are not attending the same colleges. Not at all. At Ivy Plus colleges, on average, more than two-thirds of undergraduates grew up rich, and fewer than 4 percent of students grew up poor. Elite college campuses are almost entirely populated by the students who benefit the least from the education they receive there: the ones who were already wealthy when they arrived on campus. Using the IRS data, Chetty’s team was able to produce Mobility Report Cards not just for each broad category of college, but for each individual institution. What they found was that while every selective college was tilted in favor of wealthy students, some were tilted more sharply than others. And two of the colleges where the tilt was most extreme were Princeton and Penn, the two colleges that rejected Shannen Torres.

I just had a conversation today about the lawsuit from an Asian against Harvard recently due to the “discrimination” against Asians. The gist of this issue is that , Asians, who tend to score better on average, get less seats than their resume or test scores would justify. An Asian might be rejected rejected compared to other candidates even if they had better scores. Colleges rationale for this is that Asians already take a disproportionate amount of places. In that conversation, the person I spoke to couldn’t fathom how this “reverse discrimination” could even take place in a fair society. In this case, there was no Meritocracy.

But Meritocracy can be misleading. The Inequality Machine is a book that uses college as the one example of that and goes in depth about it. Its basic premise is that students from wealthier families attend elite colleges disproportionately more. Are these students really more capable or better inherently? Or they did just have access to better cram schools, guidance counsellors and opportunities.

This feels like some other conversations we’ve had before on Meritocracy (from the book The Tyranny of Merit), so I won’t go into too much detail about it. However, 1 thing this book focuses on that I thought was interesting was how out of place someone, who came from an under-privileged background, could feel in these “elite institutions”. Even if they were already there on “merit”, there are further barriers in terms of fitting in socially that makes their life in college that tad bit harder.

I’ve been super lucky to grow up in an environment any financial worries, but I’ve definitely felt out of place or having the”you don’t belong” notion back in the days of school. You get that social burden or even anxiety in some sense, especially when interacting with those who have that self-confidence or obviously came from more highly educated families. This feeling general becomes an unwelcome distraction. In the context of the book, there have been under-privileged students who attended elite institutions and just couldn’t fit in and this actually also impacted their results.

Paul Tough goes into more details in the rest of the book, and I’ll be picking some of the more interesting perspectives or anecdotes to share.

I’ve also been much slower in posting updates recently. I am intentionally slowing down to have more time to improve the depth of my reflections and the quality of my writing. This break has also allowed me to think about what I wanna do with this channel going forward, so do stay in touch!

We draw a number of lessons for the use of models in business and government. First, deploy simple models to identify the key factors that influence an assessment. A common response to criticisms of the kind we have described above is an offer to add to the model whatever we think is missing. But this is another reflection of the mistaken belief that such models can describe ‘the world as it really is’. The useful purpose of modelling is to find ‘small world’ problems which illuminate part of the large world of radical uncertainty. Second, having identified the parameters which are likely to make a significant difference to an assessment, undertake research to obtain evidence on the value of these parameters. For example, what value do rail passengers attach to a faster journey? Quantification can often serve as a reality check even when precise quantification is obviously spurious. The preservation of the beautiful and well-preserved Norman church at Stewkley in England (close to a proposed new high-speed rail line) is worth something, but surely not a billion pounds. Often this kind of calibration is enough to resolve some aspects of a decision. Third, simple models provide a flexibility which makes it much easier to explore the effects of modifications and alternatives. For example, the WHO demographic model not only diverted attention from the key issue but its complexity made it harder to investigate alternative specifications of the model structure and parameters. Scenarios are always useful in conditions of radical uncertainty. How might this policy decision look in five years’ time – or fifty? Fourth, under radical uncertainty, the options conferred by a policy may be crucial to its evaluation. Faced with a choice as to which of London’s two major airports, Gatwick or Heathrow, should be chosen for expansion, recognition that the topography of Gatwick allows piecemeal adaptation of the development of facilities in the light of uncertain future demand, while that of Heathrow does not, should be an important factor in the choice. Options may be positive or negative in value – facilitating policies not directly connected to the initial objectives, or excluding possible attractive alternatives. In the end, a model is useful only if the person using it understands that it does not represent ‘the world as it really is’, but is a tool for exploring ways in which a decision might or might not go wrong.

Are models useful in a world of radical uncertainty?

Certainly, but not in the way we are normally accustomed to. In this excerpt, what the authors suggest is to view models as a way to replicate ‘small world’ problems that serve as part of a larger problem. While no model can be an accurate representation of the world, we can use simple models to illuminate the choices we can make for smaller decisions.

The example given was simply finding a way to quantity the value rail passengers attach to a faster journey. While models alone would not help you determine what is needed for an effective railway system, having a good understanding of how much passengers value speed goes a long way in illuminating the right decisions.

In such cases, simplicity prevails.

