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Being Smart is Not Enough

Reading Time: 3 minutes

When hiring a team, we tend to favor the geniuses who hatch innovative ideas, but overlook the butterflies, the crucial ones who share and implement them. Here’s why it’s important to be both smart AND social.

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In business, it’s never enough to have a great idea. For any innovation to be successful, it has to be shared, promoted, and bought into by everyone in the organization. Yet often we focus on the importance of those great ideas and seem to forget about the work that is required to spread them around.

Whenever we are building a team, we tend to look for smarts. We are attracted to those with lots of letters after their names or fancy awards on their resumes. We assume that if we hire the smartest people we can find, they will come up with new, better ways of doing things that save us time and money.

Conversely, we often look down on predominantly social people. They seem to spend too much time gossiping and not enough time working. We assume they’ll be too busy engaging on social media or away from their desks too often to focus on their duties, and thus we avoid hiring them.

Although we aren’t going to tell you to swear off smarts altogether, we are here to suggest that maybe it’s time to reconsider the role that social people play in cultural growth and the diffusion of innovation.

In his book, The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter, Joseph Henrich explores the role of culture in human evolution. One point he makes is that it’s not enough for a species to be smart. What counts far more is having the cultural infrastructure to share, teach, and learn.

Consider two very large prehuman populations, the Geniuses and the Butterflies. Suppose the Geniuses will devise an invention once in 10 lifetimes. The Butterflies are much dumber, only devising the same invention once in 1000 lifetimes. So, this means that the Geniuses are 100 times smarter than the Butterflies. However, the Geniuses are not very social and have only 1 friend they can learn from. The Butterflies have 10 friends, making them 10 times more social.

Now, everyone in both populations tries to obtain an invention, both by figuring it out for themselves and by learning from friends. Suppose learning from friends is difficult: if a friend has it, a learner only learns it half the time. After everyone has done their own individual learning and tried to learn from their friends, do you think the innovation will be more common among the Geniuses or the Butterflies?

Well, among the Geniuses a bit fewer than 1 out of 5 individuals (18%) will end up with the invention. Half of those Geniuses will have figured it out all by themselves. Meanwhile, 99.9% of Butterflies will have the innovation, but only 0.1% will have figured it out by themselves.

Wow.

What if we take this thinking and apply to the workplace? Of course you want to have smart people. But you don’t want an organization full of Geniuses. They might come up with a lot, but without being able to learn from each other easily, many of their ideas won’t have any uptake in the organization. Instead, you’d want to pair Geniuses with Butterflies—socially attuned people who are primed to adopt the successful behaviors of those around them.

If you think you don’t need Butterflies because you can just put Genius innovations into policy and procedure, you’re missing the point. Sure, some brilliant ideas are concrete, finite, and visible. Those are the ones you can identify and implement across the organization from the top down. But some of the best ideas happen on the fly in isolated, one-off situations as responses to small changes in the environment. Perhaps there’s a minor meeting with a client, and the Genius figures out a new way of describing your product that really resonates. The Genius though, is not a teacher. It worked for them and they keep repeating the behavior, but it doesn’t occur to them to teach someone else. And they don’t pick up on other tactics to further refine their innovation.

But the Butterfly who went to the meeting with the Genius? They pick up on the successful new product description right away. They emulate it in all meetings from then on. They talk about it with their friends, most of whom are also Butterflies. Within two weeks, the new description has taken off because of the propensity for cultural learning embedded in the social Butterflies.

The lesson here is to hire both types of people. Know that it’s the Geniuses who innovate, but it’s the Butterflies who spread that innovation around. Both components are required for successfully implementing new, brilliant ideas.

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The Spiral of Silence

Reading Time: 5 minutes

Our desire to fit in with others means we don’t always say what we think. We only express opinions that seem safe. Here’s how the spiral of silence works and how we can discover what people really think.

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Be honest: How often do you feel as if you’re really able to express your true opinions without fearing judgment? How often do you bite your tongue because you know you hold an unpopular view? How often do you avoid voicing any opinion at all for fear of having misjudged the situation?

Even in societies with robust free speech protections, most people don’t often say what they think. Instead they take pains to weigh up the situation and adjust their views accordingly. This comes down to the “spiral of silence,” a human communication theory developed by German researcher Elisabeth Noelle-Neumann in the 1960s and ’70s. The theory explains how societies form collective opinions and how we make decisions surrounding loaded topics.

