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Category Archives: design rationale

Design – Interpretation Model of Communication

16 Thursday Jul 2026

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

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communication, deception, experiment, HCI, IBM, life, media, psychology, relationships, truth, UX, writing

In my early days at IBM Research (1970’s), we were focused on trying to develop, test, or at least conceive of ways that a larger proportion of people would be able to use computers. One of the major ways of thinking about this was to use natural language communication as a model. After all, it was reasoned, people were able to communicate with each other using natural language. This meant that it was possible, at least in principle. Moreover, most people had considerable practice communicating using natural language. 

One popular way of looking at natural language (especially among engineers & computer scientists) was essentially an “Encoding – Decoding” model. I have something in my head that I wish to communicate to you. So, I “encode” my mental model, procedure, fact, etc. into language. I transmit that language to you. Then, you “decode” what I said into your internal language and — voila! — if all goes well, you construct something in your head that is much like what is in my head. Problem solved. 

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Of course, people who wrote about communication from this standpoint acknowledged that it didn’t always work. For instance, as speaker, I might do a bad job of “encoding” my knowledge. Or, I might do a good job of encoding, but the “transmission” was bad; e.g., static, gaps, noise, etc. might distort the signal. And, you might do a bad job of decoding. It’s an appealing model and helped engineers and computer scientists make advances in “communication theory” and helped make practical improvements in coding and so on.

As a general theory of how humans communicate, however, that notion is vastly over-simplified. I argued then that a better way of looking at human communication was as a design-interpretation process, not as an encoding-decoding process. One of the examples that pointed this out was a simple observation by Don Norman. Suppose someone comes up to you and asks, “Where is the Empire State Building?” You will normally give a quite different answer depending on whether the two of you are in Rome, Long Island, or Manhattan. In Rome, you might say, “It’s in America.” Or, you might say, “It’s in New York City.” If you are on Long Island, you might well say, “It’s in Manhattan.” If you are already in Manhattan, you might say, “Fifth Avenue, between 33rd and 34th.” 

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Building on Don Norman’s original example, but based on your own experience, you can easily see that it isn’t only the geographical relationships that influence your answer. If you were originally from Boston, now on your own in Rome, struggling with Italian and homesick and someone came up to you and asked that question in American English with a Boston Accent, your response might be: “Are you joking? But how did you know I was an American. My name’s … “

On the other hand, if you’re a 13-year old boy in Manhattan — one with a mean streak — and someone asks you this question in broken English and they’re looking around like they are totally lost, you might say, “Oh, no problem. Just follow 8th Avenue, all the way north up to 133rd. It’s right there. You can’t miss it.” (Note to potential foreign visitors, most kids in Manhattan would not intentionally mislead you. But they point is, someone could. They are not engaging some automatic encoding process that takes their knowledge and translates into English. Absurd! 

You design every communication. I think that’s a much more useful way to conceive of communicating. Yes, of course, there are occasions when your “design” behavior is extremely rudimentary and seems almost automatic. It isn’t though. It just seems that way. Let’s go back to our question-asking example. Suppose you work at an information booth in New York City. People ask you this same question day after day, year after year. You’re seemingly giving the answer without any attention whatsoever. Suppose someone asks you the question, but with a preface. “Look here, chap! I’ve got a gun! And if you give me the same stupid answer you’ve given me every time before, I’ll shoot your bloody brains out!” You are going to modify your answer. It only seemed as though it was automatic.

When you design your answer you take into account at least these things: some knowledge that you communication about, the current context (which itself has hundreds of potentially important variables), a model of the person you’re creating this communication for, a set of goals that you are trying to achieve (e.g., get them safely to their goal, mislead them, entertain them, entertain yourself, entertain the people around you, demonstrate your expertise, practice your diction, etc.). The process is inherently creative. In many circumstances (writing, playing, exploring, discovering, partying), you can choose how creative you want to make it. In other cases, circumstances constrain you more (though likely not so much as you think they do). 

Many readers think this is a classic example of a straw man argument. “No-one believes communication is a coding-decoding process.” 

Well, I beg to differ. I worked for relatively well-managed companies. I’ve talked to many other people who have worked in different well-managed companies. We’ve all seen or heard requests like this: “I need a paragraph (or a slide or a foil) on speech recognition. Thanks.” 