In a complex and uncertain world, a simpler model allows you to have the flexibility in changing the parameters whenever you see fit. We might generally think that adding more details lead to a more “accurate representation”. However, the author contends that that is seldom the case and what happens is you end up diverting attention away from the key issue.

If we accept that we live in an uncertain world that cannot be predicted, then the best course of action is to remain adaptable. Select choices that provide you flexibility for change if you are uncertain of the result.

In models used by international agencies and central banks, beliefs are guided over time towards the correct rational expectation defined by the model. And if we are unsure which is the correct model then statistical learning leads to the right choice. This might make sense in a stationary world. But in a non-stationary world there is no underlying probability distribution or model to discover. The process of forming expectations is one in which the views of friends and colleagues, the stories in the Daily Mail or the New York Times , the news and prognostications on Fox News or BBC, play an important role. We are social animals, even in – and perhaps especially in – the trading rooms of investment banks. People talk to each other and learn from each other. They read the same Daily Mail and New York Times , and Fox News and BBC show the same pictures on every screen. Social media have speeded up this process. Traders imitate each other and may try to outwit each other. It is entirely in accordance with reason and logic to learn from other people’s mistakes rather than wait and learn only from one’s own. Beliefs are embodied in a narrative, and the prevailing narrative can change in an abrupt or discontinuous fashion when a sufficiently large number of people see evidence that leads them to change their view. Such evidence might be derived from fresh regression analysis. Or from watching pictures of bewildered former Lehman employees carrying their possessions into the street in cardboard boxes. Or from messages conveyed by social media. The events of September 2008 changed the prevailing narrative and led to discontinuous changes in expectations. No one had imagined that the sophisticated American financial system would find itself on the brink of collapse. Central banks were not prepared to deal with the consequences of such a failure. Compared with the vast array of financial instruments in the world, the simplicity of a single financial asset in the textbook model did not generate insights, and so central banks relied more on a study of financial history than the predictions of econometric models.

The key point in this whole excerpt, and probably the book is that almost all social and economic phenomenon are non-stationary. In such cases, we will not be able to discover any 1 “true” model unlike say for the fields of Physics or Chemistry. Heck, its downright impossible to 100% predict the movements of more than 2 objects exerting gravity on each other with today’s computing power, much less predict social or economic trends where there are infinite variables.

In a non-stationary world, it, according to the authors, it makes more sense to learn from others, whether it be from social media, personal connections or from the history books. Doing so is much better than holding any 1 “true model” to forecast things anyways. Relying on just 1 true model, as the authors would say, is closer to that of religion and faith than science.

Steve Jobs was not watching a Bayesian dial: he was waiting until he recognised ‘the next big thing’. And Winston Churchill also played a waiting game as he saw the United States gradually dragged into war – and did his utmost to accelerate American entry. We do not know whether Obama walked into the fateful meeting with a prior probability in his mind: we hope not. He sat and listened to conflicting accounts and evidence until he felt he had enough information – knowing that he could expect only limited and imperfect information – to make a decision. That is how good decisions are made in a world of radical uncertainty, as decision-makers wrestle with the question ‘What is going on here?’ In contrast, bank executives relied on the judgements of their risk professionals, who in turn relied on Bayesian techniques, and the results were not encouraging. Woodford’s students, even though they were familiar with the principles of Bayesian reasoning, did not approach their task in this way – even though the experiment was designed to stimulate them to do so. Woodford’s students were not making bad decisions. They simply did not use Bayesian reasoning to process new information. An alternative interpretation of the experimental results is that the students were developing a sequence of narratives, and challenging and revising the narrative at discrete intervals as they went along. Far from being systematically biased, the students were systematically struggling to come to terms with radical uncertainty in the manner in which thoughtful people normally come to terms with it. (Or, perhaps, waiting for the session to end and to collect their $10.) When we express doubt about the practical relevance of the Bayesian dial, we are not for a moment suggesting that people should not modify their views in the light of new information. We think they should manage radical uncertainty as President Obama did – listening to evidence, hearing pros and cons, inviting challenges to the prevailing narrative, and finally reaching a considered decision. And Obama might have been forced, as Carter had been, to change his decision when he learnt of problems in the execution of the agreed plan which had not been anticipated. In the fortunate event this proved unnecessary.

What do you do when you have to make decisions when there are no or false probabilities that you can consult from? Steve Jobs certainly didn’t calculate probabilities before he chose to venture into PCs. If you only rely on making decisions based on maximum probability of success, it might actually prevent you from making the most meaningful life decisions you can make. The entrepreneur who only assesses whether to begin a venture based on probability probably wouldn’t do so as the numbers wouldn’t check out.

Assessing important life decisions based on probability should always be a consideration, and can help us be aware of the risks. However, it should just be a guideline and never your only principle. Given that we are unable to assess real probability due to the vast amount of uncertainties, relying on numbers simply give you a “false sense of precision”.