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~ Finding, formulating and solving life's frustrations.

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Tools of Thought

14 Sunday Dec 2025

Posted by petersironwood in AI, creativity, design rationale, management, psychology, science, Uncategorized

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AI, chatgpt, index, life, problem formulation, problem framing, problem solving, sense-making, summary, technology, thinking, tools of thought, writing

Tools of Thought (Summary and Index)

In December, 2018, I began writing a series of essays on “tools of thought.” I realize that many readers probably read these tools at the time they were first published. However, in times of great division such as those we now face, effective thinking is more important than ever yet every day in the news and in social media, I see many examples that overlook even the most basic tools of thought. I therefore decided that it would be worthwhile to reprint the index of such tools now.

I suppose many readers will already be familiar with many of these tools. Nonetheless, I think it’s worthwhile to have a compilation of tools. After all — plumbers, carpenters, programmers, piano tuners, sales people — they all have tool kits. I see at least three advantages to having them together in some one place.

Without a toolkit you may be prone to try to use the tool that just so happens to be nearest to hand at the time you encounter the problem. You need to tighten a screw and you happen to have a penny in your pocket. You don’t feel like walking all the way down into the garage to get your toolkit. A penny will do. I get it. But for more serious work, you are going to want to consider the whole toolkit and choose the tool that’s most appropriate to the situation at hand.

First, then, the existence of a toolkit serves as a reminder of all the tools at your disposal. This will help you choose appropriately. 

Second, you may only be familiar with one or two ways to use a tool. I may have thought of ways to use a tool that are different from the way you use it. In the same way, you undoubtedly know useful things about these tools of thought that I have never thought of. We can learn from each other. Readers are more than welcome to comment on uses, misuses, and variations.

Third, having all the tools together may stimulate people to invent new tools or see a way to use two or more in sequence and begin to think about the handoff between two tools. 

Here’s an index to the toolkit so far.

Many Paths(December 5, 2018). The temptation is great to jump to a conclusion, snap up the first shiny object that looks like bait and charge ahead! After all, “he who hesitates is lost!” But there is also, “look before you leap.” What works best for me in many circumstances is to think of many possible paths before deciding on one. This is a cousin to the Pattern: Iroquois Rule of Six. This heuristic is a little broader and is sometimes called “Alternatives Thinking.”

Many Paths

And then what?(Dec. 6, 2018). This is sometimes called “Consequential Thinking.” The idea is simple: think not just about how you’ll feel and how a decision will affect you this moment but what will happen next. How will others react? It’s pretty easy to break laws if you set your mind to it. But what are the likely consequences?

And, then what?

Positive Feedback Loops(December 7, 2018). Also known as a virtuous or vicious circle. If you drink too much of a depressant drug (e.g., alcohol or opioids), that can cause increased nervousness and anxiety which leads you to want more of the drug. Unfortunately, it also makes your body more tolerant of the drug so you need more to feel the same relief. So, you take more but this makes you even more irritable when it wears off.

Systems Thinking: Positive Feedback Loops

Meta-Cognition.(December 8, 2018). This is basically thinking about thinking. For example, if you are especially good at math, then you tend to do well in math! Over time, if your meta-cognition is accurate, you will know that you are good in math and you can use that information about your own cognition to make decisions about the education you choose, your job, your methods of representing and solving problems and so on.

Meta-Cognition

Theory of Mind(December 9, 2018). Theory of Mind tasks require us to imagine the state of another mind. It is slightly different from empathy, but a close cousin. Good mystery writers – and good generals – may be particularly skilled at knowing what someone else knows, infers, thinks, feels and therefore, how they are likely to act.

Theory of Mind

Regression to the Mean(December 10, 2018). This refers to a statistical artifact that you sometimes need to watch out for. If you choose to work with the “best” or “worst” or “strongest” or “weakest” and then measure them again later, their extreme scores will be less extreme. The tool is to make sure that you don’t make untoward inferences from that change in the results of the measurement.

Regression to the Mean

Representation(December 11, 2018): The way we represent a problem can make a huge difference in how easy it is to solve it. Of course, we all know this, and yet, it is easy to fall into the potential trap of always using the same representations for the same types of problems. Sometimes, another representation can lead you to completely different – and better – solutions.

 Representation 

Metaphor I (December 12, 2018): Do we make a conscious choice about the metaphors we use? How can metaphors influence behavior?

Metaphors We Live By and Die By

Metaphor II (December 13, 2018): Two worked examples: Disease is an Enemy and Politics is War.

Metaphors We Live and Die By: Part 2

Imagination (December 14, 2018): All children show imagination. Many adults mainly see it as a tool for increasing their misery; viz., by only imagining the worst. Instead of a tool to help them explore, it becomes a “tool” to keep themselves from exploring by making everything outside the habitual path look scary.

Imagination

Fraught Framing (December 16, 2018): Often, how we frame a problem is the most crucial step in solving it. In this essay, several cases are examined in which people presume a zero-sum game when it certainly need not be.

Fraught Framing: The Virulent “Versus” Virus

Fraught Framing II(December 17, 2018). A continuation of thinking about framing. This essay focuses on how easy it sometimes is to confuse the current state of something with its unalterable essence or nature. 

Fraught Framing: The Presumed Being-ness of State-ness

Negative Space(December 17, 2018). Negative space is the space between. Often we separate a situation into foreground and background, or into objects and field, or into assumptions and solution space. What if we reverse these designations?

Negative Space

Problem Finding(December 18, 2018). Most often in our education, we are handed problems and told to solve them. In real life, success is as much about being able to find problems or see problems in order to realize that there is even something to fix.

Problem Finding

More recently, I wrote a series of posts about the importance of Problem Finding, Problem Framing, and Problem Formulation. I haven’t yet put this in the form of “Tools of Thought” — these posts are specific experiences from my own life where I initially mis-formulated a problem or watched my friends do that. 

The Doorbell’s Ringing! Can you get it?
Reframing the Problem: Paperwork & Working Paper
Problem Framing: Good Point!
I Say: Hello! You Say: “What City Please?”
I Went in Seeking Clarity
Problem Formulation: Who Knows What?
Wordless Perfection
How to Frame Your Own Hamster Wheel
Measure for Measure
The Slow-Seeming Snapping Turtle
A Long Day’s Journey into Hangover
Training Your Professor for Fun & Profit
Astronomy Lesson: Invisible Circles
Tag! You’re it!
Ohayōgozaimasu
Career Advice from Polonius

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Author Page on Amazon

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Non-Linearity. (December 20, 2018). We often think that things are linear when they may not be. In some cases, they can be severely non-linear. Increasing the force on a joint may actually make it stronger. But if increased force is added too quickly, rather than strengthening the joint even further, it can destroy it. The same is true of a system like American democracy.

Non-Linearity

Resonance. (December 20, 2018). If you add your effort to something at the right time, you are able to multiply the impact of your effort. This is true in sports, in music, and in social change.

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Resonance

Symmetry(December 23, 2018). There are many kinds of symmetry and symmetry is found in many places; it is rampant in nature, but humans in all different cultures also use symmetry. It exists at macro scales and micro scales. It exists in physical reality and in social relationships.

Symmetry

Other posts that are related to various mental errors you might want to avoid.

Labelism

Wednesday

The Stopping Rule

Finding the Mustard

What about the Butter Dish?