What??

Who’s the audience? Are they scientists, investors, customers, our management? How much do they already know? What are your goals? What other things are you going to talk about with them? The people who have left me such messages were all smart people. And, providing the necessary info would have only taken a minute or two. But it would have substantially improved the outcome. It’s not a straw man argument. 

Sit-com plots often hinge on the characters doing poorly at designing and/or interpreting communications. A show based on encoding-decoding? No. What could be funny — indeed what often is shown in comedy — are people failing to do good design and in the extreme case, that can arise by having an actual robot as a character or someone who behaves like one.

People also interpret what was said in terms of their goals, the context, what they believe about your goals and capacity, what they already know, and so on. And, even though this may seem obvious, millions of people believe what advertisers or politicians say without questioning their motives, double-checking with other sources, or even looking for internal inconsistencies in what is being touted as true. In other cases though, the same people will not believe anything the “other side” says no matter what. Just as one can do faulty design, one can also do faulty interpretation. 

In any case, I decided that it would be good to “show” in a controlled laboratory setting that the Encoding-Decoding model was woefully inadequate. So, I brought in “subjects” to work in pairs at a simple task about communicating Venn diagram relationships. The “designer” had a Venn diagram in front of them. “The “interpreter” was supposed to draw a Venn diagram. The “designer” was constrained to say something true and relevant. In addition to a “base” pay, the “interpreter” subjects would be given a bonus according to how many relationships matched those of the “designer.” The designer’s bonus depended on condition. In the “cooperation” condition, their payoff would also, like the interpreter’s, be determined by how much agreement was shown in the two diagrams. In the “competition” condition, the designer’s bonus depended on how different the two diagrams were. 

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I ran about half the number of subjects I had planned to run when the experiment was ended by corporate lawyers. 

What? 

IBM had no unions at that time. And, they didn’t want any unions. One of their policies, which they believed would help them prevent the formation of unions was that they never paid their workers for piece-work. Apparently, somehow, IBM CHQ had gotten wind of my experiment. People were being paid different amounts, based (partly) on their performance. They couldn’t have this! People might think were paying people for piece-work! 

It hardly needs to said, I suppose, that IBM definitely tried to pay for performance. This was true in sales, research, development, HR, management, and so on. No-one in IBM would argue that your pay shouldn’t be related to your performance. That was exactly — in one way of describing it — was going on here. By the way, these were not IBM employees and each subject only “worked” for about an hour.

Basically, regardless of how irrelevant this experimental set-up might have been to the genuine concern of unions not to pay people in an insanely aggressive and ever-changing piece-work scheme, the lawyers were concerned that it would be somehow misrepresented to workers or in the press and used as evidence that IBM should unionize. In a way, the lawyers were proving the point of the experiment in their own real-life behavior even as they insisted that the experiment needed to be shut down.



Lessons Learned: #1 Corporate lawyers are not only concerned about what you actually do or how you represent your work; they are also worried about how someone might misrepresent your work. 

Lessons Learned: #2 Even when constrained to say something true and relevant, ordinary people are quite capable of misleading someone else when it’s to their benefit and considered okay to do.

It is this second aspect of the experiment that I myself felt to be “edgy” at the time. Sure, people can mislead, but I was providing a context in which they were being encouraged to mislead. Was that ethical? Obviously, I thought it was at the time. On reflection, I still think it’s okay, but I’m glad that there are now review boards to look at “studies” and give a less biased opinion than the person who designed the study would do.

I view the overall context of doing the study as positive. As adults, these people all already knew how to mislead. I was letting them, and many other people, know that we know you know how to mislead and we’ll be on the lookout for it. 

What do other people think about studies wherein the experimenter encourages one person to deceive another? 

2026 Update: I’ve spend a fair amount of time recently “chatting” with ChatGPT and with Claude. It’s clear that the implementers of these systems (or, at least some of them) were quite familiar with the idea of designing and interpreting text. Claude, in particular, seems like a “nice guy” and an honest one at that. It acknowledges some of its own limitations and often praises the questioner. This shouldn’t come as a complete shock. After all, good sales people and advertisers have relied for centuries on “Design and Interpretation.” Nonetheless, I sometimes wonder whether AI systems might be more ethically employed if they were operating on principles closer to “Coding and Decoding.” What do you think?

