Tag Archives: Daniel Kahneman

Why do bad things happen to good companies?

Last week, I attended a Finance conference were one of the speakers (a coach and keynote speaker by the name Martin Carper) delivered a talk titled “Why do bad things happen to good companies?” (1).

Martin opened the speech with the fall of the Medici bank collapse at the end of the XV century, followed by the more recent sound cases of  Enron scandal (fraud accounting), the BP oil spill (in the Gulf of Mexico), Volkswagen emissions scandal (rigged tests on diesel cars). Why all those companies which seemed so good found themselves immersed in such crises. Were they so good? Those companies were filled up with outstanding individuals, following well thought, proven processes, yet they found themselves caught in fire. As it turns out, those companies were not so good after the fact. Investigations revealed major frauds, wrong incentives schemes, bad attitudes.

The reason according to Martin: the key to keep being good is about mindset.

He proposed the audience a couple of quick exercises:

  • triangles“Rate yourself as driver in relation to the rest of the group”. Studies show that 80% of the individuals to whom this question is asked, rate themselves above average. The key: Illusionary superiority.
  • How many triangles do you see here?” “Does anyone see more than 4, 6… 8 triangles?

I was one of those in the audience seeing plenty of triangles. One new triangle after each couple of seconds. But there are none. “A triangle is a polygon with three edges and three vertices“. There are no three edges in any of those figures you may think you see.

This trick helped him to introduce what is commonly known as System 1 thinking, the kind of short-term memory, quick way of thinking, as opposed to System 2 thinking; the more rational way, responsible of the complex thought process used to solve difficult problems. The difference between multiplying mentally 3×3 or 17×23. The difference between driving home or finding your route in an unknown place with the only help of a chart (without a GPS navigator). This terminology of System 1 and 2 was introduced in the book “Thinking, fast and slow” by the 2002 Economics Nobel prize laureate Daniel Kahneman (1).

The speaker then recommended to pause, and, in order to have the correct mindset to avoid those bad things from happening, he invited us to adopt what he called the 3 Ps:

  • Pace. He stressed the need to combine the different ways of thinking, systems 1 and 2, with their respective speeds. Not to be driven always by automatic processes into a purely system 1 way of thinking. He used the classical adage “Festina lente“, meaning “More haste, less speed”.
  • Position. He called for taking a step back to see the overall picture before taking action. To analyze the situation, see all possible options before chosing one. He showed the difference in the layout of a captain’s deck vs. an admiral one in a major British navy ship.
  • Perspective. Here he mentioned an anecdote from Jan Carlzon, the CEO of the SAS airline during the 80s and beginning of the 90s, and credited with the transformation of the company. To stress that small things mattered, Jan would check on and insist that coffee stains be cleaned in the lavatories, as it served as an indicator to the everyone (including the customers) of how seriously SAS took all maintenance procedures. Otherwise, if a coffee stain had slipped through the processes, what other faults could have done so as well.

(1) His speech shares almost squarely the title with but has no relation to the Harvard Business School case study published in the 90s by the authors Benson P. Shapiro, Richard S. Tedlow and Adrian J. Slywotzky, in which they introduced the concept of value migration.

(2) This a fabulous book, published in 2011, on the mental process and the biases of our mind, which references plenty of psychology studies made by different researches along decades. I read it back in 2013 and I strongly recommend it.

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Forecasting France Euro 2016

I have a work colleague who not only is a tremendous negotiator and aircraft seller but also has a great sense of humor and manages in his free time late in the night to set up a contest for office staff to try to guess winners, matches’ scores, top scorers, etc., of major international soccer competitions. The France Euro 2016 which starts this afternoon could not be missed. Nacho managed to set up the contest in time.

In this post I am going to explain how I went about forecasting the results of the UEFA Euro 2016.

“when in doubt, build a model”, Nate Silver.

The readers of this blog may already know how much I do like to build models to produce forecasts, guesstimates, etc. In relation to forecasting this UEFA Euro 2016 there is some background that has shaped my mind in relation to the subject in the recent years, let me give you some hints:

Having shared this background, you may understand that I tried to remove all the beauty of guessing and my football knowledge out of the forecasting process (1).

I rather made use of:

  • ESPN Soccer Power Index (SPI) ranking, introduced by the economist Nate Silver. I used its offensive and defensive scores plus weight for each of the scores based on a tip indicating that in competitive matches the defensive factor tends to be slightly more important (see “A Guide to ESPN’s SPI rankings”) (2).
  • The frequency of different scores in the group phases of the Euro 2012 and the World Cup 2010, the in the round of 16, quarter finals and semi-finals.

