Tag Archives: Hogarth

Antifragile

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.

 

People Say/ Everyone Knows/ They Say

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.

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The Myth of Experience

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)

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Coronavirus Math

Early 2020 is a truly Interesting time for decision making. We have learned what an inexact science risk communication is and had Robin Hogarth’s statement reemphasized:

“Human achievement is lower when there are nonlinearities in the ecology.” (What has Brunswik’s Lens Model Taught?).

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The Mind is Flat

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.

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Far sighted

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 MiddlemarchElastic uses Jonathan Franzen and mentions his book Corrections. The Mind is Flat uses Leo Tolstoy and Anna Karenina. 

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Confidence, Part II

Now the confidence heuristic is not the only thing Trump takes advantage of, but we will leave those for another time. I will also avoid the question of whether or not Trump is actually confident. So what is the relationship of confidence and decision making? Daniel Kahneman in Thinking, Fast and Slow on page 13  describes:

a puzzling limitation of our mind:  our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in. We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events. Overconfidence is fed by the illusory certainty of hindsight.

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Kind and Wicked Learning Environments

This post is based on a paper: “The Two Settings of Kind and Wicked Learning Environments” written by Robin M. Hogarth, Tomás Lejarraga, and Emre Soyer that appeared in Current Directions in Psychological Science 2015, Vol. 24(5) 379–385. Hogarth created the idea of kind and wicked learning environments and it is discussed in his book Educating Intuition.

Hogarth et al state that inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate  inferences. Wicked learning environments involve mismatches.

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Parallel Constraint Satisfaction Model updated with Learning

This post is based on a draft dated July 10, 2015, “Learning in Dynamic Probabilistic Environments: A Parallel-constraint Satisfaction Network-model Approach,” written by Marc Jekel, Andreas Glöckner, & Arndt Bröder. The paper includes experiments that contrast Parallel Constraint Satisfaction with the Adaptive Toolbox Approach. I have chosen to look only at the update of the PCS model with learning. The authors develop an integrative model for decision making and learning by extending previous work on parallel constraint satisfaction networks with algorithms of backward error-propagation learning. The Parallel Constraint Satisfaction Theory for Decision Making and Learning (PCS-DM-L) conceptualizes decision making as process of coherence structuring in which learning is achieved by adjusting network weights from one decision to the next. PCS-DM-L predicts that individuals adapt to the environment by gradual changes in cue weighting.

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Nonlinear

This post is based on a paper: “Learning from experience in nonlinear environments: Evidence from a competition scenario,” authored by Emre Soyer and Robin M. Hogarth, Cognitive Psychology 81 (2015) 48-73. It is not a new topic, but adds to the evidence of our nonlinear shortcomings.

In 1980, Brehmer questioned whether people can learn from experience – more specifically, whether they can learn to make appropriate inferential judgments in probabilistic environments outside the psychological laboratory. His assessment was quite pessimistic. Other scholars have also highlighted difficulties in learning from experience. Klayman, for example, pointed out that in naturally occurring environments, feedback can be scarce, subject to distortion, and biased by lack of appropriate comparative data. Hogarth asked when experience-based judgments are accurate and introduced the concepts of kind and wicked learning environments (see post Learning, Feedback, and Intuition). In kind learning environments, people receive plentiful, accurate feedback on their judgments; but in wicked learning environments they don’t. Thus, Hogarth argued, a kind learning environment is a necessary condition for learning from experience whereas wicked learning environments lead to error. This paper explores the boundary conditions of learning to make inferential judgments from experience in kind environments. Such learning depends on both identifying relevant information and aggregating information appropriately. Moreover, for many tasks in the naturally occurring environment, people have prior beliefs about cues and how they should be aggregated.

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