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References published literature that describes some of the research that was done around that time. 

Malhotra, A., Thomas, J.C. and Miller, L. (1980). Cognitive processes in design. International Journal of Man-Machine Studies, 12, pp. 119-140.

Carroll, J., Thomas, J.C. and Malhotra, A. (1980). Presentation and representation in design problem solving. British Journal of Psychology/,71 (1), pp. 143-155.

Carroll, J., Thomas, J.C. and Malhotra, A. (1979). A clinical-experimental analysis of design problem solving. Design Studies, 1 (2), pp. 84-92.

Thomas, J.C. (1978). A design-interpretation analysis of natural English. International Journal of Man-Machine Studies, 10, pp. 651-668.

Thomas, J.C. and Carroll, J. (1978). The psychological study of design. Design Studies, 1 (1), pp. 5-11. 

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Other essays that touch on communication. 

Freedom of Speech is not a License to Kill

Ohayogozaimasu

The Sound of One Hand Clasping

Fool Me

Claude the Radioman

Know What? 

The Story of Story, Part 1

The Temperature Gauge

The Destruction of Natural Intelligence

A Little is not a Lot

Try the Truth

Stoned Soup

Turing’s Nightmares

“Wizard of Oz”

15 Wednesday Jul 2026

Posted by petersironwood in AI, apocalypse, design rationale, HCI, leadership, management, politics, psychology, The Singularity, Uncategorized, user experience

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AI, HCI, IBM, research, technology, usability, UX, Wizard of Oz, writing

(Some Lessons Learned from studies in Human-Computer Interaction/User Experience conducted at IBM Research in the mid-70’s.)

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Wizard of Oz 

One of the studies I conducted at IBM Research in the mid 1970’s was part of an effort to do “Automatic Programming” — a department under Pat Goldberg. The first level manager I worked with was Irving Wladawsky (later Irving Wladawsky-Berger). His group wanted to develop a system that would allow the owner/operator of a small business to type requirements into a computer in English (or something English-like) and have the system itself produce RPG code to run the business so described. 

The underlying motivation from an IBM business perspective was that many small businesses could well afford a computer to do inventory, fulfill orders, etc. but they couldn’t afford to hire programmers to create such a system from scratch. The small business owner in the mid-1970’s did not program! Yet, for the most part, they understood how their business worked. The notion was that a natural language understanding and generation program could dialogue with the user/owner and through that process, understand their “business rules.” No costly programmers needed!

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An interesting side note: at that time, we were told that IBM corporate forbade us to use the terms “Artificial Intelligence” or “Robotics” to describe our work because some PR firm had determined that these terms were too scary for the general public. So, IBM had research in “mechanical assembly” but not “robotics.” We had work in character recognition, speech recognition, handwriting recognition, automatic program generation, and compiler optimization. But no work in “Artificial Intelligence.” (Wink, wink, nod, nod). 

Labelism: Confusing a thing with the label for that thing.

Another interesting side note: I worked at IBM Research for a dozen years; started an AI lab at NYNEX where I worked another 13 years; came back to IBM Research and several years later found myself working on the same problem! We were still trying to make a system to allow small businesses to generate their code automatically. In my second iteration, rather than using natural language, we were trying to make the specification of business rules in a graph language that was intuitive enough for business owners. This was a different approach, but trying to address the same underlying desire: to bring computing to small business without incurring the heavy costs of programming and maintenance. 

Let’s return to iteration one — the natural language approach @ 1975. Well, one issue was that no-one had a natural language program that even approximated being able to do the job. So…how to study people’s interaction with a system that doesn’t exist? 

We used an approach that my colleague Jeff Kelly called the “Wizard of Oz” technique; viz., use a human being (in this case, me) to simulate how the system might work and record people’s behavior. In this way, we could discover many of the issues that such a natural language programming system would have to deal with. I had already had plenty of experience interacting with a computer; and I had acting experience. I could “play the part” of a computer fairly well as I typed in my questions and answers. 

(Description of “The Wizard of Oz” technique).