Frenquency

  • A few simple rules about how to allocate results given the difference between SPI ratings of the two nations playing each match. (3)
  • The total number of goals during group phases the latest Euro and World Cup. In order to cross check that the total numbers of goals that my forecast yielded was in check with previous competitions.

It may sound very complex. It is not. It requires a bit of reading (which most of it I did years ago), retrieving the latest ratings, giving it a bit of thought to set up the model and then, not even looking at the names of the teams, you go about allocating the scores based on raw figures. Let’s see how my forecast fares this time! (4)

Porra Euro 2016

Les grandes personnes aiment les chiffres” (5), the Little Prince.

(1) In fact I have not watched a single national team football match from any country since the World Cup in Brazil in 2014.

(2) See here the blog post I published yesterday in which I made a more thorough review of the ESPN SPI index.

(3) I set up rules like “if the difference of the combination of indices of the two nations is below this threshold, I take it as a draw, if it is between x and y as victory by 1 goal, if higher…”, etc.

(4) This way of forecasting allowed me to finish 4th out of 47 in 2010, 15th out of 87 in 2014. As it removes biases it allows to be better than the average, though it prevents you of guessing outliers, gut feelings, etc.

(5)”Adults love figures”.

Note: In the blog post from yesterday I mentioned that the latest complete ranking from the ESPN SPI index that I could retrieve dated from October 2015. That is the one I have used, therefore, Germany results as winner. Of the latest ranking, covering the Top 25 nations, only 13 countries of the 24 competing at the Euro 2016 are included. I could have set up an hybrid ranking taking the latest rankings and ratings for the top 13 from June and using the October figures for the lower 11 teams. I decided to go on with a single set of data. If I had done so, the maing changes would have come from the semifinals onwards. France would have appeared as winner instead of Germany. We’ll see if that was a good decision.

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Forecasting 2014 FIFA World Cup Brazil

I have a work colleague who not only is a tremendous negotiator and contracts’ drafter but also has a great sense of humor and manages in his free time late in the night to set up a contest for office staff to try to guess winners, matches’ scores, top scorers, etc., of major international soccer competitions. The 2014 FIFA World Cup in Brazil, which will start tomorrow, could not be missed. Nacho managed to set up the contest in time.

To set up the background as to how I have approached the game of forecasting this World Cup:

  • I had written a review of the book “Soccernomics“, which among other things advocates the use of data in order to make decisions in relation to football transfer market, forecasting, etc. This book relies somewhat heavily in “Moneyball” another book which I read some months ago with a similar scope but with baseball as the theme sport.
  • When the draw of the World Cup took place last December, I wrote a couple of blog posts discussing what was the so-called “group of death” basing the analysis on FIFA and ESPN rankings.
  • During the last year, I read a couple of books which approach how we make decisions and how to remove different kind of biases from the thought processes of making them: “Thinking Fast and Slow” (by the 2002 winner of the Nobel Prize in Economics Daniel Kahneman) and “Seeking Wisdom“.
  • Finally, last year I followed the open course “A Beginner’s Guide to Irrational Behavior” by Dan Ariely (though I missed the last exam due to my honeymoon and could not get credit for it).

Having shared this background, you may understand that I tried to remove all the beauty of guessing and my football “knowledge” to the forecasting process. I rather made use of  ESPN Soccer Power Index (SPI) ranking, introduced by the economist Nate Silver. I used its offensive and defensive scores plus the tip indicating that in competitive matches the defensive factor tends to be slightly more important (see “A Guide to ESPN’s SPI rankings”).

Once I plugged in the numbers from the index and used the referred tip on the defensive side, I built a simple model to guess each of the World Cup matches. Once you take this approach you will find that the model gives you plenty of results such as Nigeria 1.32 – 1.53 Bosnia… What to do with it? When the result was very tight I resolved it as a draw, otherwise a victory for the team with the highest score.

In very few instances I forecast that a team would score 3 or more goals in a match. I bore in mind that in the 2010 World Cup 80% of the matches ended up with scores of 1-0 (26% of the matches), 2-1 (15%), 0-0, 1-1 or 2-0 (each 13%).  That a team scores more than 3 goals in a match will certainly happen in some games, but I did not bother to guess in which ones, the odds are against.

The prize pot of the game organized by this colleague is not particularly big (few hundreds euros). The main point of the game is enjoying the chit-chat with work colleagues. My second main point is putting this rational approach to work and see how it fares.

Finally, what did I forecast?