Where does your Loyalty Lie?

Roar, Ocean, Roar

The Update Problem

The Invisibility Cloak of Habit

The Impossible

Your Cage is Unlocked

We won the war! We won the war!

The self-made man

Madison Keys, Francis Scott Key, the “Prevent Defense” and giving away the Keys to the Kingdom. 

12 Friday Dec 2025

Posted by petersironwood in America, family, management, psychology, sports, Uncategorized

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art, books, bravery, Business, career, choice, courage, HCI, human factors, IBM, life, school, sports, technology, Travel, UX

Madison Keys, Francis Scott Key, the “Prevent Defense” and giving away the Keys to the Kingdom. 

Madison Keys, for those who don’t know, is an up-and-coming American tennis player. In Friday’s Wimbledon match of July, 2018, Madison sprinted to an early 4-1 lead. She accomplished this through a combination of ace serves and torrid ground strokes. Then, in an attempt to consolidate, or protect her lead, or play the (in)famous “prevent defense” imported from losing football coaches, she managed to stop hitting through the ball – guiding it carefully instead — into the net or well long or just inches wide. 

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Please understand that Madison Keys is a wonderful tennis player. And, her “retreat” to being “careful” and playing the “prevent defense” is a common error that both professional and amateur players fall prey to. It should also be pointed out that what appears to be overly conservative play to me, as an outside observer, could easily be due to some other cause such as a slight injury or, even more likely, because her opponent adjusted to Madison’s game. Whether or not she lost because of using the “prevent defense” no-one can say for sure. But I can say with certainty that many people in many sports have lost precisely because they stopped trying to “win” and instead tried to protect their lead by being overly conservative; changing the approach that got them ahead. 

Francis Scott Key, of course, wrote the words to the American National Anthem which ends on the phrase, “…the home of the brave.” Of course, every nation has stories of people behaving bravely and the United States of America is no exception. For the American colonies to rebel against the far superior naval and land forces (to say nothing of sheer wealth) of the British Empire certainly qualifies as “brave.” 

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In my reading of American history, one of our strengths has always been taking risks in doing things in new and different ways. In other words, one of our strengths has been being brave. Until now. Now, we seem in full retreat. We are plunging headlong into the losing “prevent defense” borrowed from American football. 

American football can hardly be called a “gentle sport” – the risk of injury is ever present and now we know that even those who manage to escape broken legs and torn ligaments may suffer internal brain damage. But there is still the tendency of many coaches to play the “prevent defense.” In case you’re unfamiliar with American football, here is an illustration of the effect of the “prevent defense” on the score. A team plays a particular way for 3 quarters of the game and is ahead 42-21. If you’re a fan of linear extrapolation, you might expect that  the final score might be something like 56-28. But coaches sometimes want to “make sure” they win so they play the “prevent defense” which basically means you let the other team make first down after first down and therefore keep possession of the ball and score, though somewhat slowly. The coach suddenly loses confidence in the method which has worked for 3/4 of the game. It is not at all unusual for the team who employs this “prevent defense” to lose; in this example, perhaps, 42-48. They “let” the other team get one first down after another. 

red people outside sport

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America has apparently decided, now, to play a “prevent defense.” Rather than being innovative and bold and embracing the challenges of new inventions and international competition, we instead want to “hold on to our lead” and introduce protective tariffs just as we did right before the Great Depression. Rather than accepting immigrants with different foods, customs, dress, languages, and religions — we are now going to “hold on to what we have” and try to prevent any further evolution. In the case of American football, the prevent defense sometimes works. In the case of past civilizations that tried to isolate themselves, it hasn’t and it won’t. 

landscape photography of gray rock formation

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This is not to say that America (or any other country) should right now have “open borders” and let everyone in for every purpose. (Nor, by the way, has any politician of any party suggested that we do that). Nor should a tennis player hit every shot with all their might. Nor should a football team try the riskiest possible plays at every turn. All systems need to strike a balance between replicating what works–providing defense of what one has while also bravely exploring what is new and different. That is what nature does. Every generation “replicates” aspects of the previous generation but every generation also explores new directions. Life does this through sexual selection, mutation, and cross over. 

This balance plays out in career as well. You need to decide for yourself how much and what kinds of risks to take. When I obtained my doctorate in experimental psychology, for example, it would have been relatively un-risky in many ways to get a tenure-track faculty position. Instead, I chose managing a research project on the psychology of aging at Harvard Med School. To be sure, this is far less than the risk that some people take when; e.g., joining “Doctors without borders” or sinking all their life savings (along with all the life savings of their friends and relatives) into a start-up. 

At the time, I was married and had three small children. Under these circumstances, I would not have felt comfortable having no guaranteed income. On the other hand, I was quite confident that I could write a grant proposal to continue to get funded by “soft money.” Indeed, I did write such a proposal along with James Fozard and Nancy Waugh who were at once my colleagues, my bosses, and my mentors. Our grant proposal was not funded or rejected but “deferred” and then it was deferred again. At that point, only one month of funding remained before I would be out of a job. I began to look elsewhere. In retrospect, we all realized it would have been much wiser to have a series of overlapping grants so that all of our “funding eggs” were never in one “funding agency’s basket.” 

brown chicken egg

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I began looking for other jobs and had a variety of offers from colleges, universities, and large companies. I chose IBM Research. As it turned out, by the way, our grant proposal was ultimately funded for three years, but we only found out after I had already committed to go to IBM. During this job search, I was struck by something else. My dissertation had been on problem solving but my “post-doc” was in the psychology of aging. So far as I could tell, this didn’t bother any of the interviewers in industry in the slightest. But it really freaked out some people in academia. It became clear that one was “expected” in academia, at least by many, that one would choose a specialty and stick with it. Perhaps, one need not do that during their entire academic career, but anything less than a decade smacked of dilettantism. At least, that was how it felt to me as an interviewee. By contrast, it didn’t bother the people who interviewed me at Ford or GM that I knew nothing more than the average person about cars and had never really thought about the human factors of automobiles. 

Photo by Pixabay on Pexels.com
Photo by Pixabay on Pexels.com
Photo by Pixabay on Pexels.com
Photo by Pixabay on Pexels.com

The industrial jobs paid more than the academic jobs and that played some part in my decision. The job at GM sounded particularly interesting. I would be “the” experimental psychologist in a small inter-disciplinary group of about ten people who were essentially tasked with trying to predict the future. The “team” included an economist, a mathematician, a social psychologist, and someone who looked for trends in word frequencies in newspapers. The year was 1973 and US auto companies were shocked and surprised to learn that their customers suddenly cared about gas mileage! These companies didn’t want to be shocked and surprised like that again. The assignment reminded me of Isaac Asimov’s fictional character in the Foundation Trilogy — Harry Seldon — who founded “psychohistory.” We had the chance to do it in “real life.” It sounded pretty exciting! 

antique auto automobile automotive

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On the other hand, cars seemed to me to be fundamentally an “old” technology while computers were the wave of the future. It also occurred to me that a group of ten people from quite different disciplines trying to predict the future might sound very cool to me and apparently to the current head of research at GM, but it might seem far more dispensable to the next head of research. The IBM problem that I was to solve was much more fundamental. IBM saw that the difficulty of using computers could be a limiting factor in their future growth. I had had enough experience with people — and with computers — to see this as a genuine and enduring problem for IBM (and other computer companies); not as a problem that was temporary (such as the “oil crisis” appeared to be in the early 70’s). 

airport business cabinets center

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There were a number of additional reasons I chose IBM. IBM Research’s population at the time showed far more diversity than that of the auto companies. None of them were very diverse when it came to male/female ratios. At least IBM Research did have people from many different countries working there and it probably helped their case that an IBM Researcher had just been awarded a Nobel Prize. Furthermore, the car company research buildings bored me; they were the typical rectangular prisms that characterize most of corporate America. In other words, they were nothing special. Aero Saarinen however, had designed the IBM Watson Research Lab. It sat like an alien black spaceship ready to launch humanity into a conceptual future. It was set like an onyx jewel atop the jade hills of Westchester. 

