The habits of thinking we need to thrive in today’s world differ markedly from those of the past. Leonard Mlodinow, a theoretical physicist, who, in his recent book, Elastic, suggests just what those new habits of thinking are. This article, then, intends to share some habits of thinking, and some habits more generally that will help us thrive in today’s world. Some may not be novel or surprising to those who read around this area. I did, however, find his book refreshing and thought that it complemented some of the articles I’ve written before. His insights also complement material from another book, Charles Duhigg’s Smarter Faster Better, which will also be summarised here.  

Today’s World

Recall the time before the ubiquity of the internet. When we travelled, we needed to plan our routes on physical maps, talk to travel agents and call airlines and hotels. Today, the internet enables the travel industry to push vacation planning online. Prospective vacationers wade through the sludge of competing offers and promotions, all the while attempting to elude price hikes from demand. After a certain point, most give in and take the offer that somewhat resembles their original plan, knowing full well that digging deeper could yield them a better deal. In Mlodinow’s words, ‘If you didn’t need a vacation when you started planning one, you might by the time you are done.’

More broadly, rapid changes to industries facilitated by advances in technology create new and arguably unprecedented challenges to those looking to flourish in this new landscape. Mlodinow identifies one overarching habit of thinking, or cognitive style that, while important in all ages, is particularly essential today: elastic thinking. Elastic thinking is the ability to

• Let go of comfortable ideas and become accustomed to ambiguity and contradiction
• To reframe long accepted assumptions and paradigms
• To rely on imagination and logic to generate and integrate a variety of ideas, and
• To be willing to experiment and be tolerant of failure     

Elastic Thinking

Cognition researchers hold that there are two main styles of thinking: top down and bottom up. These have appeared in various forms in the literature, most notably, Amos Tversky and Daniel Kahneman’s System 1 and System 2, which gained popularity with the latter’s book Thinking, Fast and Slow. They also feature as the ‘focussed’ and ‘diffused’ modes of cognition that Barbary Oakley encourages us to leverage in her Learning How to Learn course. Top down (or System 2) processing involves the brain’s high-level executive structures dictating the approach. Thinking that requires careful steps to be accurate, like complex mental calculations, use the top down approach. By contrast, in the bottom up approach, single neurons fire without direction from on high but with some valuable input from the emotional (i.e., reward) centres of the brain. Evolution is economical, and the bottom up approach, which is the dominant mode of cognition for most living creatures, is an efficient way for them to win at the game of life. This non-executive style of thinking, while unable to produce the hard-won results of slow, cumulative, and disciplined thought characteristic of the top down approach, can, regardless, produce insights that are paradigm shifting.       

But while the bottom up approach yields insights that the more rigid and inflexible top down approach cannot, it is considered the more ancient and basic structure in evolutionary history. Other animals that do not have a developed pre-frontal cortex and therefore no high-level executive structures, rely almost exclusively on the bottom up approach to find protection from predators, to sustain themselves, and to reproduce. Unfortunately, pre-programmed scripts arising from the bottom up approach can only be effective in predictable situations. This partly explains why over 99 percent of species that have ever lived are now extinct.   

Some of the ‘higher’ animals, animals whose brains more closely resemble the ones we possess, don’t rely on programmed scripts alone but have the flexibility to change their behaviour to fit the context. Mlodinow provides the example of a lioness stalking her prey. By changing her long-term memory to incorporate her varied hunting scenarios, i.e., the environment, the prey, and their collective challenge, the lioness is in fact, learning. This affords her the flexibility to catch her prey and provide for her pride. Both top down and bottom up processes are involved.

And this, in a nutshell, is elastic thinking: leveraging the divergent results of bottom up, or scripted, thinking with top down analysis, such that paradigm shifting ideas can be generated and explored. Our brains are problem-solving machines, and throughout history, they’ve formulated solutions humanity didn’t even know they needed or were hitherto possible, including the bathroom, air travel, and tons of other innovations we take for granted in our daily lives.  