Let’s take a look at how the spiral of silence works and how understanding it can give us a more realistic picture of the world.

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How the spiral of silence works

According to Noelle-Neumann’s theory, our willingness to express an opinion is a direct result of how popular or unpopular we perceive it to be. If we think an opinion is unpopular, we will avoid expressing it. If we think it is popular, we will make a point of showing we think the same as others.

Controversy is also a factor—we may be willing to express an unpopular uncontroversial opinion but not an unpopular controversial one. We perform a complex dance whenever we share views on anything morally loaded.

Our perception of how “safe” it is to voice a particular view comes from the clues we pick up, consciously or not, about what everyone else believes. We make an internal calculation based on signs like what the mainstream media reports, what we overhear coworkers discussing on coffee breaks, what our high school friends post on Facebook, or prior responses to things we’ve said.

We also weigh up the particular context, based on factors like how anonymous we feel or whether our statements might be recorded.

As social animals, we have good reason to be aware of whether voicing an opinion might be a bad idea. Cohesive groups tend to have similar views. Anyone who expresses an unpopular opinion risks social exclusion or even ostracism within a particular context or in general. This may be because there are concrete consequences, such as losing a job or even legal penalties. Or there may be less official social consequences, like people being less friendly or willing to associate with you. Those with unpopular views may suppress them to avoid social isolation.

Avoiding social isolation is an important instinct. From an evolutionary biology perspective, remaining part of a group is important for survival, hence the need to at least appear to share the same views as anyone else. The only time someone will feel safe to voice a divergent opinion is if they think the group will share it or be accepting of divergence, or if they view the consequences of rejection as low. But biology doesn’t just dictate how individuals behave—it ends up shaping communities. It’s almost impossible for us to step outside of that need for acceptance.

A feedback loop pushes minority opinions towards less and less visibility—hence why Noelle-Neumann used the word “spiral.” Each time someone voices a majority opinion, they reinforce the sense that it is safe to do so. Each time someone receives a negative response for voicing a minority opinion, it signals to anyone sharing their view to avoid expressing it.

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An example of the spiral of silence

A 2014 Pew Research survey of 1,801 American adults examined the prevalence of the spiral of silence on social media. Researchers asked people about their opinions on one public issue: Edward Snowden’s 2013 revelations of US government surveillance of citizens’ phones and emails. They selected this issue because, while controversial, prior surveys suggested a roughly even split in public opinion surrounding whether the leaks were justified and whether such surveillance was reasonable.

Asking respondents about their willingness to share their opinions in different contexts highlighted how the spiral of silence plays out. 86% of respondents were willing to discuss the issue in person, but only about half as many were willing to post about it on social media. Of the 14% who would not consider discussing the Snowden leaks in person, almost none (0.3%) were willing to turn to social media instead.

Both in person and online, respondents reported far greater willingness to share their views with people they knew agreed with them—three times as likely in the workplace and twice as likely in a Facebook discussion.

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The implications of the spiral of silence

The end result of the spiral of silence is a point where no one publicly voices a minority opinion, regardless of how many people believe it. The first implication of this is that the picture we have of what most people believe is not always accurate. Many people nurse opinions they would never articulate to their friends, coworkers, families, or social media followings.

A second implication is that the possibility of discord makes us less likely to voice an opinion at all, assuming we are not trying to drum up conflict. In the aforementioned Pew survey, people were more comfortable discussing a controversial story in person than online. An opinion voiced online has a much larger potential audience than one voiced face to face, and it’s harder to know exactly who will see it. Both of these factors increase the risk of someone disagreeing.

If we want to gauge what people think about something, we need to remove the possibility of negative consequences. For example, imagine a manager who often sets overly tight deadlines, causing immense stress to their team. Everyone knows this is a problem and discusses it among themselves, recognizing that more realistic deadlines would be motivating, and unrealistic ones are just demoralizing. However, no one wants to say anything because they’ve heard the manager say that people who can’t handle pressure don’t belong in that job. If the manager asks for feedback about their leadership style, they’re not going to hear what they need to hear if they know who it comes from.

A third implication is that what seems like a sudden change in mainstream opinions can in fact be the result of a shift in what is acceptable to voice, not in what people actually think. A prominent public figure getting away with saying something controversial may make others feel safe to do the same. A change in legislation may make people comfortable saying what they already thought.