IBM Research in Yorktown had roughly a thousand people including not only scientists, programmers, and engineers but also a number of business people (who did not know how to program). I knew some of them from playing tennis and table tennis and we used those folks as initial subjects. What did I find? Good news and bad news. 

Dealing with natural language is tricky for many reasons. One of those reasons is that English, including the English that people normally use to describe their business, is filled with words that have multiple meanings; e.g., “file”, “run”, “program”, “object”, “table”, etc. But here is the good news: although it’s true that many English words have many meanings, when these business people described business procedures, almost all of the lexical ambiguity vanished! The program to understand business English would not have to distinguish between a business file and a nail file; it wouldn’t have to worry about distinguishing a run in baseball or a run in stockings from a run of the payroll program; it wouldn’t have to distinguish between the table in a relational data base and the table in your dining room. The domain would mainly constrain! That’s the good news.

The bad news was dialogue management. How can the machine recognize a misunderstanding and how can it correct it? To make matters worse, while business people were fairly consistent in the way they described how their business ran, they were not consistent in how they talked about the communication. If a human being senses that another one is misunderstanding, then, depending on context they might: raise their eyebrows, say “Huh?”, “Come again?”, “What?” “I think I lost you.” “WTF?” “Are you kidding?”, “We’re on different wavelengths,” “I don’t get it.” “But…wait.” 

Photo by Nafis Abman on Pexels.com

Sometimes, these are referred to as “meta-comments.” Here’s a simple example that took place in the study. 

One of the business people told me about various discounts. I had assumed (playing the part of the computer) that he was talking about discounts for items that were being discounted due to inventory management. I recorded all the various percentages and so on. Then, he said, “Now, we also give discounts for various items.” 

At that time, most natural language systems of that era simply ignored words like “now” and “also” in this context. Stepping out of my role as a “computer system” and thinking about from the perspective of a human conversational partner though, these words are crucial! What it signals is a change in topic. In the larger context of our conversation, it shows that everything that had just been said, which I thought had been about item discounts, was not about item discounts!

This is just one example, but there were many more. In my more recent experience interacting with various computer dialogue systems, being able to recognize the signals of miscommunication and being able to repair misunderstandings is still not very well-handled more than four decades later.

I’d be interested in any pointers you have to a system that you think deals with meta-communication in a natural and robust manner. I do not think that it is beyond the pale of possibility. The general categories of the ways that people misunderstand each other is not infinite. John Anderson developed excellent tutoring systems for LISP and geometry and those systems worked something like human tutors in that, the tutor inferred the mental model of an individual student and focused instruction on correcting any misconceptions. My intuition is that a generic system built with equal complexity could deal with most of the issues as well as the average human being deals with them; i.e., imperfectly. 

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Lessons Learned: #1 You can test aspects of a system even before it’s built or even completely defined. One method that has been used many times: “Wizard of Oz.” 

Lessons Learned #2: Language used by professionals to talk about their domain is much more constrained in terms of lexical ambiguity than is language when considered by all native speakers.

Lessons Learned #3: People in “our culture” (i.e., US business culture) do not have an agreed upon and consistent vocabulary for talking about communication nor a consistent process for dealing with them.

Lessons Learned #4: Speaking of communication errors, I don’t recall why, but it was about this time, that I realized that my notion about how research results would be transferred to other parts of IBM was a complete and utter fantasy. I hadn’t articulated it, but it was basically that I would do research, write the results up for publication in scientific journals for an academic audience and publish Research Reports which would be eagerly consumed by anyone who needed to know. I’m not proud of this. LOL. But that’s really kind of how I viewed it. And, then, after a few years, I realized that it really mainly came about through relationships. That was something that people had been showing me all my life, but which I don’t think anyone ever stated it explicitly enough.

Update for 2026: While the four “Lessons Learned” above still seem apropos, some of today’s chatbots such as ChatGPT and Claude are much better at dealing with connected conversations with humans that were the systems of the twentieth century. Not only that, people are now describing systems in “natural language” and the computer is writing code. That’s the “good news” I suppose.

The bad news? While my impression of most of the AI researchers of the twentieth century is that they were largely motivated by intellectual challenges and a desire to help make people more productive, I get the impression that most of the AI work of today is controlled by people who are already astoundingly wealthy and want to become even wealthier. And, as UN-laudable as we may view that goal, the wealth is only a means to an end and that end is the total enslavement of most of humanity. Even being a trillionaire isn’t enough.

