Category Archives: Uncertainty

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.

 

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?).

Continue reading

Taming Uncertainty

Taming Uncertainty  by Ralph Hertwig (See posts Dialectical Bootstrapping and Harnessing the Inner Crowd.), Timothy J Pleskac (See post Risk Reward Heuristic.), Thorsten Pachur (See post Emotion and Risky Choice.) and the Center for Adaptive Rationality, MIT Press, 2019, is a new compendium that I found accidentally in a public library. There is plenty of interesting reading in the book. It takes the adaptive toolbox approach as opposed to the Swiss Army Knife . The book gets back cover raves from Cass Sunstein (See posts Going to Extremes, Confidence, Part 1.), Nick Chater, and Gerd Gigerenzer (See post Gigerenzer–Risk Saavy, and others.). I like the pieces, but not the whole.

 

Continue reading

Confidence, Part III

In Confidence, Part II, the authors conclude that confidence is computed continuously, online, throughout the decision making process, thus lending support to models of the mind as a device that computes with probabilistic estimates and probability distributions.

 

The Embodied Mind

One such explanation is that of predictive processing/embodied mind. Andy Clark, Jacob Hohwy, and Karl Friston have all helped to weave together this concept. Our minds are blends of top down and bottom up processing where error messages and the effort to fix those errors makes it possible for us to engage the world. According to the embodied mind model, our minds do not just reside in our heads. Our bodies determine how we interact with the world and how we shape our world so that we can predict better. Our evolutionary limitations have much to do with how our minds work. One example provided by Andy Clark and Barbara Webb is a robot without any brain imitating human walking nearly perfectly (video go to 2:40). Now how does this tie into confidence?  Confidence at a conscious level is the extent of our belief that our decisions are correct. But the same thing is going on as a fundamental part of perception and action. Estimating the certainty of our own prediction error signals of our own mental states and processes is as Clark notes:  “clearly a delicate and tricky business. For it is the prediction error signal that…gets to ‘carry the news’.”

Continue reading

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.

Continue reading

Confidence, Part I

Confidence is defined as our degree of belief that a certain thought or action is correct. There is confidence in your own individual decisions or perceptions and then the between person confidence where you defer your own decision making to someone else.

Why am I thinking of confidence? An article by Cass Sunstein explains it well. The article appeared in Bloomberg, Politics & Policy, October 18, 2018, Bloomberg Opinion, “Donald Trump is Amazing. Here’s the Science to Prove It.”

Continue reading

A Nice Surprise

This post is based on a paper written by Andy Clark, author of Surfing Uncertainty (See Paper Predictive Processing for a fuller treatment.),A nice surprise? Predictive processing and the active pursuit of novelty,”  that appeared in Phenomenology and the Cognitive Sciences, pp. 1-14. DOI: 10.1007/s11097-017-9525-z. For me this is a chance to learn how Andy Clark has polished up his arguments since his book.  It also strikes me as connected to my recent posts on Curiosity and Creativity.

Clark and Friston (See post The Prediction Machine) depict human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory simulations. But we know that we are attracted to the unexpected. We humans often seem to actively seek out surprising events, deliberately seeking novel and exciting streams of sensory stimulation. So how does that square with the idea of minimizing prediction error.

Continue reading

Denver Bullet Study

This post is largely a continuation of the Kenneth R Hammond post, but one prompted by recent current events. My opinion on gun control is probably readily apparent. But if it is not, let me say that I go crazy when mental health is bandied about as the reason for our school shootings or when we hear that  arming teachers is a solution to anything. However,  going crazy or questioning the sincerity of people with whom you are arguing is not a good idea. Dan Kahan (See my posts Cultural Cognition or Curiosity or his blog Cultural Cognition) has some great ideas on this, but Ken Hammond actually had accomplishments and they could help guide all of us today. I should note also that I was unable to quickly find the original sources so I am relying completely on: “Kenneth R. Hammond’s contributions to the study of judgment and decision making,” written by Mandeep K. Dhami and Jeryl L. Mumpower that appeared in  Judgment and Decision Making, Vol. 13, No. 1, January 2018, pp. 1–22.

Continue reading

Dark Room Problem- Minimizing Surprise

dark_roomThis post is based on the paper: “Free-energy minimization and the dark-room problem,” written by Karl Friston, Christopher Thornton and Andy Clark that appeared in Frontiers in Psychology in May 2012. Recent years have seen the emergence of an important new fundamental theory of brain function (Posts Embodied Prediction and Prediction Error Minimization). This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose over arching principle is the minimization of surprise (or, equivalently, the maximization of expectation). A puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. People do not simply seek a dark, unchanging chamber, and stay there. This is the “Dark-Room Problem.”

Continue reading

Aging and Decisions from Experience

homerThis post is based on the paper: “The role of cognitive abilities in decisions from experience: Age differences emerge as a function of choice set size,” by Renato Frey, Rui Mata,  and Ralph Hertwig that appeared in Cognition 142 (2015) 60–80.

People seldom enjoy access to summarized information about risky options before making
a decision except for things like weather forecasts that explicitly state a probability. Instead, they may search for information and learn from the environment—thus making decisions from experience. Many consequential decisions—including health care choices, finances, and everyday risks (e.g., driving in bad weather; crossing a busy street)—are made without full knowledge of the possible outcomes and their probabilities so we must make decisions from experience. According to the authors, the mind’s most notable transformation across the life span is a substantial decline in processing speed, working memory and short-term memory capacity —all components potentially involved in search and learning processes.

Continue reading