A World Cup won by Brazil against Argentina in the final. With Spain beating Germany for the third place (in the penalties). For my English readers: England defeated by Colombia in the 1/8 of final. For the ones from USA, it doesn’t make the cut from the group phase. We will see along this month how well do I fare.

2014 FIFA World Cup Brazil forecast.

2014 FIFA World Cup Brazil forecast.

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The Titanic: a project management disaster

The Titanic (public domain image, taken from Wikimedia, by F. Stuart).

Few days ago, I attended a conference on an example of a project management disaster: the Titanic, the British ship which sank in its maiden trip from Southampton to New York in 1912 causing the death of above 1,500 passengers, above 2 thirds of those aboard.

[The conference was part of the same cycle of which I attended another one about 2 months ago about the success of the management project for the delivery of the infrastructure, design and construction of buildings, transport and the legacy of the London 2012 Olympic Games (I wrote about it here).]

Learning from a well-known disaster, as opposed to a success, made the audience more eager to listen to Ranjit Sidhu, a consultant who has made extensive research about the Titanic and has written the book “Titanic Lessons in Project Leadership“.

She was going to focus the conference on 3 sides of project management: communication, leadership and teamwork (1), and the problems which each of those originated in the disaster.

Titanic captain E. J. Smith (public domain image, taken from Wikimedia, author: New York Times).

Sidhu started giving an introduction of some of the characters involved in the project to show the kind of power plays and conflicts that took place at the time of taking decisions. Some of those characters were: Bruce Ismay (chairman of White Star Line), Lord Pirrie (chairman of the shipbuilding company Harland and Wolff), J.P. Morgan (American banker who financed the formation of International Mercantile Marine Company, mother company of White Star Line), Alexander Carlisle (chief engineer of the project in Harland and brother-in-law of Pirrie), Thomas Andrews (successor of Carlisle), Captain Smith (sea-captain of the Titanic).

From the beginning of the project the mantra that the Olympic line of boats was going to be unsinkable was created due to some features which indeed made the boats more secure than others at the time, as well as the largest and most luxurious. From that point onwards, several psychological flaws impeded perceptions to be re-evaluated, messages to get across, decisions to be questioned, etc.

For some of the characters (Carlisle and Andrews) safety was the top objective, to the point that when the number of life boats was decided to be reduced against the engineers’ criteria Carlisle resigned as chief engineer of the project and left Harland despite of being a relative of the chairman.

For other characters in the story the emphasis was in the size or the luxury: an ample dinning room, clean views from the cabins (not disturbed by life boats, for instance), etc.

The power play, the financial pressure on the project, the deadlines of both departure and arrival in New York, the image to keep before the press, etc., all made that several decisions were taken despite of compromising technical features (life boats reduction and placement), manufacturing operations (working in increasing shifts due to the delay caused by the repair of the Olympic at the same shipyard), operational decisions (such as short time for sea trial of the ship, radio operators priorities and incentives misalignment…), etc., adding to the diminished safety of the trip.

Some of the psychological flaws that were going on when taking those decisions include: anchoring effect (the image of the Titanic as unsinkable was fixed in the mindset despite of decisions compromising safety), bandwagon effect, confirmation bias (negative signals being filter out vs. acknowledging supporting evidences), conformity to the norm, framing effect, normalcy bias (denial and underestimation of the consequences of the disaster once occurred), etc.

Last minute misfortunes added up to the disaster: missing binoculars for the scouts (due to the departure of a crew component who held them), a shorter rope to perform ice tests, radio messages from the Californian boat not being prioritized by operators to be brought to the main deck…

The end to the story is well-known.

Have we progressed as a society since them?

Today we like to think that yes. More requirements regarding safety are put into projects. Regulations are passed to ensure safety. Risk management is used as part of project management to ensure that the kind of decisions taken at the time of the Titanic today they are taken without overlooking the risks behind them.

However, I would like to bring 3 questions raised by colleagues in the Q&A session that followed the presentation:

  • Of the cited characters, who could have been more proactive to prevent the disaster? Taking into account that Carlisle, the chief engineer, went to the point of resigning without (a seemingly) major effect to the fate of the ship.
  • How can we react to a pressure situation under a powerful sponsor? We can try to find allies, framing the situation as an “us” as a group instead of opposing the sponsor.
  • If the Titanic hadn’t sunk, would it be seen as an example of success in project management instead of a disaster? You may dismiss the point too quickly by thinking “oh, yes, but it happened that it sank!“.