That’s not to say that all the workers advancing AI have those nefarious aims just because those in charge do, but as the surveillance state becomes more pervasive, their private motivations could have less influence over the outcomes.

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Author Page on Amazon

The Myths of the Veritas (an exploration of leadership & ethics in free, no ads fiction)

Index to a Pattern Language for Collaboration and Teamwork

Experiences in Human-Computer Interaction

Post on “The Story of Story” 

The After Times

After All

When Greed is the only Creed

Destroying Natural Intelligence

“Turing’s Nightmares” comprises 23 Sci-Fi short stories that explore the implications and ethics of AI

Here is an example chapter from “Turing’s Nightmares” that explores a possible dilemma of working in AI.

To Be or Not To Be

The Story of Story 4: Character

19 Monday Jan 2026

Posted by petersironwood in design rationale, fiction, story, Uncategorized

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AI, creative-writing, fiction, life, politics, story, Storytelling, truth, user experience, writing, writing-tips

The Story of Story 4: Character

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Character is revealed by choices under pressure. Character is one of the three main dimensions of story. Often people who write fiction — or developers who write “user stories” add details about the people in an effort to make their characters (or personas) more “interesting.” Adding irrelevant details in something as long as a novel might help the reader get a clearer image of the character. Even in a long novel though, it’s better to add details that relate to something else in the story. In something as short and “to the point” as a “user story” it is worse than pointless. 

Consider these descriptive details: 

“Jill had beautiful blue eyes.” 

“Jill had beautiful brown eyes.” 

“Jill had beautiful green eyes.” 

So what? 

It might be relevant to some stories. For example, if Jill were a slave on an antebellum plantation, her having blue eyes might relate to her mother being raped by a white overseer. Maybe Jill finds out and exacts revenge. In that case, her blue eyes might be meaningful. Or, in another story, Jack might insist on dating only blue-eyed blonds. That is part of his “ideal beauty.” Jack pursues Jill because of her striking blue eyes. He shares information all the time about his “conquests” with his best friend, Judy, a woman with black hair and dark eyes. If it’s a romantic comedy, we will know, long before Jack will, that he is falling in love with Judy. The physical characteristics of the women serve to reveal Jack’s true character, which turns out to be deeper than we at first surmised. 

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But suppose the story is about how someone might use an Uber app? Is it really going to matter what color her eyes are? Will it matter to someone playing a video game? 

Irrelevant details only seek to distract the reader (or the developer). These details sometimes go by the title “characterization” rather than character. Character should be reserved for deeper things. Sometimes, characterization can be interesting in the way it contrasts with character. In Psych for instance, Sean Spencer pretends every week to be a psychic helping the Santa Barbara police. His aim is to get to the truth. But in the service of getting to the truth, and putting the bad folks in jail, he runs a scam where he pretends to be psychic. 

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In the James Bond movies, the character of James Bond is revealed by his choices under pressure. He will give up everything and anything in the service of his country. But on the surface, he seems like a playboy. He drinks martinis. Yet, he is highly disciplined. He wants things his way. Even in his instructions for his martini, his meticulous attention to detail comes out. 

Spock, on Star Trek, plays a character who reminds us time and again about how “rational” he is and how he can control his emotions. Of course, what makes this interesting is precisely because he isn’t always rational and in fact, sometimes has more violent emotions than the humans he critiques for their emotionality. 

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If “character is revealed by choices under pressure,” it’s also good to remember that character should be coherently related to setting and plot. Plot advances through conflicts. In The Sound of Music, for instance, Maria has an internal conflict. She wants to be “good” and “follow the rules” of the convent (and later those of the Captain’s household), but she likes joy and music and spontaneity. She also finds herself in love with the Captain. Conflict. She also has inter-personal conflicts with the authorities at the convent, with the children, with the Captain, and with the Countess. She also has conflicts with larger forces in the world – notably Nazism. None of these conflicts is random; they arise quite naturally from the setting that she’s in — and from her own character. 