For instance, if recreational marijuana use is legalized where someone lives, they might freely remark to a coworker that they consume it and consider it harmless. Even if that was true before the legislation change, saying so would have been too fraught, so they might have lied or avoided the topic. The result is that mainstream opinions can appear to change a great deal in a short time.

A fourth implication is that highly vocal holders of a minority opinion can end up having a disproportionate influence on public discourse. This is especially true if that minority is within a group that already has a lot of power.

While this was less the case during Noelle-Neumann’s time, the internet makes it possible for a vocal minority to make their opinions seem far more prevalent than they actually are—and therefore more acceptable. Indeed, the most extreme views on any spectrum can end up seeming most normal online because people with a moderate take have less of an incentive to make themselves heard.

In anonymous environments, the spiral of silence can end up reversing itself, making the most fringe views the loudest.

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When Technology Takes Revenge

Reading Time: 6 minutes

While runaway cars and vengeful stitched-together humans may be the stuff of science fiction, technology really can take revenge on us. Seeing technology as part of a complex system can help us avoid costly unintended consequences. Here’s what you need to know about revenge effects.

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By many metrics, technology keeps making our lives better. We live longer, healthier, richer lives with more options than ever before for things like education, travel, and entertainment. Yet there is often a sense that we have lost control of our technology in many ways, and thus we end up victims of its unanticipated impacts.

Edward Tenner argues in Why Things Bite Back: Technology and the Revenge of Unintended Consequences that we often have to deal with “revenge effects.” Tenner coined this term to describe the ways in which technologies can solve one problem while creating additional worse problems, new types of problems, or shifting the harm elsewhere. In short, they bite back.

Although Why Things Bite Back was written in the late 1990s and many of its specific examples and details are now dated, it remains an interesting lens for considering issues we face today. The revenge effects Tenner describes haunt us still. As the world becomes more complex and interconnected, it’s easy to see that the potential for unintended consequences will increase.

Thus, when we introduce a new piece of technology, it would be wise to consider whether we are interfering with a wider system. If that’s the case, we should consider what might happen further down the line. However, as Tenner makes clear, once the factors involved get complex enough, we cannot anticipate them with any accuracy.

Neither Luddite nor alarmist in nature, the notion of revenge effects can help us better understand the impact of intervening with complex systems But we need to be careful. Although second-order thinking is invaluable, it cannot predict the future with total accuracy. Understanding revenge effects is primarily a reminder of the value of caution and not of specific risks.

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Types of revenge effects

There are four different types of revenge effects, described here as follows:

  1. Repeating effects: occur when more efficient processes end up forcing us to do the same things more often, meaning they don’t free up more of our time. Better household appliances have led to higher standards of cleanliness, meaning people end up spending the same amount of time—or more—on housework.
  2. Recomplicating effects: occur when processes become more and more complex as the technology behind them improves. Tenner gives the now-dated example of phone numbers becoming longer with the move away from rotary phones. A modern example might be lighting systems that need to be operated through an app, meaning a visitor cannot simply flip a switch.
  3. Regenerating effects: occur when attempts to solve a problem end up creating additional risks. Targeting pests with pesticides can make them increasingly resistant to harm or kill off their natural predators. Widespread use of antibiotics to control certain conditions has led to be resistant strains of bacteria that are harder to treat.
  4. Rearranging effects: occur when costs are transferred elsewhere so risks shift and worsen. Air conditioning units on subways cool down the trains—while releasing extra heat and making the platforms warmer. Vacuum cleaners can throw dust mite pellets into the air, where they remain suspended and are more easily breathed in. Shielding beaches from waves transfers the water’s force elsewhere.

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Recognizing unintended consequences

The more we try to control our tools, the more they can retaliate.

Revenge effects occur when the technology for solving a problem ends up making it worse due to unintended consequences that are almost impossible to predict in advance. A smartphone might make it easier to work from home, but always being accessible means many people end up working more.

Things go wrong because technology does not exist in isolation. It interacts with complex systems, meaning any problems spread far from where they begin. We can never merely do one thing.

Tenner writes: “Revenge effects happen because new structures, devices, and organisms react with real people in real situations in ways we could not foresee.” He goes on to add that “complexity makes it impossible for anyone to understand how the system might act: tight coupling spreads problems once they begin.”