I had mistakenly thought that because New York City was such a giant metropolis, everything north of “The City” (as locals call it) would be concrete and steel for a hundred miles. But no! Westchester was full of cut granite, rolling hills, public parks of forests marbled with stone walls and cooled by clear blue lakes. My commute turned out to be a twenty minute, trafficless drive through a magical countryside. By contrast, since Detroit car companies at that time held a lot of political power, there was no public transportation to speak of in the area. Everyone who worked at the car company headquarters spent at least an hour in bumper to bumper traffic going to work and another hour in bumper to bumper traffic heading back home. In terms of natural beauty, Warren Michigan just doesn’t compare with Yorktown Heights, NY. Yorktown Heights even smelled better. I came for my interview just as the leaves began painting their autumn rainbow palette. Even the roads in Westchester county seemed more creative. They wandered through the land as though illustrative of Brownian motion, while Detroit area roads were as imaginative as graph paper. Northern Westchester county sports many more houses now than it did when I moved there in late 1973, but you can still see the essential difference from these aerial photos. 

YorktownHts-map

Warren-map

The IBM company itself struck me as classy. It wasn’t only the Research Center. Everything about the company stated “first class.” Don’t get me wrong. It wasn’t a trivial decision. After grad school in Ann Arbor, a job in Warren kept me in the neighborhood I was familiar with. A job at Ford or GM meant I could visit my family and friends in northern Ohio much more easily as well as my colleagues, friends and professors at the U of M. The offer from IBM felt to me like an offer from the New York Yankees. Of course, going to a top-notch team also meant more difficult competition from my peers. I was, in effect, setting myself up to go head to head with extremely well-educated and smart people from around the world. 

You also need to understand that in 1973, I would be only the fourth Ph.D. psychologist in a building filled with physicists, mathematicians, computer scientists, engineers, and materials scientists. In other words, nearly all the researchers considered themselves to be “hard scientists” who delved in quantitative realms. This did not particularly bother me. At the time, I wanted very much to help evolve psychology to be more quantitative in its approach. And yet, there were some nagging doubts that perhaps I should have picked a less risky job in a psychology department. 

The first week at IBM, my manager, John Gould introduced me to yet another guy named “John” —  a physicist whose office was near mine on aisle 19. This guy had something like 100 patents. A few days later, I overheard one of that John’s younger colleagues in the hallway excitedly describing some new findings. Something like the following transpired: 

“John! John! You can’t believe it! I just got these results! We’re at 6.2 x 10 ** 15th!” 

His older colleague replied, “Really? Are you sure? 6.2 x 10 ** 15th?” 

John’s younger colleague, still bubbling with enthusiasm: “Yes! Yes! That’s right. You know. Within three orders of magnitude one way or the other!” 

I thought to myself, “three orders of magnitude one way or the other? I can manage that! Even in psychology!” I no longer suffered from “physics envy.” I felt a bit more confident in the correctness of my decision to jump into these waters which were awash with sharp-witted experts in the ‘hard’ sciences. It might be risky, but not absurdly risky.

person riding bike making trek on thin air

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Of course, your mileage may differ. You might be quite willing to take a much riskier path or a less risky one. Or, maybe the physical location or how much of a commute is of less interest to you than picking the job that most advances your career or pays the most salary. There’s nothing wrong with those choices. But note what you actually feel. Don’t optimize in a sequence of boxes. That is, you might decide that your career is more important than how long your commute is. Fair enough. But there are limits. Imagine two jobs that are extremely similar and one is most likely a little better for your career but you have to commute two hours each way versus 5 minutes for the one that’s not quite so good for your career. Which one would you pick? 

In life beyond tennis and beyond football, one also has to realize that your assessment of risk is not necessarily your actual risk. Many people have chosen “sure” careers or “sure” work at an “old, reliable” company only to discover that the “sure thing” actually turned out to be a big risk. I recall, for example, reading an article in INC., magazine that two “sure fire” small businesses were videotape rental stores and video game arcades. Within a few years of that article, they were almost sure-fire losers. Remember Woolworths? Montgomery Ward?

At the time I joined IBM, it was a dominant force in the computer industry. But there are no guarantees — not in career choices, not in tennis strategy, not in football strategy, not in playing the “prevent defense” when it comes to America. The irony of trying too hard to “play it safe” is illustrated this short story about my neighbor from Akron: 

police army commando special task force

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Wilbur’s Story

Wilbur’s dead. Died in Nam. And, the question I keep wanting to ask him is: “Did it help you face the real dangers? All those hours together we played soldier?”

Wilbur’s family moved next door from West Virginia when I was eleven. They were stupendously uneducated. Wilbur was my buddy though. We were rock-fighting the oaks of the forest when he tried to heave a huge toaster-oven sized rock over my head. Endless waiting in the Emergency Room. Stitches. My hair still doesn’t grow straight there. “Friendly fire.”

More often, we used wooden swords to slash our way through the blackberry and wild rose jungle of The Enemy; parry the blows of the wildly swinging grapevines; hide out in the hollow tree; launch the sudden ambush.

We matched strategy wits on the RISK board, on the chess board, plastic soldier set-ups. I always won. Still, Wilbur made me think — more than school ever did.

One day, for some stupid reason, he insisted on fighting me. I punched him once (truly lightly) on the nose. He bled. He fled crying home to mama. Wilbur couldn’t stand the sight of blood.

I guess you got your fill of that in Nam, Wilbur.

After two tours of dangerous jungle combat, he was finally to ship home, safe and sound, tour over — thank God!

He slipped on a bar of soap in the shower and smashed the back of his head on the cement floor.

Wilbur finally answers me across the years and miles: “So much for Danger, buddy,” he laughs, “Go for it!”

Thanks, Wilbur.

Thanks.

 

 

 

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And, no, I will not be giving away the keys to the kingdom. Your days of fighting for freedom may be over. Mine have barely begun.


Author Page on Amazon

Where does your loyalty lie? 

Essays on America: The Game

The Three Blind Mice

Roar, Ocean, Roar

Stoned Soup

The First Ring of Empathy

Math Class: Who are you?

