Your Daily Tao

Maybe not. Consider some of the puzzling data points that Robin discovered. To start with, people in developed countries consume way too much medicine—doctor visits, drugs, diagnostic tests, and so forth—well beyond what’s useful for staying healthy. Large randomized studies, for example, find that people given free healthcare consume a lot more medicine (relative to an unsubsidized control group), yet don’t end up noticeably healthier. Meanwhile, non-medical interventions—such as efforts to alleviate stress or improve diet, exercise, sleep, or air quality—have a much bigger apparent effect on health, and yet patients and policymakers are far less eager to pursue them. Patients are also easily satisfied with the appearance of good medical care, and show shockingly little interest in digging beneath the surface—for example, by getting second opinions or asking for outcome statistics from their doctors or hospitals. (One astonishing study found that only 8 percent of patients about to undergo a dangerous heart surgery were willing to pay $50 to learn the different death rates for that very surgery at nearby hospitals.) Finally, people spend exorbitantly on heroic end-of-life care even though cheap, palliative care is usually just as effective at prolonging life and even better at preserving quality of life. Altogether, these puzzles cast considerable doubt on the simple idea that medicine is strictly about health. To explain these and other puzzles, Robin took an approach unusual among health policy experts. He suggested that people might have other motives for buying medicine—motives beyond simply getting healthy—and that these motives are largely unconscious. On introspection, we see only the health motive, but when we step back and triangulate our motives from the outside, reverse-engineering them from our behaviors, a more interesting picture begins to develop. When a toddler stumbles and scrapes his knee, his mom bends down to give it a kiss. No actual healing takes place, and yet both parties appreciate the ritual. The toddler finds comfort in knowing his mom is there to help him, especially if something more serious were to happen. And the mother, for her part, is eager to show that she’s worthy of her son’s trust. This small, simple example shows how we might be programmed both to seek and give healthcare even when it isn’t medically useful. Robin’s hypothesis is that a similar transaction lurks within our modern medical system, except we don’t notice it because it’s masked by all the genuine healing that takes place. In other words, expensive medical care does heal us, but it’s simultaneously an elaborate adult version of “kiss the boo-boo.” In this transaction, the patient is assured of social support, while those who provide such support are hoping to buy a little slice of loyalty from the patient. And it’s not just doctors who are on the “kissing” or supportive side of the transaction, but everyone who helps the patient along the way: the spouse who insists on the doctor’s visit, the friend who watches the kids, the boss who’s lenient about work deadlines, and even the institutions, like employers and national governments, that sponsored the patient’s health insurance in the first place. Each of these parties is hoping for a bit of loyalty in exchange for their support. But the net result is that patients end up getting more medicine than they need strictly for their health. The conclusion is that medicine isn’t just about health—it’s also an exercise in conspicuous caring.

Sharing excerpts from a new book. This book talks about how our actions are driven by many hidden motives that we might not be aware of, and that over time, evolution has shaped our current behaviours to be able to live in social groups.

In this example, healthcare isn’t really just about fixing one’s health. There are social elements involved and for many other parties, it is also more about showing one cares than actually helping to deal with the illness in question. These are the sort of social norms and contracts that we have subconsciously developed over the pasts thousands of years.

By the time Deng left Singapore on November 14, the two leaders had developed a special relationship that, like that between Zhou Enlai and Kissinger, enabled them to communicate with mutual respect on a common wavelength. Lee and Deng had both come of age fighting colonialism, and both had lived abroad in a colonial power. Both had been bold leaders during their countries’ revolutionary struggles, and both understood what it took to build order from a chaotic situation. Although Lee had received an English education, he had also studied Chinese history and could sense where Deng was coming from. They were both straightforward realists, utterly dedicated to their nations, who had risen to responsible positions at a young age and believed in the need for strong personal leadership. They understood power and thought strategically, taking into account long-term historical trends. Only one other person outside mainland China, Y. K. Pao (Yue-Kong Pao, founder of Hong Kong’s World Wide Shipping Group), and no other political leader, had bonded with Deng the way Lee did. Deng had close ties with many foreign leaders, but his relationship with Lee reflected a greater depth of mutual understanding. From Deng’s perspective, what made Lee and Y. K. Pao attractive was their extraordinary success in dealing with practical issues, their first-hand contacts with world leaders, their knowledge of world affairs, their grasp of long-term trends, and their readiness to face facts and speak the truth as they saw it. Lee considered Deng to be the most impressive leader he ever met—one who thought things through, and, when something went wrong, was ready to admit the mistake and set out to solve it.