Prior to the Industrial Revolution, technology typically consisted of tools that served as an extension of the user. They were not, Tenner argues, prone to revenge effects because they did not function as parts in an overall system like modern technology. He writes that “a machine can’t appear to have a will of its own unless it is a system, not just a device. It needs parts that interact in unexpected and sometimes unstable and unwanted ways.”

Revenge effects often involve the transformation of defined, localized risks into nebulous, gradual ones involving the slow accumulation of harm. Compared to visible disasters, these are much harder to diagnose and deal with.

Large localized accidents, like a plane crash, tend to prompt the creation of greater safety standards, making us safer in the long run. Small cumulative ones don’t.

Cumulative problems, compared to localized ones, aren’t easy to measure or even necessarily be concerned about. Tenner points to the difference between reactions in the 1990s to the risk of nuclear disasters compared to global warming. While both are revenge effects, “the risk from thermonuclear weapons had an almost built-in maintenance compulsion. The deferred consequences of climate change did not.”

Many revenge effects are the result of efforts to improve safety. “Our control of the acute has indirectly promoted chronic problems”, Tenner writes. Both X-rays and smoke alarms cause a small number of cancers each year. Although they save many more lives and avoiding them is far riskier, we don’t get the benefits without a cost. The widespread removal of asbestos has reduced fire safety, and disrupting the material is often more harmful than leaving it in place.

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Not all effects exact revenge

A revenge effect is not a side effect—defined as a cost that goes along with a benefit. The value of being able to sanitize a public water supply has significant positive health outcomes. It also has a side effect of necessitating an organizational structure that can manage and monitor that supply.

Rather, a revenge effect must actually reverse the benefit for at least a small subset of users. For example, the greater ease of typing on a laptop compared to a typewriter has led to an increase in carpal tunnel syndrome and similar health consequences. It turns out that the physical effort required to press typewriter keys and move the carriage protected workers from some of the harmful effects of long periods of time spent typing.

Likewise, a revenge effect is not just a tradeoff—a benefit we forgo in exchange for some other benefit. As Tenner writes:

If legally required safety features raise airline fares, that is a tradeoff. But suppose, say, requiring separate seats (with child restraints) for infants, and charging a child’s fare for them, would lead many families to drive rather than fly. More children could in principle die from transportation accidents than if the airlines had continued to permit parents to hold babies on their laps. This outcome would be a revenge effect.

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In support of caution

In the conclusion of Why Things Bite Back, Tenner writes:

We seem to worry more than our ancestors, surrounded though they were by exploding steamboat boilers, raging epidemics, crashing trains, panicked crowds, and flaming theaters. Perhaps this is because the safer life imposes an ever increasing burden of attention. Not just in the dilemmas of medicine but in the management of natural hazards, in the control of organisms, in the running of offices, and even in the playing of games there are, not necessarily more severe, but more subtle and intractable problems to deal with.

While Tenner does not proffer explicit guidance for dealing with the phenomenon he describes, one main lesson we can draw from his analysis is that revenge effects are to be expected, even if they cannot be predicted. This is because “the real benefits usually are not the ones that we expected, and the real perils are not those we feared.”

Chains of cause and effect within complex systems are stranger than we can often imagine. We should expect the unexpected, rather than expecting particular effects.

While we cannot anticipate all consequences, we can prepare for their existence and factor it into our estimation of the benefits of new technology. Indeed, we should avoid becoming overconfident about our ability to see the future, even when we use second-order thinking. As much as we might prepare for a variety of impacts, revenge effects may be dependent on knowledge we don’t yet possess. We should expect larger revenge effects the more we intensify something (e.g., making cars faster means worse crashes).

Before we intervene in a system, assuming it can only improve things, we should be aware that our actions can do the opposite or do nothing at all. Our estimations of benefits are likely to be more realistic if we are skeptical at first.

If we bring more caution to our attempts to change the world, we are better able to avoid being bitten.

 

A Primer on Algorithms and Bias

Reading Time: 7 minutes

The growing influence of algorithms on our lives means we owe it to ourselves to better understand what they are and how they work. Understanding how the data we use to inform algorithms influences the results they give can help us avoid biases and make better decisions.

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Algorithms are everywhere: driving our cars, designing our social media feeds, dictating which mixer we end up buying on Amazon, diagnosing diseases, and much more.

Two recent books explore algorithms and the data behind them. In Hello World: Being Human in the Age of Algorithms, mathematician Hannah Fry shows us the potential and the limitations of algorithms. And Invisible Women: Data Bias in a World Designed for Men by writer, broadcaster, and feminist activist Caroline Criado Perez demonstrates how we need to be much more conscientious of the quality of the data we feed into them.

