Emotions impact decision making. Normative decision making models struggle to internalize this impact. Resentment, grievance, and bitterness are similar to each other, but different enough for me to include them all. They are low grade long term emotions that seem dysfunctional for those who carry them, but maybe functional at some level to encourage us to treat others better.
According to Wikipedia resentment is a complex, multilayered emotion that has been described as a mixture of disappointment, disgust, anger, and fear. You can feel resentment directly toward a person or group for how they mistreated you or you can feel resentment toward a person or group because they have been treated better by others or seem better in some way than you. Grievance somehow seems more specific, while bitterness seems more general and resigned. Resentment is anger’s passive aggressive brother.
In 2022, resentment seems to be driving all sorts of decision making all over the world. Vladimir Putin declares war on Ukraine. Millions of people suddenly do not want to get vaccinated and millions of others believe an election was stolen without evidence.
I finally read Antifragile by Nassim Taleb that was published in 2012. It has much interesting stuff, but the overall concept does not sell. The premise is that the opposite of fragile is not robust, but antifragile. I was not convinced that anything is antifragile except chaos. That hunter gatherer society was probably the most antifragile possible. Of course, Taleb would say it is Beirut. However, as usual, he makes some great points including how smart and well read he is (See post Dancing with Chance for more on Taleb). I think the antifragile idea was just to give him a big concept for the book. He says rock stars and restaurants are antifragile. Huh?
Fukishima was not a black swan. A bunch of risk calculations were cobbled together and the standard deviations turned out to be off on several dimensions. All of a sudden 1 in 1,000,000 became 1 in 30 and it happened. This leads to the so called barbell strategy for investing. You place the vast majority of assets in the safest possible places and then take much risk with just a few assets. Taleb believes that the assets of seeming moderate risk tend to be those where risk can be miscalculated the most. I agree with him. It reminds me of my favorite financial planner who tells me that a particular asset mix has a 95% chance of proving adequate to get my wife and I through our lives comfortably. If the 5% chance happens, I need a story to tell my wife or a letter to the file. I need to do better than that even if that means changing how we define comfortable.
He says that you need the people making predictions to have skin in the game, but he also points out that smart people make bets where the downside is small and the upside is huge. Is that skin in the game?
I strongly agree with him that detailed forecasts of the future are a sad joke, but that does not mean to me that we should not do our best to plan for the future. I believe that we can avoid many of the worst alternative futures.
He suggests that David Ricardo’s law of comparative advantage is crazy because governments actually try to implement it and then bad things happen. So if Portugal puts all its production in wine and none in cloth and demand for wine goes down, that is a problem. It seems to me that he misses the point that trade is advantageous overall and that everyone has something to contribute. Finally he does admit that Ricardo is right.
Taleb and I agree that the relationship of the USA and Saudia Arabia makes no sense and that has only gotten more true since the book was published. Stability and reducing uncertainty are nice goals, but if you have to sacrifice everything else to achieve them, you will ultimately experience major instability and uncertainty. You need the correct level of stability. When things appear very stable and there seems to be no uncertainty, you can bet that something bad is being papered over.
Taleb also agrees that humans are not that good with nonlinearity (See post Nonlinear). Nonlinearity is either convexity or concavity (or sometimes both). In such situations small changes in one input can result in very large changes in harm or benefit. You do not want to operate in nonlinear ranges where harm can increase geometrically. That goes for building bridges or stockpiling N95 masks.
I agree that modern does not equate with good for humans. The navigators in our navy need to be able figure out where they are with charts and a sextant and not just GPS. Human society would be more robust if we all knew how to cook a little and garden a little and sew a little.
The book really makes one point well. If you want to survive you need backup/redundancy. A longer view has its downsides but if survival is a goal you need to be saving for the future. That is not sufficient for survival since a black swan can get you regardless, but it is necessary. That goes for the Texas power grid or pandemic response or your family finances.
Taleb’s maxim for the book is:
Everthing gains or loses from volatility. Fragility is what loses from volatility and uncertainty.
The glass on the table is short volatility. In Taleb’s option trader lingo, this means that the glass on the table will survive best if no one even comes into that room. As Taleb notes, time is volatility. Over time things will happen in that room and they could all be bad for that glass.