If we are to continue innovating today and successfully solving tomorrow’s problems, given the pace of change, we need to accelerate our ability to think elastically. This will involve both the creative divergence of bottom up thinking and the executive control of top down thinking. Each is particularly good at solving the classes of problems they’ve been designed to solve. But each also manifests the kinds of maladaptive behaviours which we are all too familiar. In the case of bottom up processing, consider the example of the cycle of conflict in romantic or familial relationships:

one partner in a relationship may do something to irritate an emotional “raw spot” in his or her counterpart, triggering an angry but predictable reaction. Unfortunately, that anger often serves as a trigger for a reaction in the first partner, who takes the anger personally rather than seeing it as a mindless reaction based on an automated script. The result, again, is an escalation and a familiar cycle of conflict and argument.

This stimulus-response feedback loop resembles can also be seen in the behaviour of ants. Such eusocial insects make programmed decisions based on their immediate environment, allowing them to coordinate efforts without a central executive and, for the most part, thrive. The human brain comprises neurons that act like ants, but unlike ants, neurons can communicate with thousands of other neurons via axons and dendrites, enabling some hierarchical organization by virtue of connecting to various structures in the brain.  

But top down processing has its own shortcomings too. It only works remarkably well when the rules of the “game” are already determined. Take a look at the sublime ability of chess grandmasters. Unfortunately, in situations where the rules must be created or re-conceptualised, top down processing alone lacks the flexibility to deliver. The difference would be akin to finishing a game of chess versus composing a symphony from scratch. Today’s advances in algorithmic artificial intelligence resemble more of the top down than bottom up approach, and, while such AIs can run enough simulations to know how to win any human in a rules-bounded system like chess, it will not be able to compose a musical piece that has artistic merit without some significant input from a programmer. AI researchers opine that the most complex computers today are really very powerful calculators. They can solve all sorts of problems, but only if an agent, i.e., the person programming the computer, sets up the problem first:

Consider the following paragraph:

Aoccdrnig to a rseheearcr at Cmabrigde Uinerevtisy it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a tatol mses and you can sitll raed it wouthit porbelm.

There are many computer programs that can read printed text aloud, but they choke when presented with such a serious deviation from standard spelling. We humans, by contrast, have very little difficulty with it.

This is because we possess both bottom up and top down processes, as do other animals, most notably mammals, but in a greater degree. The emotion-based reward centres of the brain which are linked with bottom up thinking enable elasticity by generating ideas and then using top down processes to filter through and choose among them.

Enough variability in the human phenotype ensures that people are endowed with wildly different temperaments – a la Fisher – and that some will be more inclined toward sticking to the tried and true, while others are on the lookout for new ways of doing things. These temperaments come with a reliable constellation of personality traits, some of which have been mentioned in the previous article about the anthropology of romantic love. Hence, having a complementary balance of temperaments in a company is a good idea. While much progress can be made with analytical thinking, and much interest can be generated from the creative process, elastic thinking best emerges when people of varied temperaments collaborate.

Mlodinow demonstrates now elastic thinking contributed in paradigm shifting ways to mathematics and physics. He gives the example of how the 16^th century mathematician Rafael Bombelli solved the previously unsolvable equation x² = –1 by conceiving of numbers as not concrete physical things but as abstractions that obey rules. This new way of looking at numbers allowed him to denote i, an imaginary number, as the answer to the above equation.

Imaginary numbers are now ubiquitous in mathematics. It is taught in school to students the world over. Millions of people have learned what the brightest thinkers before Bombelli could not even conceive. Imaginary numbers are also important in many areas of physics, for example, to explain wave phenomena and provide the foundation for quantum theory and electronics.   