Humans or algorithms?

First, what is an algorithm? Explanations of algorithms can be complex. Fry explains that at their core, they are defined as step-by-step procedures for solving a problem or achieving a particular end. We tend to use the term to refer to mathematical operations that crunch data to make decisions.

When it comes to decision-making, we don’t necessarily have to choose between doing it ourselves and relying wholly on algorithms. The best outcome may be a thoughtful combination of the two.

We all know that in certain contexts, humans are not the best decision-makers. For example, when we are tired, or when we already have a desired outcome in mind, we may ignore relevant information. In Thinking, Fast and Slow, Daniel Kahneman gave multiple examples from his research with Amos Tversky that demonstrated we are heavily influenced by cognitive biases such as availability and anchoring when making certain types of decisions. It’s natural, then, that we would want to employ algorithms that aren’t vulnerable to the same tendencies. In fact, their main appeal for use in decision-making is that they can override our irrationalities.

Algorithms, however, aren’t without their flaws. One of the obvious ones is that because algorithms are written by humans, we often code our biases right into them. Criado Perez offers many examples of algorithmic bias.

For example, an online platform designed to help companies find computer programmers looks through activity such as sharing and developing code in online communities, as well as visiting Japanese manga (comics) sites. People visiting certain sites with frequency received higher scores, thus making them more visible to recruiters.

However, Criado Perez presents the analysis of this recruiting algorithm by Cathy O’Neil, scientist and author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, who points out that “women, who do 75% of the world’s unpaid care work, may not have the spare leisure time to spend hours chatting about manga online . . . and if, like most of techdom, that manga site is dominated by males and has a sexist tone, a good number of women in the industry will probably avoid it.”

Criado Perez postulates that the authors of the recruiting algorithm didn’t intend to encode a bias that discriminates against women. But, she says, “if you aren’t aware of how those biases operate, if you aren’t collecting data and taking a little time to produce evidence-based processes, you will continue to blindly perpetuate old injustices.”

Fry also covers algorithmic bias and asserts that “wherever you look, in whatever sphere you examine, if you delve deep enough into any system at all, you’ll find some kind of bias.” We aren’t perfect—and we shouldn’t expect our algorithms to be perfect, either.

In order to have a conversation about the value of an algorithm versus a human in any decision-making context, we need to understand, as Fry explains, that “algorithms require a clear, unambiguous idea of exactly what we want them to achieve and a solid understanding of the human failings they are replacing.”

Garbage in, garbage out

No algorithm is going to be successful if the data it uses is junk. And there’s a lot of junk data in the world. Far from being a new problem, Criado Perez argues that “most of recorded human history is one big data gap.” And that has a serious negative impact on the value we are getting from our algorithms.

Criado Perez explains the situation this way: We live in “a world [that is] increasingly reliant on and in thrall to data. Big data. Which in turn is panned for Big Truths by Big Algorithms, using Big Computers. But when your data is corrupted by big silences, the truths you get are half-truths, at best.”

A common human bias is one regarding the universality of our own experience. We tend to assume that what is true for us is generally true across the population. We have a hard enough time considering how things may be different for our neighbors, let alone for other genders or races. It becomes a serious problem when we gather data about one subset of the population and mistakenly assume that it represents all of the population.

For example, Criado Perez examines the data gap in relation to incorrect information being used to inform decisions about safety and women’s bodies. From personal protective equipment like bulletproof vests that don’t fit properly and thus increase the chances of the women wearing them getting killed to levels of exposure to toxins that are unsafe for women’s bodies, she makes the case that without representative data, we can’t get good outputs from our algorithms. She writes that “we continue to rely on data from studies done on men as if they apply to women. Specifically, Caucasian men aged twenty-five to thirty, who weigh 70 kg. This is ‘Reference Man’ and his superpower is being able to represent humanity as whole. Of course, he does not.” Her book contains a wide variety of disciplines and situations where the gender gap in data leads to increased negative outcomes for women.

The limits of what we can do

Although there is a lot we can do better when it comes to designing algorithms and collecting the data sets that feed them, it’s also important to consider their limits.

