With a perverse incentive, a company incents its employees to behave in ways that are contrary to the company’s interests. The company, in other words, pays employees to do things that reward the employee but prevent the company from reaching its stated goals. (See here and here for more detail).
Why would a company do that? Sometimes the stated goals of the company are not its actual goals. For instance, the company may say that it aims to increase customer satisfaction. That’s nice wind dressing but the real goal may be to “make the numbers”. So, the company may incent its sale force to act in ways that make the numbers even if such behavior also reduces customer satisfaction. In this example, studying the perverse incentive can help us understand what the company’s real goals are. This seemed to be the case at Wells Fargo, for instance.
In other cases, one business process conflicts with another. Perhaps each process is perfectly fine when running in isolation. When they run in tandem, however, they create perverse incentives. A good example comes from Signet Jewelers, the owner of several retail jewelry chains, including Jared’s, Kay Jewelers, and Zales. (I discovered this case in the business pages of the New York Times. Click here for the original article.)
The Signet situation involves two different business processes: sales and financial credit. By combining the two, Signet created a perverse incentive. Each business process works fine in and of itself. It’s the combination that spawns confusion. Here are the two processes:
Now let’s change the scenario. You’re now the manager of a retail jewelry store that also offers loans to its customers to enable them to buy more jewelry. Your compensation is based on how much jewelry you sell.
It sounds like a good idea. So, what’s wrong with this picture? To sell more jewelry, you have a strong incentive to give loans to non-credit-worthy individuals. You make the sale, but a relatively high proportion of the loans you make go bad and are not repaid. The company either writes off the loans or spends a lot of money with debt collectors trying to redeem them. The net result is often a negative: you sell more but also lose more.
The Signet example is just one of many. Once you’re familiar with the concept of perverse incentives, you can find them most everywhere, including the morning paper.
Loneliness is a growing problem in modern societies. Political polarization is also a noteworthy trend. Could the two phenomena be linked? Could loneliness, in fact, cause political polarization?
According to numerous sources, loneliness is a serious health problem – not just mental health but physical health as well. Several recent studies have documented the physical effects:
Additionally, the problem of loneliness is growing. The Harvard Business Review reports that, “Today, over 40% of adults in America report feeling lonely, and research suggests that the real number may well be higher.” The BBC reports that half of adults in England experience loneliness. And it’s not just old people. Julianne Holt-Lunstad’s study suggests, “ the prevalence of loneliness peaks in adolescents and young adults, then again in the oldest old.”
Vivek Murthy, the former U.S. Surgeon General, describes loneliness as an “epidemic”. The United Kingdom has now appointed a minister for loneliness. And Natalie Proulx asks in the New York Times, “Does Every Country Need A Loneliness Minister?”
Shifts in demographics and joining behavior have contributed to loneliness. People today are less likely to belong to church or fraternal organizations. Enrollment in trade unions has declined sharply. Men’s clubs have all but disappeared. Long-term employment with a single company has also declined. The gig economy is perhaps the ultimate expression of the atomized workplace.
If we can no longer find a sense of identity and belonging in traditional identity organizations, where do we turn to alleviate loneliness? Two trends suggest that we may have already moved in loneliness-alleviating directions.
The first trend is that we have sorted ourselves into what might be called “identity neighborhoods.” (Neal Stephenson, in his novel Snow Crash, calls them “burbclaves”). As Bill Bishop pointed out in his 2008 book, The Big Sort, we now segregate ourselves by political identity as much as by class or ethnic identity. Liberals live here; conservatives live there. Our neighborhood can give us a sense of identity and belonging. It also insulates us from opinions that differ from our own. Bishop points out that, “The clustering of like-minded Americans is tearing us apart.” Bill Clinton adds, “Some of us are going to have to cross the street, folks.”
The second trend is the rise of identity politics. We’ve moved away from broad-based political parties and towards “…political positions based on the interests and perspectives of social groups with which people identify.”
Is identity politics driven by loneliness? Simon Kuper thinks so. Writing in the Financial Times, Kuper notes that people are increasingly joining political parties to signal, “…they belong to the same tribe …with a shared identity … and something to talk about. In other words they are doing something that is usually considered positive: they are forging a new kind of community.” If Kuper is right – and I’m inclined to think that he is – then loneliness is the root cause of identity politics.
Loneliness then is not simply an individual issue. It affects the way we organize ourselves in neighborhoods, communities, nations, and political parties. Many observers suggest that identity politics is bad for America – click here, here, and here for representative examples. If we want to stifle identity politics, we first need to work on the problem of loneliness. Perhaps we need a minister for loneliness, after all.
Are older people wiser? If so, why?
Some societies believe strongly that older people are wiser than younger people. Before a family or community makes a big decision in such societies, they would be sure to consult their elders. The elders’ advice might not be the final word but it’s highly influential. Further, elders always have say in important matters. Nobody would think of not including them.