Cultivating Elastic Thinking

Increasing mindfulness is the first step to developing elastic thinking. This can involve regular mindfulness practices and blocking off moments when you are free to let your mind wander, after having given it a problem to solve. Walking has been shown to be useful in this regard. Anecdotally, being obliged to walk a pet daily not only improves your health, but also encourages your creativity. If you are confident that you can raise a pet without causing it unnecessary suffering, consider getting one, or walking the one you already have! Sitting in a dark expansive room or closing your eyes helps too.     

Having the opportunity to problem solve without any time pressure is good. This means that you should start on difficult projects as soon as possible, so that you have the longest time to let your diffused mode (Cf. Learning How to Learn) solve your problems for you. Attention residue (Cf. Deep Work) prevents you from marshalling all your intellectual resources on some problem, so be sure to shut them out when you intend to focus.  

Don’t get caught in Einstellung (Cf. Learning How to Learn), i.e., predisposed ways of problem solving brought about by expertise in a subject which prevents the problem solver from considering novel ideas that could lead to better solutions. A 2014 study published in the Journal of the American Medical Association suggests that patients are, on average, better off if they are treated by novice rather than experienced doctors. The study marshalled ten years’ worth of data featuring several thousand hospital admissions and found that the mortality rate was a third lower when the top doctors were not involved (i.e., when they were out of town for conferences, etc.). The researchers hypothesize that experienced doctors make judgments quickly and either ignore or are unable to change their minds when presented with potentially disconfirming information. By contrast, junior doctors, while slower in their analysis, tend to be more open-minded when treating patients with subtler symptoms and often catch what the experienced doctors miss.

Test your elastic thinking with fun puzzles like the nine-dot problem. It works best if you haven’t encountered this puzzle before. Connect all the dots pictured below with four continuous straight lines without retracing any line (crossing lines are fine) or lifting your pencil from the paper (or, in this case, your finger from the screen):

Very few people can solve this puzzle, even if they are told to think outside the box. I’ve put the solution at the bottom of this article. Get ready to say ‘oh!’

Within each of us are two distinct thinkers, both a logician and a poet, competitors out of whose struggle emerge our thoughts and ideas. We can all switch between the mode of thought in which we spontaneously generate original ideas and that in which we rationally scrutinize them, and our success hinges in part upon our ability to shift modes as needed.

Mlodinow’s book is a great read with much more insights to elucidate and help us cultivate elastic thinking, so check it out if you can.

I’d like to shift focus now and share some complementary insights from Charles Duhigg’s book about how to increase one’s productivity. He offers eights tips but this article will home in on three: motivation, teams, and focus.  

Supplement from Smarter Better Faster

• Motivation

Social science indicates that giving people a sense of control, however small, increases their motivation. This might explain why some people prefer driving to flying even though flying is – demonstrably – far safer than driving: we’re not in control when we’re flying. Anecdotally, it might also explain why freedom from control is a common trope among Western films.    

Applying this to the realm of productivity, when people feel that they are in control, they will likely work harder, approach challenges more confidently, and recover from setbacks faster. So, give people the freedom to choose how they should approach their work and offer them feedback that is reliably tracked to their performance.  

Animals and humans demonstrate a preference for choice over non-choice, even when that choice confers no additional reward

Mauricio Delgado, Psychology Professor

That’s one of the reasons why your cable company asks all those questions when you sign up for service. If they ask if you prefer a paperless bill to an itemized statement, or the ultra-package versus the platinum lineup, or HBO to Showtime, you’re more likely to be motivated to pay the bill each month. As long as we feel a sense of control, we’re more willing to play along.

Choices are powerful because they are linked to the emotion-based reward centres of the brain. Some people who are chronically unmotivated have later been found to have an emotional dysfunction: they experience no pleasure and cannot be motivated to make the kind of choices that would normally bring people pleasure. While this might be caused by genetic factors outside one’s control, situations where people grow up with no autonomy and have internalized that sense of helplessness can develop emotional dysfunction too.      

