Alabama and Clemson have met each year for the past four years in the college football playoffs. Alabama has won two games; Clemson has won two. The aggregate score of the four games: Clemson 121 — Alabama 120. If Alabama hadn’t missed an extra point in last night’s game, the aggregate score would be tied. The two teams are so close that they might as well be one. Let’s call them Clembama.
Meanwhile, no other team has come close. The great teams of years past – Notre Dame, Oklahoma, Georgia, Southern Cal, Nebraska, and Texas – have all fallen by the wayside. When they match up against Clemson or Alabama, they don’t lose by inches. They lose by yards.
What’s it all mean? Simply that skill is unevenly distributed in college football. As Michael Mauboussin points out, when skill is evenly distributed, luck plays a greater role in the outcome of any competitive event, including sports and business competition. When skill is unevenly distributed, luck’s role is greatly diminished.
It seems counter-intuitive that luck should be more important in some situations than in others. Isn’t luck more or less random? Shouldn’t it apply equally in all situations? It’s true that luck is essentially random but when everything else is even, even a little bit of luck can make a huge difference. A funny bounce, an odd hop, a slippery field can determine who wins and who loses.
To see the difference, just look at the NFL, where skill is more evenly distributed. More specifically, look at Sunday’s game between the Chicago Bears and the Philadelphia Eagles. The Eagles were ahead by one point when the Bears maneuvered into position to kick a field goal near the end of the game. Make the field goal and the Bears win. Miss it and the Eagles win. The Bears kicked, the ball hit an upright, bounced downward, hit the crossbar, and then bounced back into the field of play. A bouncing football is a pretty random thing. If the ball had bounced off the crossbar and through, the Bears would have won. As it was, the Eagles won. In truth, luck – not skill –determined the outcome.
If Oklahoma, say, had made the same kick the last time they played Alabama, it would not have made a whit of difference. The game wasn’t close. The skill levels weren’t close. Luck didn’t matter.
Mauboussin’s paradox of skill states that: “In activities that involve some luck, the improvement of skill makes luck more important…” The paradox makes me feel somewhat humble. My business career was in the highly competitive computing industry, where skill is very widely distributed. As I look back on both my successes and my failures, I wonder how many were caused by skill (or lack of it) and how many were caused by luck. When I won, maybe it was because I was more skilled. Or maybe I just got lucky.
I first wrote about Clembama two years ago. Click here to find that article, which includes several links to Michael Mauboussin’s work.
If I ask you about the crime rate in your neighborhood, you probably won’t have a clear and precise answer. Instead, you’ll make a guess. What’s the guess based on? Mainly on your memory:
Our estimates, then, are not based on reality but on memory, which of course is often faulty. This is the availability bias. Our probability estimates are biased toward what is readily available to memory.
The broader concept is processing fluency– the ease with which information is processed. In general, people are more likely to judge a statement to be true if it’s easy to process. This is the illusory truth effect– we judge truth based on ease-of-processing rather than objective reality.
It follows that we can manipulate judgment by manipulating processing fluency. Highly fluent information (low cognitive cost) is more likely to be judged true.
We can manipulate processing fluency simply by changing fonts. Information presented in easy-to-read fonts is more likely to be judged true than is information presented in more challenging fonts. (We might surmise that the new Sans Forgetica font has an important effect on processing fluency).
We can also manipulate processing fluency by repeating information. If we’ve seen or heard the information before, it’s easier to process and more likely to be judged true. This is especially the case when we have no prior knowledge about the information.
But what if we do have prior knowledge? Will we search our memory banks to find it? Or will we evaluate truthfulness based on processing fluency? Does knowledge trump fluency or does fluency trump knowledge?
Knowledge-trumps-fluency is known as the Knowledge-Conditional Model. The opposite is the Fluency-Conditional Model. Until recently, many researchers assumed that people would default to the Knowledge-Conditional Model. If we knew something about the information presented, we would retrieve that knowledge and use it to judge the information’s truthfulness. We wouldn’t judge truthfulness based on fluency unless we had no prior knowledge about the information.
A 2015 study by Lisa Fazio et. al. starts to flip this assumption on its head. The article’s title summarizes the finding: “Knowledge Does Not Protect Against Illusory Truth”. The authors write that, “An abundance of empirical work demonstrates that fluency affects judgments of new information, but how does fluency influence the evaluation of information already stored in memory?”
The findings – based on two experiments with 40 students from Duke University – suggest that fluency trumps knowledge. Quoting from the study:
“Reading a statement like ‘A sari is the name of the short pleated skirt worn by Scots’ increased participants later belief that it was true, even if they could correctly answer the question, ‘What is the name of the short pleated skirt worn by Scots?’” (Emphasis added).
The researchers found similar examples of knowledge neglect– “the failure to appropriately apply stored knowledge” — throughout the study. In other words, just because we know something doesn’t mean that we use our knowledge effectively.
Note that knowledge neglect is similar to the many other cognitive biases that influence our judgment. It’s easy (“cognitively inexpensive”) and often leads us to the correct answer. Just like other biases, however, it can also lead us astray. When it does, we are predictably irrational.
This fall, in addition to my regular academic courses, I’ll teach three one-day seminars designed for managers and executives.
These seminars draw on my academic courses and are repackaged for professionals who want to think more clearly and persuade more effectively. They also provide continuing education credits under the auspices of the University of Denver’s Center for Professional Development.
If you’re guiding your organization into an uncertain future, you’ll find them helpful. Here are the dates and titles along with links to the registration pages.
I hope to see you in one or more of these seminars. If you’re not in the Denver area, I can also take these on the road. Just let me know of your interest.