The Last Gleam of Twilight

The Impossible

I Went in Seeking Clarity

10 Wednesday Dec 2025

Posted by petersironwood in Uncategorized, psychology, creativity, user experience, HCI, AI

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AI, Artificial Intelligence, coding, parallel programming, problem formulation, problem framing, problem solving, programming, technology, thinking, tools, X10

“I stopped by the bar at 3 A.M.
To seek solace in a bottle or possibly a friend
And I woke up with a headache like my head against a board
Twice as cloudy as I’d been the night before
And I went in seeking clarity” — Lyrics from The Indigo Girls: Closer to Fine

If you think programming is cognitively difficult, try parallel programming. It is generally harder to design, to code, and to debug than its sequential cousin. One of the fun projects I worked on at IBM Research was on the X10 language which was designed to enable parallel programmers to be more productive. Among other things, I fostered community among X10 programmers and used analytic techniques to show that X10 “should be” more productive. Although these analytic techniques are very useful, we also wanted to get some empirical data that the language was, in actuality, more productive. 


Photo by Dominika Greguu0161ovu00e1 on Pexels.com


One part of those empirical studies involved comparing people doing a few parallel programming tasks in X10 to those using a popular competitor. But, like many other “chicken and egg” problems, there were no X10 programmers (other than the inventors and their colleagues). I was part of a team who travelled to Rice University in Houston. The design called for one group to spend a chunk of time learning X10 (perhaps half a day) and another chunk of time coding some problems.

Besides the three behavioral scientists like me who were there to make observations, there were also three high-powered Ph.D. computer scientists present who would teach the language. Programmers tend to be very smart. Parallel programmers tend to be very very smart. People who can invent better languages to do parallel programming? You do the math.



Anyway, after the volunteers students had arrived, one of the main designers of the language began to “teach them” X10. 

But — there was a problem. 

The powerpoint presentation designed to teach the students X10 was far too blurry to read!

Immediately, the three computer scientists tried to issue commands to the projector to put the images in focus. Nothing worked. The three of them began a fascinating problem solving conversation. The conversation concerned what communication protocol(s) among the PC, the projector, and the controller was the likely source of the problem. I suppose it might not have been fascinating to everyone, but it was to me. First, it fascinated me because I was learning something about computer science and communication protocols. Second, it fascinated me because I loved to watch these people think. I suppose many of the advanced computer science students who were in this classroom to learn X10 also found it interesting. Third, I found it fascinating because my dissertation was about human problem solving and I’ve been interested in it ever since.

But the study itself had completely stalled. 

After a few minutes of fascinating conversation that did nothing to focus the images, something possessed me to walk over to the projector and turn the lens by hand. The images were immediately clear and the rest of the experiment continued. 

The three computer scientists had “framed” the problem as a computer science problem and I found the discussion that sprang from that framing to be fascinating. But one of the part-time jobs I had had as an undergraduate was as a “projectionist” at Case-Western, and it was that experience that allowed me to try framing the problem differently. All of us have huge reservoirs of experience outside of our professional “training” and those experiences can sometimes be important sources of alternative ways to frame a problem, issue, or situation.

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Essays on America: Wednesday 

Essays on America: The Update Problem 

Essays on America: The Stopping Rule

The Invisibility Cloak of Habit

Labelism

Tools of Thought

Where Does Your Loyalty Lie?

Stoned Soup

The First Ring of Empathy

Travels with Sadie: Teamwork

Author Page on Amazon

   

I Say: Hello! You Say: “What City Please?”

09 Tuesday Dec 2025

Posted by petersironwood in AI, creativity, design rationale, HCI, management, psychology, Uncategorized, user experience

≈ 1 Comment

Tags

art, communication, conversation, Design, efficiency, HCI, human factors, photography, primacy, problem framing, problem solving, sensemaking, technology, thinking, UX

Photo by Tetyana Kovyrina on Pexels.com

In the not so distant past, people would often call directory assistance operators. These operators would find a number for you. For an additional charge, they would dial it for you. In fact, this was a very commonly used system. Phone companies would have large rooms filled with such operators who worked very hard and very politely, communicating with what was often a hostile and irrational public. 

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Customer: “I have to get the number of that bowling alley right near where the A&P used to be before they moved into that new shopping center.”

Operator: “Sir, you haven’t told me what town you’re in. Anyway…”

Customer: “What town?! Why I’m right here in Woburn where I’ve always been!” 

Photo by Johannes Plenio on Pexels.com

There were so many operators that the phone companies wanted their processes to be efficient. Operators were trained to be friendly and genial but not chatty. The phone companies searched for better keyboards and better screen layouts to shave a second here or there off the average time it took to handle a call. 

There are some interesting stories in that attempt but that we will save for another article, but here I want to tell you what made the largest single impact on the average time per call. Not a keyboard. Not a display. Not an AI system. 

It was simply changing the greeting. 

Photo by eberhard grossgasteiger on Pexels.com

Operators were saying something like: “New England Telephone. How can I help you?” 

After our intervention, operators instead said, “What city please?” It’s shorter and it’s takes less time to say. But the big change was not in how long the operator took to ask the question. The biggest savings was how this change in greeting impacted the customer’s behavior. 

When the operator begins with “How can I help you?” the customer, or at least some fraction of them, are put into a frame of mind of a conversation. They might respond thusly:

“Oh, well, you know my niece is getting married! Yeah! In just a month, and she still hasn’t shopped for a dress! Can you believe it? So, I need the number for that — if it were up to me, I would go traditional, but my niece? She’s — she’s going avant-garde so I need the number of that dress shop on Main Street here in Arlington.” 

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With the “What City Please?” greeting, the customer was apparently put into a more businesslike frame of mind and answers more succinctly. They now understand their role as proving information in a joint problem solving task with the operator. A typical answer would now be:

“Arlington.” 

“In Arlington, what listing?” 

“Dress shop on Main Street.”

The way in which a conversation begins signals what type of conversation it is to be. We know this intuitively. Suppose you walked up to an old friend and they begin with: “Name?” You would be taken aback. On the other hand, suppose you walk up to the line at the DMV and the clerk says, “Hey, have you seen that latest blog post by J. Charles Thomas on problem framing?” You would be equally perplexed! 

Conversation can be thought of partly as a kind of mutual problem solving exercise. And, before that problem solving even begins, one party or the other will tend to “frame” the conversation. That framing can be incredibly important. 

Even the very first words can cause someone to frame what kind of a conversation this is meant to be.

Words matter.

The Primacy Effect and The Destroyer’s Advantage

https://petersironwood.com/2018/02/13/context-setting-entrance/

Essays on America: Wednesday

After the Fall

The Crows and Me

Cancer Always Loses in the End

Come Back to the Light

Imagine All the People…

Roar, Ocean, Roar

The Dance of Billions

How the Nightingale Learned to Sing

Travels with Sadie

The First Ring of Empathy

Donnie Visits Granny!

You Must Remember This

The Walkabout Diaries: Bee Wise

Author Page on Amazon 

Problem Framing: Good Point!

08 Monday Dec 2025

Posted by petersironwood in AI, America, design rationale, HCI, management, psychology, story, Uncategorized, user experience

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AI, art, life, politics, problem finding, problem formulation, problem framing, problem solving, technology, thinking, tools, USA

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You have probably heard variations on this old saw, “To a hammer, everything looks like a nail.” I’ve also heard, “If you have a hammer, everything looks like a nail.” There is also this popular anecdote:

One night, I took my dog out for a walk and I noticed one of my neighbors under a nearby street lamp crawling around on his hands and knees, apparently looking for something. I walked over and asked, “What are you looking for?”

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“My car keys!” He replied.