Interesting anecdote that I saved from this book. Last excerpt that I’ll be sharing. What I think was interesting was the parallels between Deng and Lee in their practical approach to leading the country, speaking truths in a direct manner and despite this trait, still being able to have strong relationships with foreign world leaders. As we can see in today’s climate, generals or political leaders speaking in a direct fashion doesn’t necessarily endear you to foreign leaders or the local populace.

Mao Zedong, a charismatic visionary, brilliant strategist, and shrewd but devious political manipulator, led the Communists to victory in the civil war and in 1949 unified the nation and eliminated most of the foreign-held territories. The military forces he had accumulated during the civil war were sufficiently strong that with the Communist Party’s organizational discipline and propaganda, he was able to establish in the early 1950s a structure that penetrated far more deeply into the countryside and into urban society than had the imperial system. He built up a unified national governing structure led by the Communist Party and, with Soviet help, began to introduce modern industry. By 1956, with both peace and stability at hand, Mao might have brought wealth and power to China. But instead he plunged the country into an ill-advised utopian debacle that led to massive food shortages and millions of unnatural deaths. In his twenty-seven years of rule, Mao destroyed not only capitalists and landlords, but also intellectuals and many senior officials who had served under him. By the time he died in 1976, the country was in chaos and still mired in poverty. When Deng ascended to power in 1978, he had many advantages that his predecessors lacked. In the mid-nineteenth century, few people had understood how deeply the new technology and developments along the coast were challenging the Chinese system. In the last years of the empire, the reformers had little idea of the institutional developments required to implement progressive new ideas. At the time of Yuan Shikai and Sun Yat-sen, there was no unified army and no governmental structure capable of uniting contenders for power. And after coming to power, Mao, who had no foreign experience, could not receive help from the West due to the Cold War. By the time Deng came to power, Mao had already unified the country, built a strong ruling structure, and introduced modern industry—advantages that Deng could build on. Many high officials realized that Mao’s system of mass mobilization was not working, that China was lagging far behind the foreign countries in science and technology, and that it needed to learn from the West. More fundamental change was called for, and Deng could rely on help from disgraced former senior officials who had been removed from power but not eliminated. These returning revolutionaries stood ready to unite under the leadership of Deng and the Communist Party, providing a ready resource of skills and energy, a useful transition to a new generation better trained in modern science, technology, and administration.

I have always been fascinated by the story of Deng Xiaoping and his role as the leader of China and how he steered the country towards its economic growth and be the success story it is today. So I decided to pick this book up a couple years ago. One of the things that stood out to me, was about timing.

Sometimes, things need to fall into place, and Deng enjoyed certain circumstances that enabled him to succeed. In a time where power might not have been consolidated, or if certain other key and capable individuals were not available to lead the country with him, then we might not be reading a book about him today and his pivotal role in transforming China.

Proponents of patents push back by noting that while slowing innovation may be bad, without patents the reduction in investment would be much larger. To counter that claim, Boldrin and Levine use a logic partly based on our diffusion model. A useful product based on new knowledge will spread quickly through the population of buyers. That was true of the radio, television, Google’s search engine, and Facebook. This creates a first-mover advantage. The innovator can still benefit, but only by producing something with the idea. With a patent, an inventor can wait for others to implement the idea and profit. Boldrin and Levine also question how much credit the inventor deserves anyway. If breakthroughs were the result of a solitary genius, and most ideas would never have been produced without incentives, then the case for patents is stronger. The rugged landscape model suggests that difficult problems may have multiple workable solutions. New inventions, particularly those that combine existing ideas and technology such as the car, the telephone, and online auctions, may be natural occurrences not acts of genius. Any number of people might have made these innovations given the ideas swirling around in the community of thinkers. The simultaneity of major discoveries—calculus (Isaac Newton and Gottfried Leibniz), the telephone (Alexander Graham Bell and Elisha Gray), and the natural selection theory of evolution (Charles Darwin and Alfred Russel Wallace)—supports that inference. In sum, many-model thinking shows advantages and disadvantages to patents. The deeper, more nuanced understanding the models provide argues for a more flexible patent policy.