The glass is dead; living things are long volatility. Taleb states:
The best way to verify that you are alive is by checking if you like variations. Remember that food would not have a taste if it were not for hunger; results are meaningless without effort, joy without sadness, convictions without uncertainty, and an ethical life is not so when stripped of personal risks.
This post is sooo… derivative, but I cannot help myself. Good judgment is dependent on good information. It has never been so obvious how much we rely on good referees to determine what is good information. Most persuasion is based on filtering the information to the persuader’s advantage, but it has been rare in my lifetime to use the strategy of just hammering the lie.
It is easy to imagine that our paleo brains were rewarded by believing the chief. We both had skin in the game. So our still tribal brains believe things that are repeated over and over, even lies. Unfortunately, our information sources have gotten further and further from us so that our futures are not intertwined, except in an existential way. Our information networks have expanded and more critically selectively expanded.
Emre Soyer and Robin Hogarth have written a new book, The Myth of Experience. Why We Learn the Wrong Lessons, and Ways to Correct Them. This book is aimed at a general audience although it has copious and detailed notes and an index that will allow for deeper looks. I have much respect for their past work both individually and together.
The key idea that they have developed elsewhere is that some learning environments are kind so that what you learn by experience is helpful–say riding a bike– while other environments are wicked and experience cannot be relied upon to make good decisions. Robin Hogarth’s Educating Intuition develops this (See posts: What has Brunswik’s Lens Model Taught? ‘ , Kind and Wicked Learning Environments)
Nick Chater is the author of The Mind is Flat–the Remarkable Shallowness of the Improvising Brain, Yale University Press, New Haven, 2019. He is a professor of behavioral science at the Warwick Business School. The book is two parts and overall it is as ambitious as it is simple. The first part is the most convincing. He shows how misguided we are on our perceptions, emotions, and decision making. Our vision seems to provide us with a full fledged model of our environment, when we really only can focus on a very small area with our furtive eye movements providing the impression of a complete detailed picture. Our emotions do not well up from deep inside, but are the results of in-the-moment interpretations based on the situation we are in, and highly ambiguous evidence from our own bodily state. Chater sees our beliefs, desires, and hopes as just as much inventions as our favorite fictional characters. Introspection does not work, because there is nothing to look at. We are imaginative creatures with minds that pretty much do everything on the fly. We improvise so our decision making is inconsistent as are our preferences.
This post is based on the book, Elastic–Flexible Thinking in a Time of Change by Leonard Mlodinow, Pantheon Books, New York, 2018. Mlodinow is a physicist and worked with Stephen Hawking. His previous book Subliminal evidently gave him considerable access to interesting people like Seth MacFarlane. He mentions that Stephen Hawking’s pace of communicating was at best six words a minute with public presentations being done ahead of time. Mlodinow notes that this slowing of the pace of a conversation is actually quite helpful in forcing you to consider the words as opposed to thinking of what you are going to say while the other person is talking so that you can have an instant response.
Every couple of years, I seem to go back and look at “decision making” books that have arrived in my local library. I clearly take a broad view of decision making. This time I came up with Farsighted, Elastic, and the Mind is Flat. The first two books were definitely written to be popular books with the third less so. They share quite a bit. They all rely quite a bit on illustrations or questionnaires that show the peculiarities and shortcomings of our minds. They all rely on literature to explain their cases on how our minds work. Farsighted uses George Eliot and Middlemarch. Elastic uses Jonathan Franzen and mentions his book Corrections. The Mind is Flat uses Leo Tolstoy and Anna Karenina.
This post is inspired by the book: Rebooting AI – Building Artificial Intelligence We Can Trust, written by Gary Marcus and Ernest Davis, New York, 2019. Gary Marcus (see post Kluge) is a well known author and artificial intelligence entrepreneur and Ernest Davis is a professor of computer science at Carnegie Mellon. To oversimplify, the authors emphasize that the successes of AI are narrow and tend to be greedy, opaque, and brittle. They provide history of AI seemingly about being ready for prime time decade after decade after decade. Self driving cars are almost there, but they are not. Human frailties in driving result in a death about every 100,000,000 miles driven, but Marcus and Davis indicate that self driving cars require human intervention every 10,000 miles which is 10,000 times in 100,000,000 miles. It may be a very long time before we are ready to sign off on self-driving cars, because the progress thus far has been the easy part.