I have pretty good vision, so I helped him. I didn’t see any car keys so after a minute or so I asked, “Where exactly did you lose your keys?” 

He stood up, cracked his back, and pointed back to a nearby park. “Over there.”

“Over there?! Then, why are you looking under the street lamp? Why aren’t you looking over at the park entrance?”

“Oh, that’s obvious! The light is so much better here!” 

For a time, I had to very interesting and challenging job in the mid 1980’s at IBM Headquarters to try to get the company to pay more attention to the usability of their products and services. As a part of this, I visited IBM locations throughout the world. At one fabrication plant, our tour guide took us by an inspection station. This was not an inspection statement for chips. It consisted of one person whose job was to look through a microscope and make sure that two silver needles were perfectly aligned.

After we left the station, our tour guide confided that they were strongly considering replacing the person with a machine vision system. The anticipated cost would be substantial, but they hypothesized that the system would be more accurate and faster. It was, our host, insisted, just the nature of humans to be slow and inaccurate.

Maybe. 

When I looked at the inspection station however, with my background in human factors, I had a completely different impression of the situation. The inspector sat on a fixed height stool and had to bend his neck at an absurd angle to look into the microscope. He was trying to align these silver needles against a background that had almost the same hue, brightness and saturation. 

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Other than blindfolding the man, I’m not sure what they could have done to make the task more unnecessarily difficult. I suggested, and eventually, they implemented, a few inexpensive ergonomic changes and time and accuracy improved.

Like other companies in the technology segment, IBM often saw problems as ones that could be solved by technology. At that time, technology systems was their main business. Since then, they have expanded more fully into software and services. In fact, those services now include experience design.

If you find yourself enamored of technology in general, or some specific class of technology such as machine vision, speech recognition, or machine learning, you might overlook much simpler and cheaper ways to solve problems or ameliorate situations. Of course, you might lose some revenue doing that, but you can also win long term customer loyalty. 

Even if you are a hammer, everything is not a nail. 

That applies as well to User Experience. You might design the most wonderful UX imaginable for a particular product or service. But if it is shoddily made so that it is error prone; if it lacks important functionality; if the sales force is inept; or if service is horrible, those failures can completely overwhelm all the good work you have done on the UX. Because of the nature of UX, you might learn important knowledge or suggestions for other functions as well. It often requires finesse to have such suggestions taken seriously, but with some thought you can do it. 

During my second stint at IBM, I worked for a time in a field known at that time as “Knowledge Management.” One of our potential clients was a major Pharma company who felt that their researchers should do a better job of sharing knowledge across products. They wanted us to design a “knowledge management system” (by which they meant hardware and software) to improve knowledge sharing. 

Simply building a “Knowledge Management System” would be looking under the streetlamp. They knew how to specify a technology solution from IBM and have it installed.

However — they were unwilling to provide any additional space, time, or incentives for their employees to share knowledge with their colleagues!  

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They were convinced that technology would be the silver bullet, the solution, the answer, the Holy Grail, the magic pill. They viewed technology as less disruptive than it would have been to change employee incentives, or space layout, or give them time to actually learn and use the technology system. 

This reaction to “knowledge management” was not unique. It was common.

To me, this seems very similar to the notion that health problems can all be solved with a magic pill. What do you think? 

—————————————

Since originally writing, we have had the spectacle of DOGE: Destroying Our Government’s Effectiveness under the excuse of making it “more efficient.” It might be (as I strongly suspect) that the destruction was quite intentional. It might be (as some think) that it was accidental. In either case, the result was predictable because the method was guaranteed not to work to actually make things more efficient. If you really wanted to do that, you would take the time to understand a system before trying to redesign it. You would identify all relevant stakeholders and get their input. You would not redesign a system using a gang of young hackers but instead use an interdisciplinary team of experienced experts. You would check out your redesign both with those who were doing the work and with at least one group who were not familiar but had similar experience. Then, on the basis of feedback, you would redesign. When you were sure that you had the design right, you would not then institute it everywhere but in one small trial installation.

There’s a pill for that. 

The Pandemic Anti-Academic.

What about the butter dish? 

The invisibility cloak of habit. 

Process re-engineering comes to Baseball

E-Fishiness in Government

Author Page on Amazon

Measure for Measure

01 Monday Dec 2025

Posted by petersironwood in AI, essay, psychology, science, Uncategorized

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art, context, decision making, Democracy, framing, HCI, photography, politics, problem formulation, problem framing, problem solving, technology, thinking, Travel, truth, USA, UX

(More or Less is only More or Less, More or Less)

Confusing. I know. Let’s unpack. 

We like to measure things. And, generally, that can be a very good thing. Once we measure and quantify, we can bring to bear the world’s most incredible toolbox of mathematical, engineering, and scientific methods. However…

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It often happens that we can’t really measure what we’d like to measure so instead we measure something that we can measure which we imagine to be a close cousin to what we’d really like to measure. That’s still not a bad thing. But it’s risky. And it becomes a lot more risky if we forget that we are measuring a close cousin at best. Sometimes, it’s actually a distant cousin. 

Here’s an example. Suppose a company is interested in the efficient handling of customer service calls (who isn’t?). A typical measure is the average time per call. So, a company might be tempted to reward their Customer Service employees based on having a short average time per call. The result would be that the customer would get back to whatever they were doing more quickly. AND — they wouldn’t have to be on hold in the service queue so long because each call would be handled, on average, more quickly. Good for the customer. The customer service reps would be saving money for the company by answering questions quickly. Some of the money saved will (hopefully) mean raises for the customer service reps. It’s a win/win/win! 

Or is it? 

Imagine this not unlikely scenario:

The managers of the CSR’s (customer service reps) say that there’s a big push from higher management to make calls go more quickly. They may hint that if the average service time goes down enough, everyone will get a raise. Or, they might set much more specific targets to shoot for. 

In either case, the CSR’s are motivated to handle calls more quickly. But how? One way might be for them to learn a whole lot more. They might exchange stories among themselves and perhaps they will participate in designing a system to help them find relevant information more quickly. It might really turn out to be a win/win/win.

On the other hand, one can also imagine that the CSR’s instead simply get rid of “pesky” users as quickly as possible.



“Reboot and call back if that doesn’t work.” 

“Sounds like an Internet issue. Check your router.” 

“That’s an uncovered item.” 

“What’s your account number? Don’t have it? Find it & call back.” 

With answers like this, the average time to handle a call will certainly go down!

But it won’t result in a win/win/win!

Users will have to call back 2, 3, 4 or even more times to get their issues adequately resolved. This will glut the hold queues more than if they had had their question answered properly in the first place. Endlessly alternating between raspy music and a message re-assuring the customer that their call is important to company XYZ, will not endear XYZ’s customers to XYZ.

Ultimately, the CSR’s themselves will likely suffer a drop in morale if they begin to view their “job” to get off the phone as quickly as possible rather than to be as helpful as possible. Likely too, sales will begin to decline. As word gets around that the XYZ company has lousy customer service and comparative reviews amplify this effect, sales will decline even more precipitously. 

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There are two approaches executives often take in such a situation. 

Some executives (such as Mister Empathy) may be led to believe that quantification should be less emphasized and the important thing is to set the right tone for the CSR’s; to have them really care about their customers. Often, the approach is combined with better training. This can be a good approach.