On patents and how knowing different models can help us develop a more nuanced understanding on patent policy. If we assume that innovation is the result of solitary genius, then patents will definitely be helpful in promoting innovation on an aggregate basis as there will be a higher incentive for people to create new things and not get ripped off. However, if innovation is a result of spontaneity with systems and things (including humans) falling into place, then patents might actually be an impediment to actually taking innovation forward.

Markov models describe dynamic systems that move between states according to fixed transition probabilities. If we additionally assume that the process can move between any two states and that the process does not produce a cycle, then a Markov model attains a unique statistical equilibrium. In the equilibrium people or entities move between states in such a way that the probability distribution across states does not change. It follows that as a process nears that equilibrium, the changes in the probabilities diminish. Represented as a graph, the slope of the curve flattens. Recall our earlier discussion of California’s population growth when we learned linear models. California’s population growth has slowed because as the population of California has grown, the number of people leaving California has increased. That result holds true even if the proportion of Californians leaving does not change. When applying Markov models to explain phenomena or predict trends, a modeler’s selection of the states proves critical. The choice of states determines the transition probabilities between those states. A Markov model of drug addiction could assume two states: being a user or being clean. A more elaborate model might distinguish users by frequency of use. Regardless of the choice over states, if the four assumptions hold (and in this instance, the key test would be whether transition probabilities remain fixed), then the system will produce a unique statistical equilibrium. Any one-time change in the state of a system has at most a temporary effect. Reducing drug use in equilibrium would require changing transition probabilities. Continuing with that same logic, we can infer that a one-day event to spur interest in education may lack meaningful impact. Volunteers coming into a community and cleaning up a park may produce few long-term benefits. Any one-time influx of money, regardless of its size, will dissipate in its effect unless it changes transition probabilities. In 2010, Mark Zuckerberg donated $100 million to the Newark, New Jersey, public schools, an amount that was matched by other donors. That one-shot donation, which amounted to approximately $6,000 per student, has produced few measurable effects on test scores. Markov models guide action by distinguishing between policies that change transition probabilities, which can have long-term effects, and those change the state and can only have short-term effects. If transition probabilities cannot be changed, then we must reset the state on a regular schedule to change outcomes.

How understanding Markov models can help us in how we think about things. Markov models is simply a way of assigning transition probabilities to states. If I was in a location A, what is the probability of me moving to location etc. When we think about solving problems such as alcoholism via transition probabilities and increasing the probability of someone moving to sobriety and also not reverting back, we might reframe how we think about issues.

Then, one-off investments or efforts might not really make lasting impact and we should think more about lasting initiatives where we can increase the probability of people moving in the direction we want them to.

Jim Collins identified characteristics of consistently successful companies, such as having humble leaders, getting the right people on the team, and maintaining discipline (what Collins called “rinsing your cottage cheese” in homage to six-time Ironman triathlon champion Dave Scott’s habit of rinsing his cottage cheese to reduce the fat content). Collins singled out eleven great companies that kept to his principles. In the decade following the publication of his book, only one of the eleven produced strong growth. One was bought out. One was taken over by the government, and the other eight generated zero returns. The fact that the great firms shared attributes does not imply that those attributes contribute to success. Perhaps the lowest-performing firms also share those attributes. Selecting the best firms and looking at their attributes is not model thinking. Model thinking would derive attributes that cause success, such as talented workers. It would then test those conclusions against data, and if possible look for natural experiments—instances where the relevant attributes change randomly. Other models, such as the dancing and rugged landscape models we cover in Chapter 28, call into question Collins’ core assumptions. If the economy is complex, traits that prove successful today need not work in the future. What creates great success now—big rocks first—may not be a good strategy in ten years. As a rule, we should apply many models before making broad pronouncements, lest we risk correspondingly large errors. We should also avoid being fooled by patterns. What appears to be a trend might well be random.

The pitfalls of making judgements from small sample sizes and trying to identify common traits of only successful examples or companies. Also, over time, the traits for success might change, so jumping to conclusions too early on or refusing to adjust your internal models might leave you red-faced in the end.