Why would elders be wiser than others? One theory suggests that older people have simply forgotten more than younger people. They tend to forget the cripcrap details and remember the big picture. They don’t sweat the small stuff. They can see the North Star, focus on it, and guide us toward it without being distracted. (Click here for more).
For similar reasons, you can often give better advice to friends than you can give to yourself. When you consider your friend’s challenges and issues, you see the forest. When you consider your own challenges and issues, you not only see the trees, you actually get tangled up in the underbrush. For both sets of advisors – elders and friends – seeing the bigger picture leads to better advice. The way you solve a problem depends on the way you frame it.
According to this theory, it’s the loss of data that makes older people wiser. Is that all there is to it? Not according to Seth Stephens-Davidowitz, the widely acclaimed master of big data and aggregated Google searches. Stephens-Davidowitz has written extensively on the value of big data in illuminating how we behave and what we believe. He notes that companies and government agencies are increasingly trawling big data sets to spot patterns and predict – and perhaps nudge – human behaviors.
What does big data have to do with the wisdom of the aged? Well … as Stephens-Davidowitz points, what’s an older person but a walking, talking big data set? Our senior citizens have more experiences, data, information, stories, anecdotes, old wives’ tales, quotes, and fables than anybody else. And – perhaps because they’ve forgotten the cripcrap detail – they can actually retrieve the important stuff. They provide a deep and useful data repository with a friendly, intuitive interface.
As many of my readers know, my wife and I recently became grandparents. One of the pleasures of grandparenting is choosing the name you’d like to be known by. I had thought of asking our grandson to call me Big Daddy. But I think I’ve just come up with a better name. I think I’ll ask him to call me Big Data.
Seth Stephens-Davidowitz is probably best know for writing the book, Everybody Lies: Big Data, New Data, and What The Internet Can Tell Us About Who We Really Are. He’s also a regular contributor to the Op-Ed pages of the New York Times. I heard his idea about seniors-as-big-data on an episode of the podcast Hidden Brain. (Click here). I mentioned his work a few years ago in an article on baseball and brand loyalty. (Click here). He’s well worth a read.
It’s hard to think critically when you don’t know what you’re missing. As we think about improving our thinking, we need to account for two things that are so subtle that we don’t fully recognize them:
Because of assumptions and filters, we often talk past each other. The world is a confusing place and becomes even more confusing when our perception of what’s “out there” is unique. How can we overcome these effects? We need to consider two sets of questions:
The more we study assumptions and filters, the more attuned we become to their prevalence. When we make a decision, we’ll remember to inquire abut ourselves before we inquire about the world around us. That will lead us to better decisions.
In my critical thinking class, we begin by studying 17 cognitive biases that are drawn from Peter Facione’s excellent textbook, Think Critically. (I’ve also summarized these here, here, here, and here). I like the way Facione organizes and describes the major biases. His work is very teachable. And 17 is a manageable number of biases to teach and discuss.
While the 17 biases provide a good introduction to the topic, there are more biases that we need to be aware of. For instance, there’s the survivorship bias. Then there’s swimmer’s body fallacy. And the Ikea effect. And the self-herding bias. And don’t forget the fallacy fallacy. How many biases are there in total? Well, it depends on who’s counting and how many hairs we’d like to split. One author says there are 25. Another suggests that there are 53. Whatever the precise number, there are enough cognitive biases that leading consulting firms like McKinsey now have “debiasing” practices to help their clients make better decisions.
The ultimate list of cognitive biases probably comes from Wikipedia, which identifies 104 biases. (Click here and here). Frankly, I think Wikipedia is splitting hairs. But I do like the way Wikipedia organizes the various biases into four major categories. The categorization helps us think about how biases arise and, therefore, how we might overcome them. The four categories are:
1) Biases that arise from too much information – examples include: We notice things already primed in memory. We notice (and remember) vivid or bizarre events. We notice (and attend to) details that confirm our beliefs.
2) Not enough meaning – examples include: We fill in blanks from stereotypes and prior experience. We conclude that things that we’re familiar with are better in some regard than things we’re not familiar with. We calculate risk based on what we remember (and we remember vivid or bizarre events).
3) How we remember – examples include: We reduce events (and memories of events) to the key elements. We edit memories after the fact. We conflate memories that happened at similar times even though in different places or that happened in the same place even though at different times, … or with the same people, etc.
4) The need to act fast – examples include: We favor simple options with more complete information over more complex options with less complete information. Inertia – if we’ve started something, we continue to pursue it rather than changing to a different option.
It’s hard to keep 17 things in mind, much less 104. But we can keep four things in mind. I find that these four categories are useful because, as I make decisions, I can ask myself simple questions, like: “Hmmm, am I suffering from too much information or not enough meaning?” I can remember these categories and carry them with me. The result is often a better decision.