Some executives (such as Mister Measure) may be led to believe that they need to do more quantification. In addition to average work time, measures will look at the percentage of users whose problem is solved the first time. Ratings of how effective the CSR was will be taken. Some users might even be called for in-depth interviews about their experience.  This can also be a good approach. 

There is no law against doing both, or trying each approach at different times or different places in order to learn which works better. 

There is a third approach however, which never has good results. That is the approach of Mister Misdirect.

Original drawing by Pierce Morgan



Mister Misdirect’s approach is to deny that there is an issue. Mister Misdirect doesn’t improve training. Mister Misdirect doesn’t put people in a better frame of mind. Mister Misdirect does not add additional measures. Mister Misdirect simply demands that CSR’s continue to drive down the average call time of individual calls and that sales go up! In extreme cases, Mister Misdirect may even fudge the numbers and make it appear that things are much better than they really are. Oh, yes. I have seen this with my own eyes. 

Unfortunately, this way of handling things often makes Mister Misdirect an addict. Once an executive starts down the path of making things worse and denying that they did so, they are easily ensnared in a trap. Initially, they only had to take responsibility for instituting, say an incomplete measure and failed to anticipate the possible consequences. But now, having lied about it, they would have to not only admit that they caused a problem, but also that they lied about it.

The next day, when executive wakes up, they have a choice: 


1. Own up 


OR

2. Continue to deny

If they own up, the consequences will be immediately painful.
If they continue to deny, they will immediately feel relieved. Of course, if they have surrounded themselves with lackeys, they will feel more than simply relieved; they will feel vindicated or even proud. It’s not a “real pride” of course. But it’s some distant relative, I suppose. 

For a developer, UX person — or really any worker in an organization, the lesson from this is to anticipate such situations before they happen. If they happen anyway, try to call attention to the situation as quickly as possible. Yes, it may mean you lose favor with the boss. If that is so, then, you really might want to think about getting a new boss. Mister Misdirect will always ultimately fail and when he does, he will drag down a work team, a group, a division, or even an entire company. Mister Misdirect has one and only one framework for solving problems:

Try whatever pops into consciousness. 

If it works, take the credit. 

If it fails, blame an underling. 

But the real fun begins when he takes credit for something and then it turns out it was really a failure. Then, there is only one choice for Mister Misdirect and that is to claim that the false victory was real. From there on, it is Lose/Lose/Lose.

—————————————————-

  
Author Page on Amazon

————————————

Relevant essays, poems, & fiction about the importance of speaking truth to power:

Pattern Language: “Reality Check”

The Truth Train 

The Pandemic Anti-Academic

How The Nightingale Learned to Sing

Process Re-Engineering Comes to Baseball

——————————————————-

Posts on Problem Framing:

How to Frame Your Own Hamster Wheel

Wordless Perfection

Problem Formulation: Who Knows What?

I Went in Seeking Clarity

I Say Hello

Problem Framing: Good Point

Reframing the Problem: Paperwork & Working Paper

The Doorbell’s Ringing! Can you Get it?

Problem Formulation: Who Knows What?

28 Friday Nov 2025

Posted by petersironwood in AI, creativity, design rationale, psychology, Uncategorized

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AI, browser, HCI, problem formulation, problem framing, problem solving, query, search, seo, technology, thinking, usability, UX

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This post focuses on the importance of discovering who knows what. It’s easy to assume (without thinking!) that everyone knows what you know. 

At IBM Research, around the turn of the century, I was asked to look at improving customer satisfaction about the search function on IBM’s website. Rather than using someone else’s search engine, IBM used one developed at IBM’s Haifa Research lab. It was a very good search engine. Yet, customers were not happy. By way of background, it’s worth noting that compared with many companies who have websites, IBM’s website was meant for a wide variety of users and contained many kinds of information. It was meant to support people buying their first Personal Computer and IT experts at large banks. It had information about a wide variety of hardware, software, and services. The site was designed to serve as an attractor for investors, business partners, and potential employees. In other words, the site was vast and diverse. This made having a good search function particularly important.  

A little study of the existing data which had been collected showed that the mean number of search terms entered by customers was only 1.2. What?? How can that be? Here’s a website with thousands of products and services and designed for use by a huge diversity of users and they were only entering a mean of 1.2 search terms? What were they thinking?!



Of course, there were a handful of situations when one search term might work; e.g., if you wanted to find out everything about a specific product that had a unique one-word name or acronym (which was rare). For most situations though, a more “reasonable” search might be something like: “Open positions IBM Research Austin” or “PC external hard drives” or “LOTUS NOTES training.” 

We invited a sample of users of IBM products & services to come into the lab and do some tasks that we designed to illuminate this issue. In the task, they would need to find specified information on the IBM website while I observed them. One issue became immediately apparent. The search bar on the landing page was far too small. In actuality, users could enter as many search terms as they liked. Their terms would keep scrolling and scrolling until they hit “ENTER.” The developers knew this, but most of our users did not. They assumed they had to “fit” their query into the very small footprint that presented itself visually. Recommendation one was simply to make that space much larger. Once the search bar was expanded to about three times its original size, the number of search terms increased dramatically, as did user satisfaction. 

In this case, the users framed their search problem in terms of: “How can I make the best query that fits into this tiny box.” (I’m not suggesting they said this to themselves consciously, but the visual affordance led them to that self-imposed constraint). The developers thought the users would frame their search problem in terms of: “What’s the best sequence of terms I can put into this virtually infinite window to get the search results I want.” After all, the developers knew that any number of terms could be entered. 

Although increasing the size of the search bar made a big difference, the supposedly good search engine still returned many amazingly bad results. Why? The people at the Haifa lab who had developed the search engine were world class. At some point, I looked at the HTML of some of the web pages. Many web pages had masses of irrelevant metadata. I found some of the people who developed these web pages and discussed things with them. Can you guess what was going on?



Many of the developers of web pages were the same people who had been developing print media for those same products and services. They had no training and no idea about metadata. So, to put up the webpage about product XYZ, they would go to a nice-looking web page about something else, say, training opportunities for ABC. They would copy that entire page, including the metadata, and then set about changing the text about ABC to text about product XYZ. In many cases, they assumed that the strange stuff in angle brackets was some bizarre coding stuff that was necessary for the page to operate properly. They left it untouched. Furthermore, when they “tested” the pages they had created about XYZ, they looked okay. The information about XYZ was there. Problem solved.

Only of course, the problem wasn’t solved. The search engine considered the metadata that described the contents to be even more important than the contents themselves. So, the user would issue a query about XYZ and receive links about ABC because the XYZ page still had the “invisible” metadata about ABC. In this case, many of the website developers thought their problem was to put in good data when what they really needed to do was put in good data and relevant metadata. 

A third issue also revealed itself from watching users. In attempting to do their tasks, many of them suggested that IBM should provide a way for more than one webpage to appear side by side on the screen so that they could, for instance, compare features and functions of two different product models rather than having to copy the information from the web page about a particular model and then compare their notes to the second page. 

Good suggestion. 

Of course, IBM & Microsoft had provided this function. All one had to do was “Right Click” in order to bring up a new window. Remember, these were not naive users. These were people who actually used IBM products. They “knew” how to use the PC and the main applications. Yet, they were still unfamiliar with the use of Right Click. Indeed, allowing on-screen comparisons is one of the handiest uses of Right-Click for many people. 