Investment firms that hire away superstar fund managers based on the belief that investment success depends mostly on talent have not had very promising results. Empirical evidence shows that top investors also depend on networks of colleagues who provide them with specific types of information. That specific finding can be viewed within the lens of a much larger literature (some of which is model-based) showing how a person’s position in an organization influences success. Success still correlates with ability. A business idea that makes its investors millions was probably a good one. A scientist who publishes hundreds of papers and receives numerous awards has high ability. At the same time, those best positioned in the network make the largest contributions. We can measure a person’s position using betweenness and other measures of centrality. The people who occupy high-betweenness positions in a network fill what Ron Burt calls structural holes between communities, which we can identify using algorithms. Access to information and ideas from multiple communities gives people who fill structural holes power and influence. Filling a structural hole requires certain talents and abilities. A person cannot just jump in and fill any hole. She must build trust and understanding within each community. And she must be conversant in the knowledge base of each community. We can apply nearly identical logic to assess the value of firms and assign power to countries. We can see a firm’s value as intrinsic and take a balance sheet perspective by looking at assets and liabilities. We can also look at the context in which the firm operates, such as its position in the supply chain. Similarly, the power of a country depends on its resources and its alliances. For both firms and countries, intrinsic attributes and connectedness correlate. Those who occupy powerful positions in the network also possess important attributes. Our analysis as well as most of the literature considers the node as the unit of analysis. The edges can be critical as well. Taking an even broader perspective, the network itself may be an appropriate unit of analysis. For example, teacher networks that allow ideas and information to flow between classrooms can improve educational outcomes, and so a well-connected administrator can effectively coordinate curriculum reform. Similarly, a second-grade teacher knows a lot of information about the students from his class who are going on to third grade; that information could help the third-grade teacher. A mathematics teacher knows what concepts students have yet to grasp; that information could help the science teacher structure her lessons. Good schools, therefore, have strong faculty networks. This is just one example of how network models can improve our thinking.

Thinking in terms of networks and systems rather than just logic. Sometimes, we tend to frame our minds towards things such as individuals, and whether they are simply competent or not. But competence can be a very subjective thing, and can manifest (or not) in vastly different situations. Someone whos great for leading a company of a 1000 people might not be competent for a small team. In this, taking a broader perspective of the network, and viewing people as nodes that are inserted into a network can be an alternative way of assessing talent.

The formula for the standard deviation of the mean implies that large populations have much lower standard deviations than small ones. From this, we can infer that we should see more good things and more bad things in small populations. And in fact we do. The safest places to live are small towns, as are the least safe. The counties with the highest rates of obesity and cancer have small populations. These facts can all be explained by differences in standard deviations. Failure to take sample size into account and inferring causality from outliers can lead to incorrect policy actions. For this reason, Howard Wainer refers to the formula for the standard deviation of the mean the “most dangerous equation in the world.” For example, in the 1990s the Gates Foundation and other nonprofits advocated breaking up schools into smaller schools based on evidence that the best schools were small. To see the flawed reasoning, imagine that schools come in two sizes—small schools with 100 students and large schools with 1,600 students—and that student scores at both types of schools are drawn from the same distribution with a mean score of 100 and a standard deviation of 80. At small schools, the standard deviation of the mean equals 8 (the standard deviation of the student scores, 80, divided by 10, the square root of the number of students). If we assign the label “high-performing” to schools with means above 110 and the label “exceptional” to schools with means above 120, then only small schools will meet either threshold. For the small schools, an average score of 110 is 1.25 standard deviations above the mean; such events occur about 10% of the time. A mean score of 120 is 2.5 standard deviations above the mean; an event of that size should occur about once in 150 schools. When we do these same calculations for large schools, we find that the “high-performing” threshold lies five standard deviations above the mean and the “exceptional” threshold lies ten standard deviations above the mean. Such events would, in practice, never occur. Thus, the fact that the very best schools are small is not evidence that smaller schools perform better. The very best schools will be small even if size has no effect solely because of the square root rules.

Why looking at successful people and trying to copy their traits might not always make sense. Sometimes, just having a smaller sample sizer allows for larger deviations from the mean, which means you can be more outstanding or terrible.

This also reminds me of the logical flaw I only realised years later after I read some of these books by “business gurus” on how to build “great companies”. While some of the advice seem to make sense on the surface, simply identifying the common traits of the most successful companies without basing it on a total percentage of companies that have either succeeded or failed means we only get to see one side of the picture. Advice such as “focus only one thing you’re good at” sounds like great advice when you have succeeded. Fail instead, and people will ask “why are you only good at one thing”. Context matters.