Here, I remembered the theory of the safety in systems seen as layers of safety added one after the other. Each of the layer may have some holes in it just as a portion of cheese (typical image used in aerospace projects). By having several layers, accidents are prevented in most of the cases. However, from time to time the holes in the layers are perfectly aligned and the accident happens (lack of sea trials, radio messages not passed, urgency to reach New York, scouts without binoculars, improper ice tests, power vs. authority struggle in that precise trip in which the chairman of the company travels alongside the captain…).

Cheese model of safety layers in a system.

Cheese model of safety layers in a system.

My takeaways from the conference are:

  • to continuously remind ourselves of the flaws we have in our mental processes (I recommend a couple of books to that respect: “Thinking Fast and Slow” by Daniel Kahneman and “Poor Charlie’s Almanack“, by Charlie Munger),
  • to sharpen our perception of risks (both at work and daily life),
  • to understand that we are a layer (with our own holes) in the safety system (both at work and daily life).

(1) She did not enter much into risk management despite of acknowledging that it had not worked (or rather overlooked).

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There are no hot hands in basketball

I started reading the book “Thinking, Fast and Slow“, by the Nobel laureate Daniel Kahneman some months ago, and, even if I am slow progressing with it, I find it extremely interesting.

A recurring topic when reading about how our psychology deceives us is when thinking about probabilities. In this post I wanted to write about a paper he refers to in the book: “The Hot Hand in Basketball: On the Misperception of Random Sequences” [PDF, 1MB], by Thomas Gilovich of Cornell University (1985).

If you have actually made the exercise of coin-tossing several dozens times or have gone several to a casino and watched roulette results, you will believe without effort that seeing long streaks of a certain event (several heads for the coin, over a dozen reds or three 32 in a row for the roulette) is part of the randomness of those games. Basketball players and fans get it consistently wrong when believing in hot hands, and that is precisely what the paper from Gilovich is about and it is a wonderful reading.

It starts with a survey among basketball fans, who no doubt believe in hot hands being behind streaks: 91% believed a player has a better chance of hitting after having converted 2 or 3 throws. They even ventured into assigning probabilities. For a player with 50% in field throws they said the chances of :

  • hitting after a converted throw were 61%,
  • missing after a missed a shot, 42%.

Then he studied the performance of Philadelphia 76ers players (Julius Erving among them) during the season, carefully analyzing the chances of a each player hitting or missing a throw after having missed or hit the previous one, two or three consecutive throws. The results are clear, they do not support the existence of such “hot hands”, they are random. In fact, on average, the chances of hitting after a hit were always lower than the field score % of the team while chances of hitting after a miss were higher and higher than the ones of the supposedly hot hands.

He analyzed the numbers of runs (streaks, like the several heads or tails in a row for the case of a coin) and were not different that what could be expected randomly.

He went on to analyze whether the different players had more cold or hot nights than what can be expected by statistics… also discarded.

Of course, in field throws the author understood that there were many variables at play: for instance, if a player had hit 2 consecutive throws the defense might be harder on him… to eliminate those possible factors influencing results, he went to study free throws, in this case taking the figures from Boston Celtics (Larry Bird among them) and NY Knicks. Guess what? No hot hand in free throws either: there were even more players scoring after a miss than the other way around (but again, nothing statistically significant).

He went even further: he made a controlled experiment with college players in which they threw 100 shots from a distance in which their scoring success was 50% (different distance for each one). Throws were made without opposition but from different position each time. Players got paid according to the hits and could bet higher or lower money each time depending on whether they believed that they were having a hot hand… this, again, proved that there were no hot hands and what’s more: players did believe in those hot hands and were completely unreliable in predicting their next throw chance of success.

The paper has only 21 pages: I encourage anyone who likes psychology, statistics or basketball to read it, its wonderful.

I thought that to conclude this post with a funny note, I could link the following short video of Shane Battier’s “clear” hot hand in the first game of this year’s NBA finals:

Impressive, 3 consecutive 3pt-throws converted in the first quarter!

A difference between now and 1985, when the professor wrote his paper, is that now we don’t need to ask the team statistician about the figures, but NBA site records all of them. I went to check what happened to Shane and his hot hand in that match. After those 3 throws converted, he attempted other 3 in that match: he missed 2.

Still, he had a 66% on 3pt throws that night… what could be a hot night. I went to check his percentages during the season and career. During the finals he made a 0.577%, remarkable; during the whole of the play offs, 0.382% in 3pt. And guess what is his average career (13-years) percentage score for 3pt throws: the same 0.382% he showed in the play-offs. That streak you saw was nothing but the random streak expected from Shane.

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