We need to accept that algorithms can’t solve all problems, and there are limits to their functionality. In Hello World, Fry devotes a chapter to the use of algorithms in justice. Specifically, algorithms designed to provide information to judges about the likelihood of a defendant committing further crimes. Our first impulse is to say, “Let’s not rely on bias here. Let’s not have someone’s skin color or gender be a key factor for the algorithm.” After all, we can employ that kind of bias just fine ourselves. But simply writing bias out of an algorithm is not as easy as wishing it so. Fry explains that “unless the fraction of people who commit crimes is the same in every group of defendants, it is mathematically impossible to create a test which is equally accurate at predicting across the board and makes false positive and false negative mistakes at the same rate for every group of defendants.”

Fry comes back to such limits frequently throughout her book, exploring them in various disciplines. She demonstrates to the reader that “there are boundaries to the reach of algorithms. Limits to what can be quantified.” Perhaps a better understanding of those limits is needed to inform our discussions of where we want to use algorithms.

There are, however, other limits that we can do something about. Both authors make the case for more education about algorithms and their input data. Lack of understanding shouldn’t hold us back. Algorithms that have a significant impact on our lives specifically need to be open to scrutiny and analysis. If an algorithm is going to put you in jail or impact your ability to get a mortgage, then you ought to be able to have access to it.

Most algorithm writers and the companies they work for wave the “proprietary” flag and refuse to open themselves up to public scrutiny. Many algorithms are a black box—we don’t actually know how they reach the conclusions they do. But Fry says that shouldn’t deter us. Pursuing laws (such as the data access and protection rights being instituted in the European Union) and structures (such as an algorithm-evaluating body playing a role similar to the one the U.S. Food and Drug Administration plays in evaluating whether pharmaceuticals can be made available to the U.S. market) will help us decide as a society what we want and need our algorithms to do.

Where do we go from here?

Algorithms aren’t going away, so it’s best to acquire the knowledge needed to figure out how they can help us create the world we want.

Fry suggests that one way to approach algorithms is to “imagine that we designed them to support humans in their decisions, rather than instruct them.” She envisions a world where “the algorithm and the human work together in partnership, exploiting each other’s strengths and embracing each other’s flaws.”

Part of getting to a world where algorithms provide great benefit is to remember how diverse our world really is and make sure we get data that reflects the realities of that diversity. We can either actively change the algorithm, or we change the data set. And if we do the latter, we need to make sure we aren’t feeding our algorithms data that, for example, excludes half the population. As Criado Perez writes, “when we exclude half of humanity from the production of knowledge, we lose out on potentially transformative insights.”

Given how complex the world of algorithms is, we need all the amazing insights we can get. Algorithms themselves perhaps offer the best hope, because they have the inherent flexibility to improve as we do.

Fry gives this explanation: “There’s nothing inherent in [these] algorithms that means they have to repeat the biases of the past. It all comes down to the data you give them. We can choose to be ‘crass empiricists’ (as Richard Berk put it ) and follow the numbers that are already there, or we can decide that the status quo is unfair and tweak the numbers accordingly.”

We can get excited about the possibilities that algorithms offer us and use them to create a world that is better for everyone.

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Aim For What’s Reasonable: Leadership Lessons From Director Jean Renoir

Reading Time: 4 minutes

Directing a film involves getting an enormous group of people to work together on turning the image inside your head into a reality. In this 1970 interview, director Jean Renoir dispenses time-tested wisdom for leaders everywhere on humility, accountability, goal-setting, and more.

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Many of us end up in leadership roles at some point in our career. Most of us, however, never get any training or instruction on how to actually be a good leader. But whether we end up offering formal or informal leadership, at some point we need to inspire or motivate people towards accomplishing a shared vision.

Directors are the leaders of movie productions. They assemble their team, they communicate their vision, and they manage the ups and downs of the filming process. Thus the experience of a successful director offers great insight into the qualities of a good leader. In 1970, film director Jean Renoir gave an interview with George Stevens Jr. of the American Film Institute where he discussed the leadership aspects of directing. His insights illustrate some important lessons. Renoir started out making silent films, and he continued filmmaking through to the 1960s. His two greatest cinematic achievements were the films The Grand Illusion (1937) and The Rules of the Game (1939). He received a Lifetime Achievement Academy Award in 1975 for his contribution to the motion picture industry.

In the interview, Renoir speaks to humility in leadership when he says, “I’m a director who has spent his life suggesting stories that nobody wanted. It’s still going on. But I’m used to it and I’m not complaining, because the ideas which were forced on me were often better than my own ideas.”