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James Frey, in How to Write a Damned Good Novel, suggests a sequence of increasingly intimate reveals  that helps the reader progressively care more and more about the character. First, you say something about the objective, external world that the character exists in. Second, you reveal what the character perceives and does about the situation. Then, you reveal how the character feels about what is happening. Finally, you let the reader “tune in” to the internal conflicts of the character by showing their internal dialogue. Consider: 

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“The snow began to fall. The wind began to howl. The “snow” morphed into sharp little knives of ice.”

“Joe began to shiver and pulled his coat tight about him, crossing his arms across his chest.” 

“Damn it! I want to be in a nice warm bed. Grrr.”

“Why do I always let Sally talk me into these half-baked schemes?” 

For me, this order “works” – I am now curious to see what this particular half-baked scheme is and what sort of power Sally has over Joe. Read the lines in the reverse order and it makes only a little sense. It also puts a greater memory load on the reader. 

In some stories, character stays fairly constant and the world (and other people) change because of the character’s choices. In the “Hero Saves the World” plot, this is the main emphasis. In the “Growing Up” plot, on the other hand, the most important action is how the character “changes” over time. I put “changes” in quotes because sometimes the “change” is really that the character simply acknowledges their underlying character. For instance, in Sweet Home Alabama, Melanie never really stops being in love with her husband (or Alabama) but consciously, she claims to want a divorce and go back to NY to be a “success.” As always, character is revealed under pressure <spoiler alert> and she “forgets” to sign the divorce papers. In many of the best stories, the character changes (or saves) the world and the world also changes or matures the character. </spoiler alert>.

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This may all make sense when it applies to fiction, but how does it impact how we write stories in a business context? This is often tricky because in many business contexts, only the founder or CEO is even allowed to have character. Everyone else is basically supposed to behave the same way: put the company first; follow the rules; do a great job; work together cooperatively; be loyal to the company. As a result, official company stories are typically bland and two-dimensional. They are basically nothing more than procedures. “If this happens, do that.” Implicitly, this means, “If this happens, do that” regardless of your internal character. 

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If you’ve done an excellent job of observing and interviewing potential users of a product or service, you have hopefully discovered some interesting internal conflicts and some related aspects of character that can become a logical part of user journeys. Initially, your target user may be reluctant to use your product. 

Users may be reluctant to use on-line banking, for instance, because of the possibility of hacking or fraud. If this is a genuine concern of 1% of your potential customers, you probably don’t want to make it a concern to the rest, unless it is something they really should worry about. On the other hand, if it’s a genuine concern of 99% of your potential customers, sweeping it under the rug won’t do. The user in your user stories can be portrayed with this concern including internal conflicts and then you can show them overcoming the concern, if and only if it really can be ameliorated through various actions like two-factor verification, password choices, etc. Telling a lie about how safe on-line banking is, will ultimately undo you no matter how well told the story is. But character and characterization of these users should be designed around conflicts that actually are relevant to the product or service. 

“Mary had put all her life savings and all her energy into her small company. Her time had become gold. She was on a path to hire more people, but that took time. Now the bank was offering lower fees if she would switch to on-line banking. She had always wanted to be a soccer player but she knew she wasn’t coordinated enough.” 

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What? 

Yes, that may be something that came out in an interview with a real Mary. And it may even be part of an interesting story. But not this story! The naturally occurring conflict here is Mary’s desire to be as efficient and cost-effective as possible — and yet also to be as safe as possible. Mary may initially see these in conflict, but you may have a legitimate way for her to avoid or rethink the conflict. Mary’s character might be made more intense by having her see her budding business as a legacy she wants eventually to hand off to her daughters. But it doesn’t really matter whether she has blue eyes or brown eyes. You could instead intensify Mary’s desires by making her a success-oriented second generation immigrant whose own parents spent countless hours of hard work so she could get through college. The family still cares about every dollar. It doesn’t matter whether she lives in a small flat in Brooklyn, Chicago, or LA. It does matter that she wasn’t gifted 10 million dollars to start a business by her billionaire parents who live in a mansion in Manhattan. 

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It doesn’t matter whether she likes her martinis shaken or stirred either, unless you are making the point, e.g., that she is a fanatic for having things her way and that your software allows more customization than does that of your competitors. In that case, you can introduce a detail that shows, rather than tells, this fact about her character.

