Overcoming an Aversion to Loss

Most of us don’t like losing. In fact, it’s what the academics call loss aversion. We feel the pain of loss more acutely than we feel the pleasure of gain. In other words, we may like to win, but we hate to lose.

The psychologists Daniel Kahneman and Amos Tversky showed that even something as simple as a coin toss demonstrates our aversion to loss. In a recent interviews, Mr. Kahneman shared the usual response he gets to his offer of a coin toss:

“In my classes, I say: ‘I’m going to toss a coin, and if it’s tails, you lose $10. How much would you have to gain on winning in order for this gamble to be acceptable to you?’

“People want more than $20 before it is acceptable. And now I’ve been doing the same thing with executives or very rich people, asking about tossing a coin and losing $10,000 if it’s tails. And they want $20,000 before they’ll take the gamble.”

In other words, we’re willing to leave a lot of money on the table to avoid the possibility of losing.

We see this aversion to loss play out in the lives of real people when we try to make smart money decisions, especially when it’s time to make a change to our investments. It almost doesn’t matter what change we need to make. We hesitate to change from the current situation because it means having an opinion and making a decision. And with a decision comes the very real possibility that we’ll make the wrong one. Sticking with the status quo feels much better even if we know it’s costing us money.

To get past our aversion to loss, I recommend taking the Overnight Test.

Imagine you went to bed, and overnight someone sold your losing stock and replaced it with cash. The next morning, you have a choice: You can buy back the stock for the same price, or you can take that cash and (do something else with it). What would you do?

Most people wouldn’t buy the stock back.

Just by changing your perspective (investing cash versus getting rid of the stock), you can gain clarity and have the emotional space to make the decision you know you need to make.

Sometimes, that’s all it takes. While we’ll probably never embrace loss, it’s good to know that we can find ways to work around our aversion to it when it makes sense.

Carl Richards writing in the New York Times

AI Definitions: Symbolic Artificial Intelligence

Symbolic Artificial Intelligence – The dominant area of research for most of AI’s history until artificial neural networks became the center of most of the recent developments in artificial intelligence. Symbolic AI requires programmers to meticulously define the rules that specify the behavior they want from an intelligent system. It works well when the environment is predictable, and the rules are clear-cut. Researchers believed if they  programmed enough rules and logic into computers, they could create machines capable of human-like reasoning.  Despite the fact that symbolic AI has lost its luster in the last few years, most of the applications we use today are rule-based systems. An alternative approach to AI is machine learning. Some believe the future of AI lies in a hybrid combination of these approaches.

More AI definitions here.

17 Articles about Amazing Things AI can do now

A diagnostic tool that uses DNA sequencing & machine learning to detect multiple diseases from a single blood sample – Inside Precision Medicine

In a showdown of psychotherapists vs. ChatGPT, the latter wins, new study finds – Fortune  

Matchmakers in India Now Have Competition: AI – The Walrus 

AI invented a new miracle material that's as strong as steel but light as foam – BGR 

How regular people are cashing in on AI - ZDnet

A new AI tool allowed me to talk to my 80-year-old self. It’s going to be quite a life. – Wall Street Journal

AI Comes to the Apple Orchard—From Pollinating to Picking - Wall Street Journal

From zero to millions? How regular people are cashing in on AI - ZDnet 

Meta’s AI-Powered Ray-Bans Are Life-Enhancing for the Blind - Wall Street Journal 

Using AI missing Malaysia Airlines flight MH370 that disappeared in 2014 – Economic Times

Google’s X spins out Heritable Agriculture, a startup using AI to improve crop yield – Tech Crunch

A German startup specializing in geospatial data, is using sensing technology in autonomous vehicles to map the seafloor to strengthen underwater military defense – Wall Street Journal

AI designed computer chips so complex that humans can’t understand them – BGR

DeepMind AI crushes tough maths problems on par with top human solvers – Nature

Using A.I., Researchers Peer Inside a 2,000-Year-Old Scroll Charred by Mount Vesuvius’ Eruption – Smithsonian Magazine  

Generative AI meets Venn diagrams in a quite unique interface. – SuperRandom

Cancer could be spotted early on thanks to new 'human-defying' AI-powered body scan – Daily Record  

Throwing Good Money after Bad

Imagine a company that has already spent $50 million on a project. The project is now behind schedule and the forecasts of its ultimate returns are less favorable than at the initial planning stage. An additional investment of $60 million is required to give the project a chance. An alternative proposal is to invest the same amount in a new project that currently looks likely to bring higher returns. What will the company do? All too often a company afflicted by sunk costs drives into the blizzard, throwing good money after bad rather than accepting the humiliation of closing the account of a costly failure.

(This) fallacy keeps people for too long in poor jobs, unhappy marriages, and unpromising research projects. I have often observed young scientists struggling to salvage a doomed project when they would be better advised to drop it and start a new one. Fortunately, research suggests that at least in some contexts the fallacy can be overcome. (It) is taught as a mistake in both economics and business courses, apparently to good effect: there is evidence that graduate students in these fields are more willing than others to walk away from a failing project.