Leadership is not necessarily coming up with all the answers; it’s also important to put aside your own ego to cultivate and support the contributions from your team. Sometimes leaders have the best ideas. But often people on their team have excellent ones as well.

Renoir suggests that the role of a director is to have a clear enough vision that you can work through the imperfections involved in executing it. “A picture, often when it is good, is the result of some inner belief which is so strong that you have to show what you want, in spite of a stupid story or difficulties about the commercial side of the film.”

Good leaders don’t require perfection to achieve results. They work with what they have, often using creativity and ingenuity to fill in when reality doesn’t conform to the ideal image in their head. Having a vision is not about achieving exactly that vision. It’s about doing the best you can once you come into contact with reality.

When Renoir says, “We directors are simply midwives,” he implies that effective leadership is about giving shape to the talents and capabilities that already exist. Excellent leaders find a way to challenge and develop those on their team. In explaining how he works with actors, he says, “You must not ask an actor to do what he cannot do.” Rather, you need to work with what you have, using clear feedback and communication to find a way to bring out the best in people. Sometimes getting out of people’s way and letting their natural abilities come out is the most important thing to do.

Although Renoir says, “When I can, I shoot my scenes only once. I like to be committed, to be a slave to my decision,” he further explains, “I don’t like to make the important decisions alone.” Good leaders know when to consult others. They know to take in information from those who know more than they do and to respect different forms of expertise. But they still take accountability for their decisions because they made the final choice.

Good leaders are also mindful of the world outside the group or organization they are leading. They don’t lead in a vacuum but are sensitive to all those involved in achieving the results they are trying to deliver. For a director, it makes no sense to conceive of a film without considering the audience. Renoir explains, “I believe that the work of art where the spectator does not collaborate is not a work of art.” Similarly, we all have groups that we interact with outside of our organization, like clients or customers. We too need to run our teams with an understanding of that outside world.

No one can be good at everything, and thus effective leadership involves knowing when to ask for help. Renoir admits, “That’s where I like to have my friends help me, because I am very bad at casting.” Knowing your weaknesses is vital, because then you can find people who have strengths in those areas to assist you.

Additionally, most organizations are too complex for any one person to be an expert at all of the roles. Leaders show hubris when they assume they can do the jobs of everyone else well. Renoir explains this notion of knowing your role as a leader: “Too many directors work like this. They tell the actor, ‘Sit down, my dear friends, and look at me. I am going to act a scene, and you are going to repeat what I just did.’ He acts a scene and he acts it badly, because if he is a director instead of an actor, it’s probably because he’s a bad actor.”

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Although leadership can be all encompassing, we shouldn’t be intimidated by the ideal list of qualities and behaviors a good leader displays. Focus on how you can improve. Set goals. Reflect on your failures, and recognize your success.

“You know, there is an old slogan, very popular in our occidental civilization: you must look to an end higher than normal, and that way you will achieve something. Your aim must be very, very high. Myself, I am absolutely convinced that it is mere stupidity. The aim must be easy to reach, and by reaching it, you achieve more.”

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The Observer Effect: Seeing Is Changing

Reading Time: 5 minutes

The act of looking at something changes it – an effect that holds true for people, animals, even atoms. Here’s how the observer effect distorts our world and how we can get a more accurate picture.

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We often forget to factor in the distortion of observation when we evaluate someone’s behavior. We see what they are doing as representative of their whole life. But the truth is, we all change how we act when we expect to be seen. Are you ever on your best behavior when you’re alone in your house? To get better at understanding other people, we need to consider the observer effect: observing things changes them, and some phenomena only exist when observed.

The observer effect is not universal. The moon continues to orbit whether we have a telescope pointed at it or not. But both things and people can change under observation. So, before you judge someone’s behavior, it’s worth asking if they are changing because you are looking at them, or if their behavior is natural. People are invariably affected by observation. Being watched makes us act differently.

“I believe in evidence. I believe in observation, measurement, and reasoning, confirmed by independent observers.”

— Isaac Asimov

The observer effect in science

The observer effect pops up in many scientific fields.

In physics, Erwin Schrödinger’s famous cat highlights the power of observation. In his best-known thought experiment, Schrödinger asked us to imagine a cat placed in a box with a radioactive atom that might or might not kill it in an hour. Until the box opens, the cat exists in a state of superposition (when half of two states each occur at the same time)—that is, the cat is both alive and dead. Only by observing it does the cat shift permanently to one of the two states. The observation removes the cat from a state of superposition and commits it to just one.