The 1989 Tour de France was decided in the last stage, a 15.2 mile time trial into Paris. The leader, Laurent Fignon, held a fifty second advantage over Greg LeMond. Both riders were strong time trialers. To make up fifty seconds in such a short race seemed impossible. Most observers assumed that Fignon would hold his lead and win the overall title.
In most time trials, coaches radio the riders to inform them of their speed, splits, and competitive position. In this final time trial, however, LeMond turned off his radio. He didn’t want to know. He feared that, if he knew too much, he might ease up. Instead, he raced flat out for the entire distance, averaging 33.9 miles per hour, a record at the time. In a stunning finish, LeMond gained 58 seconds on Fignon and won the race by a scant eight seconds. (Here’s a terrific video recap of the final stage).
LeMond’s strategy is today known as information avoidance. He chose not to accept information that he knew was freely available to him. LeMond knew that he might be distracted by the information. He chose instead to focus solely on his own performance – the only variable that he could control.
While information avoidance worked for LeMond, the strategy often yields suboptimal outcomes. We choose not to know something and the not knowing creates health hazards, financial obstacles, and a series of unfortunate events. Here are some examples.
In some ways, information avoidance is the flip side of the confirmation bias. We accept information that confirms our beliefs and avoid information that doesn’t. But there seems to be more to avoidance than simply the desire to avoid disconfirming information. Other contributors include:
Information avoidance can also teach us about persuasion. If we want to persuade people to change their opinion about something, making it scarier is probably self-defeating. People will be more likely to avoid the information rather than seeking it out. Similarly, bombarding people with more and more information is likely to be counter-productive. People under bombardment become defensive rather than open-minded.
As Aristotle noted, persuasion consists of three facets: 1) ethos (credibility); 2) pathos (emotional connection); 3) logos (logic and information). Today, we often seek to persuade with logos – information and logic. But Aristotle taught that logos is the least persuasive facet. We typically use logos to justify a decision rather than to make a decision. Ethos and pathos are much more influential in making the decision. The recent research on information avoidance suggests that we’ll persuade more people with ethos and pathos than we ever will with logos. Aristotle was right.
Greg LeMond’s example shows that information avoidance can provide important benefits. But, as we develop our communication strategies, let’s keep the downsides in mind. We need to package our arguments in ways that will reduce information avoidance and lead to a healthier exchange of ideas.
I first wrote about Bitcoin on this website five years ago today. (Click here). I decided not to buy any at the time because the price had surged to well over one hundred dollars! Clearly it was a bubble. If only I had known that the price would peak at $18,000 a few years later. (Today, the price is about $6,800).
So what’s happened over the past five years? Let’s look at Bitcoin’s benefits and then investigate some of the ways that it has changed our world.
Bitcoin is based on a blockchain stored in multiple locations. This gives it two major advantages: it can’t be erased and can’t be tampered with. Simply put, it’s like writing checks in ink rather than in pencil, using paper that can’t be destroyed. A blockchain can record transactions and ensure that they will always be available as a matter of public record. Bitcoin uses this feature to buy and sell things. Each transaction is recorded forever, meaning that you can’t spend the same Bitcoin more than once.
Bitcoins can also reduce inflation because they can’t be printed at a government’s whim. Instead, they’re “mined” through complex mathematical calculations. The process gradually grows the supply of coins. The money supply grows in predictable ways. This appeals to anyone who worries that governments will artificially inflate their national currencies.
Bitcoin is also anonymous – just like cash. Unlike cash, however, it’s not physical. It can easily be moved around the world as electronic blips. That makes transactions convenient and inexpensive and could conceivably cut out banks as middlemen. This makes Bitcoin attractive to many groups, especially criminals.
So, what’s happened? First, the idea of the blockchain has spread. There’s no reason to limit the blockchain to currency transactions. We can store anything in blockchain and ensure that it never disappears. In other words, we believe that it is more trustworthy than government or financial entities.
As Tim Wu writes, we are undergoing, “… a monumental transfer of social trust: away from human institutions backed by governments and to systems reliant on well-tested computer code.” Wu notes that we already trust computers to fly airplanes, assist in surgery, and guide us to our destination. Why not financial systems as well? A well-organized cryptocurrency could become the de facto standard global currency and eliminate the need for many banking services.
But we don’t need to limit the blockchain to financial transactions. Any record that must be inviolate can potentially benefit from blockchain technology. Some examples:
Of course, we can also use blockchains for less noble pursuits. The blockchain can store any information, including pornography. That’s a problem but it’s the same problem that was faced by myriad new technologies, including VCRs and the Internet itself. Criminals can also use cryptocurrencies for ransomware attacks, and to traffic in contraband or avoid taxes. We can ameliorate these problems but we probably can’t eliminate them. Still, the advantages of the technology seem much greater than the disadvantages.
So … what happens over the next five years? The New York Times reports that venture capitalists poured more than half a billion dollars into blockchain projects in the first three months of this year. So, I expect we’ll see a shakeout at the platform level over the next five years. Today, there are many ways to implement blockchain. It reminds me of the personal computing market in, say, 1985 – too many vendors selling too many technologies through too many channels. I expect the market will consolidate around two or perhaps three major platforms. Who will win? Perhaps IBM. Perhaps R3. Perhaps Ethereum. Perhaps Multichain. Rather than buying Bitcoin, I’d suggest that you study the platforms and place your bets accordingly.
In the meantime, we need to ask ourselves a simple question: Are we really willing to forego our trust in traditional institutions and put it all into computer code?