Daniel Kahneman, Thinking, Fast and Slow

AI-generated Ratings for Opinion Pieces

Some Los Angeles Times opinion pieces will now be published with an AI-generated rating of their political content, and an AI-generated list of alternative political views on that issue. The AI-generated tool “operates independently” from the paper’s human journalists, and “the AI content is not reviewed by journalists before it is published.” - The Guardian

Self-Awareness and Critical Thinking

At the root of our effectiveness is our ability to grasp the world around us and to take the measure of our own performance. We are constantly making judgments about what we know and don't know whether we're capable of handling a task or solving a problem. As we work at something, we keep an eye on ourselves, adjusting our thinking or actions as we progress.

Monitoring your own thinking is what psychologists call metacognition (meta is Greek for "about".) Learning to be accurate self-observers helps us stay out of blind alleys, make good decisions, and reflect on how we might do better next time. An important part of this skill is being sensitive to the ways we can delude ourselves. One problem with poor judgment is that we usually don't know when we've got it. Another problem is the sheer scope of the ways our judgment can be led astray.

To become more competent, or even expert, we must learn to recognize competence when we see it in others, become more accurate judges of what we ourselves know and don't know, adopt learning strategies that get results, and find objective ways to track our progress.

Peter C. Brown and Henry L. Roediger III, Make It Stick: The Science of Successful Learning

Spiritual Friendship

Spiritual friends aren’t looking to get ahead. This friend weeps with you in anxiety, rejoices with you in prosperity, seeks with you in doubts. Nothing is faked; everything is in the open. A relationship that grows into something holy, voluntary, and true is one of life’s greatest pleasures and a reward in itself. It’s a “wondrous consolation” to have someone in whom your spirit can rest, to whom you can simply pour out your soul. 

Karen Wright Marsh, Vintage Saints and Sinners

Setting the Standard

Excellent performers judge themselves differently than most people do. They're more specific, just as they are when they set goals and strategies. Average performers are content to tell themselves that they did great or poorly or okay.  

By contrast, the best performers judge themselves against a standard that's relevant for what they're trying to achieve. Sometimes they compare their performance with their own personal best; sometimes they compare it with the performance of competitors they're facing or expect to face; sometimes they compare it with the best known performance by anyone in the field.  

Any of those can make sense; the key, as in all deliberate practice, is to choose a comparison that stretches you just beyond your current limits. Research confirms what common sense tells us, that too high a standard is discouraging and not very instructive, while too low a standard produces no advancement.  

Geoff Colvin, Why Talent is Overrated  

How to Spot a Liar

When an international team of researchers asked some 2,300 people in 58 countries to respond to a single question — “How can you tell when people are lying?” — one sign stood out: In two-thirds of responses, people listed gaze aversion. A liar doesn’t look you in the eye. Twenty-eight percent reported that liars seemed nervous, a quarter reported incoherence, and another quarter that liars exhibited certain little giveaway motions.

It just so happens that the common wisdom is false.

Why do we think we know how liars behave? Liars should divert their eyes. They should feel ashamed and guilty and show the signs of discomfort that such feelings engender. And because they should, we think they do.

The desire for the world to be what it ought to be and not what it is permeates experimental psychology as much as writing, though. There’s experimental bias and the problem known in the field as “demand characteristics” — when researchers end up finding what they want to find by cuing participants to act a certain way. It’s also visible when psychologists choose to study one thing rather than another, dismiss evidence that doesn’t mesh with their worldview while embracing that which does.

Maria Konnikova writing in the New York Times

Future You

Future You is a new interactive artificial-intelligence platform that allows users to create a virtual older self—a chatbot that looks like an aged version of the person, then personalized with information that the user puts in. The idea is that if people can see and talk to their older selves, they will be able to think about them more concretely, and make changes now that will help them achieve the future they hope for. -Wall Street Journal  

An Arms Race of Research Misconduct

Retractions are rising in medical research literature, even as more eyes examine peer-reviewed papers for accuracy. AI is powering an arms race in the world of research misconduct, making it easier for scientific fraud to occur, and for editors to identify and root out. In 2002, 1 in 5,000 papers were retracted, Oransky said. In 2023 retractions increased to 1 in 500 papers. -AAPS news magazine

Self-Reflection is Not Enough

Arthur Chickering writes that helping “students deepen their understanding about reaching for authenticity and spiritual growth ... starts and ends with self-reflection and employs that throughout.”

To say the journey is all about self-reflection is too restrictive. The learning adventure may dip into navel-gazing occasionally, but discovering the insights of other people who have gone before will not happen quickly. A fruitful journey requires a shift away from the self toward a focus on something greater. A student must sort through the dirt to discover valuable nuggets of truth. This often happens through reading the great thinkers and then wrestling with the questions and ideas we discover. We stand on the shoulders of others to peer down the road a bit further than we could on our own. We can waste time and energy by insisting on clearing a path by ourselves.

The Chickering quote comes from his book "Encouraging Authenticity and Spirituality in Higher Education." Chickering understands the value of encouraging students to read great works. But his goal, he writes, is to help them evolve their “own" answers. However, good teaching goes beyond applauding young people just for coming to their own conclusions. Will the answers stand up to criticism? Can they effectively defend their positions? More importantly, can they live those answers?

Stephen Goforth

Jobs on Creativity

Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it; they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.

Steve Jobs (born Feb, 24, 1955)

AI-generated Murder Stories

A so-called “true crime” YouTube channel has millions of views with AI-generated murder stories — none of which actually happened. There was no language on the channel’s homepage or in video descriptions to tell a viewer otherwise. If you looked at the comments on his videos, there were a lot of people who couldn’t tell they weren’t real. -404 Media