(Although Schrodinger meant this as a counter-argument to Einstein’s proposition of superposition of quantum states – he wanted to demonstrate the absurdity of the proposition – it has caught on in popular culture as a thought experiment of the observer effect.)

In biology, when researchers want to observe animals in their natural habitat, it is paramount that they find a way to do so without disturbing those animals. Otherwise, the behavior they see is unlikely to be natural, because most animals (including humans) change their behavior when they are being observed. For instance, Dr. Cristian Damsa and his colleagues concluded in their paper “Heisenberg in the ER” that being observed makes psychiatric patients a third less likely to require sedation. Doctors and nurses wash their hands more when they know their hygiene is being tracked. And other studies have shown that zoo animals only exhibit certain behaviors in the presence of visitors, such as being hypervigilant of their presence and repeatedly looking at them.

In general, we change our behavior when we expect to be seen. Philosopher Jeremy Bentham knew this when he designed the panopticon prison in the eighteenth century, building upon an idea by his brother Samuel. The prison was constructed so that its cells circled a central watchtower so inmates could never tell if they were being watched or not. Bentham expected this would lead to better behavior, without the need for many staff. It never caught on as an actual design for prisons, but the modern prevalence of CCTV is often compared to the Panopticon. We never know when we’re being watched, so we act as if it’s all the time.

The observer effect, however, is twofold. Observing changes what occurs, but observing also changes our perceptions of what occurs. Let’s take a look at that next.

“How much does one imagine, how much observe? One can no more separate those functions than divide light from air, or wetness from water.”

— Elspeth Huxley

Observer bias

The effects of observation get more complex when we consider how each of us filters what we see through our own biases, assumptions, preconceptions, and other distortions. There’s a reason, after all, why double-blinding (ensuring both tester and subject does not receive any information that may influence their behavior) is the gold-standard in research involving living things. Observer bias occurs when we alter what we see, either by only noticing what we expect or by behaving in ways that have influence on what occurs. Without intending to do so, researchers may encourage certain results, leading to changes in ultimate outcomes.

A researcher falling prey to the observer bias is more likely to make erroneous interpretations, leading to inaccurate results. For instance, in a trial for an anti-anxiety drug where researchers know which subjects receive a placebo and which receive actual drugs, they may report that the latter group seems calmer because that’s what they expect.

The truth is, we often see what we expect to see. Our biases lead us to factor in irrelevant information when evaluating the actions of others. We also bring our past into the present and let that color our perceptions as well—so, for example, if someone has really hurt you before, you are less likely to see anything good in what they do.

The actor-observer bias

Another factor in the observer effect, and one we all fall victim to, is our tendency to attribute the behavior of others to innate personality traits. Yet we tend to attribute our own behavior to external circumstances. This is known as the actor-observer bias.

For example, a student who gets a poor grade on a test claims they were tired that day or the wording on the test was unclear. Conversely, when that same student observes a peer who performed badly on a test on which they performed well, the student judges their peer as incompetent or ill-prepared. If someone is late to a meeting with a friend, they rush in apologizing for the bad traffic. But if the friend is late, they label them as inconsiderate. When we see a friend having an awesome time in a social media post, we assume their life is fun all of the time. When we post about ourselves having an awesome time, we see it as an anomaly in an otherwise non-awesome life.

We have different levels of knowledge about ourselves and others. Because observation focuses on what is displayed, not what preceded or motivated it, we see the full context for our own behavior but only the final outcome for other people. We need to take the time to learn the context of other’s lives before we pass judgment on their actions.

Conclusion

We can use the observer effect to our benefit. If we want to change a behavior, finding some way to ensure someone else observes it can be effective. For instance, going to the gym with a friend means they know if we don’t go, making it more likely that we stick with it. Tweeting about our progress on a project can help keep us accountable. Even installing software on our laptop that tracks how often we check social media can reduce our usage.

But if we want to get an accurate view of reality, it is important we consider how observing it may distort the results. The value of knowing about the observer effect in everyday life is that it can help us factor in the difference that observation makes. If we want to gain an accurate picture of the world, it pays to consider how we take that picture. For instance, you cannot assume that an employee’s behavior in a meeting translates to their work, or that the way your kids act at home is the same as in the playground. We all act differently when we know we are being watched.

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