This issue is indicative of a very pervasive problem. Ironically, it is an outgrowth of good usability! When I began working with computers, almost nothing was intuitive. No-one would even attempt to start programming in FORTRAN or SNOBOL, let alone Assembly Language or Machine Code without looking at the manual. But LOTUS NOTES? A browser? A modern text editor? You can use these without even looking at the manual. That’s a great thing. But — 

…there’s a downside. The downside is that you may have developed procedures that work, but they may be extremely inefficient. You “muddle through” without ever realizing that there’s a much more efficient way to do things. Generally speaking, many users formulate their problem, say, in terms like: “How do I create and edit a document in this editor?” They do not formulate it in terms of: “How do I efficiently create and edit a document in this editor?” The developers know all the splendid features and functions they’ve put into the hardware and software, but the user doesn’t. 

It’s also worth noting that results in HCI/UX are dependent on the context. I would tend to assume that in 2021 (when I first published this post), most PC users knew about right-clicking in a browser even though in 2000, none of the ones I studied seemed to realize it. But —

I could be wrong. 

————————————

The Invisibility Cloak of Habit

Essays on America: Wednesday

Index to a catalog of “best practices” in teamwork & collaboration. 

Author Page on Amazon

What about the butter dish?

Labelism

The Stopping Rule

The Update Problem

Turing’s Nightmares: Eight

21 Friday Nov 2025

Posted by petersironwood in psychology, The Singularity, Uncategorized

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AI, Artificial Intelligence, cognitive computing, collaboration, cooperation, openai, peace, philosophy, seva, teamwork, technology, the singularity, Turing, ubuntu, United Peoples Ecosystem

OLYMPUS DIGITAL CAMERA

Workshop on Human Computer Interaction for International Development

In chapter 8 of Turing’s Nightmares, I portray a quite different path to ultra-intelligence. In this scenario, people have begun to concentrate their energy, not on building a purely artificial intelligence; rather they have explored the science of large scale collaboration. In this way, referred to by Doug Engelbart among others as Intelligence Augmentation, the “super-intelligence” comes from people connecting.

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It could be argued, that, in real life, we have already achieved the singularity. The human race has been pursuing “The Singularity” ever since we began to communicate with language. Once our common genetic heritage reached a certain point, our cultural evolution has far out-stripped our genetic evolution. The cleverest, most brilliant person ever born would still not be able to learn much in their own lifetime compared with what they can learn from parents, siblings, family, school, society, reading and so on.

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One problem with our historical approach to communication is that it evolved for many years among a small group of people who shared goals and experiences. Each small group constituted an “in-group” but relations with other groups posed more problems. The genetic evidence, however, has become clear that even very long ago, humans not only met but mated with other varieties of humans proving that some communication is possible even among very different tribes and cultures.

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More recently, we humans started traveling long distances and trading goods, services, and ideas with other cultures. For example, the brilliance of Archimedes notwithstanding, the idea of “zero” was imported into European culture from Arab culture. The Rosetta Stone illustrates that even thousands of years ago, people began to see the advantages of being able to translate among languages. In fact, modern English contains phrases even today that illustrate that the Norman conquerers found it useful to communicate with the conquered. For example, the phrase, “last will and testament” was traditionally used in law because it contains both the word “will” with Germanic/Saxon origins and the word “testament” which has origins in Latin. Many other traditional legal terms in English have similar bilingual origins.

Automatic translation across languages has made great strides. Although not so accurate as human translation, it has reached the point where the essence of many straightforward communications can be usefully carried out by machine. The advent of the Internet, the web, and, more recently google has certainly enhanced human-human communication. It is worth noting that the tremendous value of google arises only a little through having an excellent search engine but much more though the billions of transactions of other human beings. People are exploring and using MOOCs, on-line gaming, e-mail and many other important electronically mediated tools.

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Equally importantly, we are learning more and more about how to collaborate effectively both remotely and face to face, both synchronously and asynchronously. Others continue to improve existing interfaces to computing resources and inventing others. Current research topics include how to communicate more effectively across cultural divides; how to have more coherent conversations when there are important differences in viewpoint or political orientation. All of these suggest that as an alternative or at least an adjunct to making purely separate AI systems smarter, we can also use AI to help people communicate more effectively with each other and at scale. Some of the many investigators in these areas include Wendy Kellogg, Loren Terveen, Joe Konstan, Travis Kriplean, Sherry Turkle, Kate Starbird, Scott Robertson, Eunice Sari, Amy Bruckman, Judy Olson, and Gary Olson. There are several important conferences in the area including European Conference on Computer Supported Cooperative Work, and Conference on Computer Supported Cooperative Work, and Communities and Technology. It does not seem at all far-fetched that we can collectively learn, in the next few decades how to take international collaboration to the next level and from there, we may well have reached “The Singularity.”

Photo by Patrick Case on Pexels.com

————————————-

For further reading, see: Thomas, J. (2015). Chaos, Culture, Conflict and Creativity: Toward a Maturity Model for HCI4D. Invited keynote @ASEAN Symposium, Seoul, South Korea, April 19, 2015.

Thomas, J. C. (2012). Patterns for emergent global intelligence. In Creativity and Rationale: Enhancing Human Experience By Design J. Carroll (Ed.), New York: Springer.

Thomas, J. C., Kellogg, W.A., and Erickson, T. (2001). The Knowledge Management puzzle: Human and social factors in knowledge management. IBM Systems Journal, 40(4), 863-884.

Thomas, J. C. (2001). An HCI Agenda for the Next Millennium: Emergent Global Intelligence. In R. Earnshaw, R. Guedj, A. van Dam, and J. Vince (Eds.), Frontiers of human-centered computing, online communities, and virtual environments. London: Springer-Verlag.

Thomas, J.C. (2016). Turing’s Nightmares. Available on Amazon. http://tinyurl.com/hz6dg2

An Inside View of IBMs Innovation Jam

————-

Author Page on Amazon

Turing’s Nightmares: The Road Not Taken

Pattern Language for Collaboration and Cooperation

The First Ring of Empathy

The Dance of Billions

Imagine All the People…

Roar, Ocean, Roar

Corn on the Cob

Take a Glance; Join the Dance

The Self-Made Man

Indian Wells

Turing’s Nightmares: Seven

20 Thursday Nov 2025

Posted by petersironwood in The Singularity, Uncategorized

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AI, Artificial Intelligence, chatgpt, cognitive computing, competition, cooperation, ethics, philosophy, technology, the singularity, Turing

Axes to Grind.

finalpanel1

Why the obsession with building a smarter machine? Of course, there are particular areas where being “smarter” really means being able to come up with more efficient solutions. Better logistics means you can deliver items to more people more quickly with fewer mistakes and with a lower carbon footprint. That seems good. Building a better Chess player or a better Go player might have small practical benefit, but it provides a nice objective benchmark for developing methods that are useful in other domains as well. But is smarter the only goal of artificial intelligence?