So, reward people who take initiative and praise those who demonstrate self-motivation. Create an environment where people can make meaningful choices and self-motivation will flourish.  

• Teams

Productivity is often dependent on the performance of teams. So how can teams come together to bring their best? What are some norms that facilitate this? It helps when team leaders are direct and straightforward yet take the time to help and support their members (Cf. Radical Candour).

This allows members to be comfortable taking risks for the collective goals of the group and implement solutions that they wouldn’t normally be able to find. Teams leaders should encourage all to speak up (i.e., speak their minds) and model open-minded listening. These actions bond the teams and encourage members to take calculated chances. The above norms all come together to create what is called ‘psychological safety,’ i.e., interpersonal trust and mutual respect. And this is one of the most reliable predictors of team performance, over and above even the aggregate intelligence (i.e., IQ) of the team. Such norms enable team members, who are average individually, to outperform teams comprised of highly intelligent or experienced people who do not practice these norms. In Duhigg’s words, ‘the right norms could raise the collective intelligence of mediocre thinkers [while] the wrong norms could hobble a group made up of people who, on their own, were all exceptionally bright.’

Furthermore, teams where members spoke in roughly the same length of time and demonstrated social sensitivity to the behavioural cues of their teammates, performed better. Other factors include teams believing that their work is important, that their work is personally meaningful, that they have been given clear goals and defined roles, and that they can trust and depend on each other.  

• Focus

Producing work that cannot be easily replicated and therefore valuable (Cf. Deep Work) requires sustained focus. Unfortunately, life presents myriad distractions that stop us from having this focus. Build mental models that motivate you to stay focused. This website featuring some of the best mental models might inspire you.

Mental Models: Learn How to Think Better and Gain a Mental Edge

You can’t delegate thinking […] Computers fail, checklists fail, everything can fail. But people can’t. We have to make decisions, and that includes deciding what deserves our attention. The key is forcing yourself to think. As long as you’re thinking, you’re halfway home.

• Additional Tips

Beware the need for closure. Some are temperamentally disposed to seek closure when problem-solving and might make decisions that aren’t well thought through. People who demonstrate a high need for closure in social-scientific studies also exhibited close-mindedness and authoritarian tendencies. So, while deciding quickly can be a boon in time-sensitive situations, deliberately taking one’s time to research further, marshal more input, and review options, are more often the superior courses of action.   

Company cultures can be broadly categorised into five: a star culture, where they hire from elite universities, pay them well and give them lots of freedom; an engineering culture, which adopts an engineering mindset when solving problems; a bureaucratic culture, wherein the culture emerges from the thick of middle managers and where job descriptions are spelled out in great detail; an autocratic culture, which is similar to the bureaucratic one but whose processes ultimately reflect the goals of one person, the person on top; and a commitment culture, one that prioritizes slow and steady growth, and which aims to have their employees satisfied enough to stay and contribute for life. A study tracking the differential success of each of these corporate cultures in Silicon Valley over a decade yielded some interesting results.

As expected, star cultures were some of the studies’ biggest winners. Unfortunately, many star cultures failed spectacularly too, possibly because of internal conflict from rival ‘stars.’ By contrast, the clear and consistent winners were the ones with the commitment culture.

Hands down, a commitment culture outperformed every other type of management style in almost every meaningful way […] “They were also the fastest companies to go public, had the highest profitability ratios, and tended to be leaner, with fewer middle managers, because when you choose employees slowly, you have time to find people who excel at self-direction.” [Furthermore] employees in commitment firms wasted less time on internal rivalries because everyone was committed to the company, rather than to personal agendas.  

In short, these cultures have all the ingredients required for psychological safety, including the careful selection of employees who are sold on the mission such that the decentralization of authority would not risk bad actors ruining the company for personal gain.

These are the insights I found worth sharing from these two books. In the process of writing this article, I’ve learned some things, and hope you did too.

Appendix: Solution to the nine-dot problem