What would or could it mean to build a more “ethical” machine? Can a machine even have ethics? What about building a nicer machine or a wiser machine or a more enlightened one? These are all related concepts but somewhat different. A wiser machine, to take one example, might be a system that not only solves problems that are given to it more quickly. It might also mean that it looks for different ways to formulate the problem; it looks for the “question behind the question” or even looks for problems. Problem formulation and problem finding are two essential skills that are seldom even taught in schools for humans. What about the prospect of machines that do this? If its intelligence is very different from ours, it may seek out, formulate, and solve problems that are hard for us to fathom.

For example, outside my window is a hummingbird who appears to be searching the stone pine for something. It is completely unclear to me what he is searching for. There are plenty of flowers that the hummingbirds like and many are in bloom right now. Surely they have no trouble finding these. Recall that a hummingbird has an incredibly fast metabolism and needs to spend a lot of energy finding food. Yet, this one spent five minutes unsuccessfully scanning the stone pine for … ? Dead straw to build a nest? A mate? A place to hide? A very wise machine with freedom to choose problems may well pick problems to solve for which we cannot divine the motivation. Then what?

In this chapter, one of the major programmers decides to “insure” that the AI system has the motivation and means to protect itself. Protection. Isn’t this the major and main rationalization for most of the evil and aggression in the world? Perhaps a super intelligent machine would be able to manipulate us into making sure it was protected. It might not need violence. On the other hand, from the machine’s perspective, it might be a lot simpler to use violence and move on to more important items on its agenda.

This chapter also raises issues about the relationship between intelligence and ethics. Are intelligent people, even on average, more ethical? Intelligence certainly allows people to make more elaborate rationalizations for their unethical behavior. But does it correlate with good or evil? Lack of intelligence or education may sometimes lead people to do harmful things unknowingly. But lots of intelligence and education may sometimes lead people to do harmful things knowingly — but with an excellent rationalization. Is that better?

Even highly intelligent people may yet have significant blind spots and errors in logic. Would we expect that highly intelligent machines would have no blind spots or errors? In the scenario in chapter seven, the presumably intelligent John makes two egregious and overt errors in logic. First, he says that if we don’t know how to do something, it’s a meaningless goal. Second, he claims (essentially) that if empathy is not sufficient for ethical behavior, then it cannot be part of ethical behavior. Both are logically flawed positions. But the third and most telling “error” John is making is implicit — that he is not trying to dialogue with Don to solve some thorny problems. Rather, he is using his “intelligence” to try to win the argument. John already has his mind made up that intelligence is the ultimate goal and he has no intention of jointly revisiting this goal with his colleague. Because, at least in the US, we live in a hyper-competitive society where even dancing and cooking and dating have been turned into competitive sports, most people use their intelligence to win better, not to cooperate better. 

The golden sunrise glows through delicate leaves covered with dew drops.

If humanity can learn to cooperate better, perhaps with the help of intelligent computer agents, we can probably solve most of the most pressing problems we have even without super-intelligent machines. Will this happen? I don’t know. Could this happen? Yes. Unfortunately, Roger is not on board with that program toward better cooperation and in this scenario, he has apparently ensured the AI’s capacity for “self-preservation through violent action” without consulting his colleagues ahead of time. We can speculate that he was afraid that they might try to prevent him from doing so either by talking him out of it or appealing to a higher authority. But Roger imagined he “knew better” and only told them when it was a fait accompli. So it goes.

———–

Turing’s Nightmares

Author Page

Welcome Singularity

Destroying Natural Intelligence

Come Back to the Light Side

The First Ring of Empathy

Pattern Language Summary

Tools of Thought

The Dance of Billions

Roar, Ocean, Roar

Imagine All the People

Essays on America: The Game

Wednesdays

What about the Butter Dish?

Where does your Loyalty Lie?

Labelism

My Cousin Bobby

The Loud Defense of Untenable Positions

Turing’s Nightmares: Chapter Five

17 Monday Nov 2025

Posted by petersironwood in The Singularity, Uncategorized

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AI, Artificial Intelligence, chatgpt, cognitive computing, health, medicine, Personal Assistant, philosophy, technology, the singularity, Turing

runtriathalon

An Ounce of Prevention: Chapter 5 of Turing’s Nightmares

Hopefully, readers will realize that I am not against artificial intelligence (after all, I ran an AI lab for a dozen years); nor do I think the outcomes of increased artificial intelligence are all bad. Indeed, medicine offers a large domain where better artificial intelligence is likely to help us stay healthier longer. IBM’s Watson had already begun “digesting” the vast and ever-growing medical literature more than a decade ago. As investigators discover more and more about what causes health and disease, we will also need to keep track of more and more variables about an individual in order to provide optimal care. But more data points also means it will become harder for a time-pressed doctor or nurse to note and remember every potentially relevant detail about a patient. Certainly, personal assistants can help medical personnel avoid bad drug interactions, keep track of history, and “perceive” trends and relationships in complex data more quickly than people are likely to. In addition, in the not too distant future, we can imagine AI programs finding complex relationships and “invent” potential treatments.

Not only medicine, but health provides a number of opportunities for technology to help. People often find it tricky to “force themselves” to follow the rules of health that they know to be good such as getting enough exercise. Fit Bit, Activity Tracker, LoseIt and similar IT apps help track people’s habits and for many, this really helps them stay fit. As computers become more aware of more and more of our personal history, they can potentially find more personalized ways to motivate us to do what is in our own best interest.

In Chapter 5 of Turing’s Nightmares, we find that Jack’s own daughter, Sally is unable to persuade Jack to see a doctor. The family’s PA (personal assistant), however, succeeds. It does this by using personal information about Jack’s history in order to engage him emotionally, not just intellectually. We have to assume that the personal assistant has either inferred or knows from first principles that Jack loves his daughter and the PA also uses that fact to help persuade Jack.

It is worth noting that the PA in this scenario is not at all arrogant. Quite the contrary, the PA acts the part of a servant and professes to still have a lot to learn about human behavior. I am reminded of Adam’s “servant” Lee in John Steinbeck’s East of Eden. Lee uses his position as “servant” to do what is best for the household. It’s fairly clear to the reader that, in many ways, Lee is in charge though it may not be obvious to Adam.

In some ways, having an AI system that is neither “clueless” as most systems are today nor “arrogant” as we might imagine a super-intelligent system to be (and as the systems in chapters 2 and 3 were), but instead feigning deference and ignorance in order to manipulate people could be the scariest stance for such a system to take. We humans do not like being “manipulated” by others, even when it for our own “good.” How would we feel about a deferential personal assistant who “tricks us” into doing things for our own benefit? What if they could keep us from over-eating, eating candy, smoking cigarettes, etc.? Would we be happy to have such a good “friend” or would we instead attempt to misdirect it, destroy it, or ignore it? Maybe we would be happier with just having something that presented the “facts” to us in a neutral way so that we would be free to make our own good (or bad) decision. Or would we prefer a PA to “keep us on track” even while pretending that we are in charge?


Author Page

Welcome, Singularity

Destroying Natural Intelligence

E-Fishiness comes to Mass General Hospital

There’s a Pill for That

Essays on America: The Game

The Self-Made Man

Travels with Sadie

The Walkabout Diaries

The First Ring of Empathy

Donnie Gets a Hamster

Plans for US; some GRUesome

Imagine All the People

Roar, Ocean, Roar

The Dance of Billions

Math Class: Who are you?

Family Matters: Part One

Family Matters: Part Two

Family Matters: Part Three

Family Matters: Part Four

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