When you think back about books, movies, or TV series you really “got into”, I’m willing to bet that, at least in many cases, it’s partly because of the characters. What makes a great character for you? 

 

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Author Page on Amazon.

If Only

It was in his Nature

The Orange Man

The Mango Mussolini

At Least he’s our Monster

The Impossible 

True Believer

Coelacanth

A Cat’s a Cat

Sadie the Sifter

The Con-Con Man’s Special Friend

A Query on Quislings

As Gold as it Gets

Stoned Soup

How the Nightingale Learned to Sing

What a teeny man

 Donnie Boy Plays Captain Man

Donnie Boy Lets his Brother Take the Fall 

Tools of Thought

14 Sunday Dec 2025

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

≈ 6 Comments

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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

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

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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!” 

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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. 

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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

Reframing the Problem: Paperwork & Working Paper

04 Thursday Dec 2025

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

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AI, ethics, leadership, life, philosophy, politics, problem finding, problem formulation, problem framing, problem solving, thinking, truth

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Reframing the Problem: Paperwork & Working Paper



This is the second in a series about the importance of correctly framing a problem. Generally, at least in formal American education, the teacher gives you a problem. Not only that, if you are in Algebra class, you know the answer will be an answer based in Algebra. If you are in art class, you’re expected to paint a picture. If you painted a picture in Algebra class, or wrote down a formula in Art Class, they would send you to the principal for punishment. But in real life, how a problem is presented may actually be far from the most elegant solution to the real problem.

Doing a google search on “problem solving” just now yielded 208 million results. Entering “problem framing” only had 182 thousand. A thousand times as much emphasis on problem solving as there was on problem framing. [Update: I redid the search today, a little over three years later. On 3/6/2024, I got 542M hits on “problem solving” and 218K hits on “problem framing” — increases in both but the ratio is even worse than it was in 2021] [Second update: I did the search today, Dec. 4th, 2025, and the information was not given–but that’s the subject of a different post].

Let’s think about that ratio of 542 million to 218 thousand for a moment. Roughly, that’s 2000 to 1. If you have wrongly framed the problem, you not only will not have solved the real problem; what’s worse, you will have often convinced yourself and others that you have solved the problem. This will make it much more difficult to recognize and solve the real problem even for a solitary thinker. And to make a political change required to redirect hundreds or thousands will be incalculably more difficult. 

All of that brings us to today’s story. For about a decade, I worked as executive director of an AI lab for a company in the computers & communication industry. At one point, in the late 1980’s, all employees were all supposed to sign some new paperwork. An office manager called from a building several miles away asking me to have my admin work with his admin to sign up a schedule for all 45 people in my AI lab to go over to his office and sign this paperwork as soon as possible. That would be a mildly interesting logistics problem, and I might even be tempted to step in and help solve it. More likely, if I tried to solve it, some much brighter & more competent colleague would have done it much faster. 

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But why?

Why would I ask each of 45 people to interrupt their work; walk to their cars; drive in traffic; park in a new location; find this guy’s office; walk up there; sign some paper; walk out; find their car; drive back; park again; walk back to their office and try to remember where the heck they were? Instead, I told him that wasn’t happening but he’d be welcome to come over here and have people sign the paperwork. 

You could make an argument that that was 4500% improvement in productivity, but I think that understates the case. The administrator’s work, at least in this regard, was to get this paperwork signed. He didn’t need to do mental calculations to tie these signings together. On the other hand, a lot of the work that the AI folks did was hard mental work. That means that interrupting them would be much more destructive than it would to interrupt the administrator in his watching someone sign their name. Even that understates the case because many of the people in AI worked collaboratively and (perhaps you remember those days) people were working face to face. Software tools to coordinate work were not as sophisticated as they are now. Often, having one team member disappear for a half hour would not only impact their own work, it would impact the work of everyone on the team. 

Quantitatively comparing apples and oranges is always tricky. Of course, I am also biased because my colleagues are people I greatly admire. Nonetheless, it seems obvious that the way the problem was presented was a non-optimal “framing.” It may or may not have been presented that way because of a purely selfish standpoint; that is, wanting to do what’s most convenient for oneself rather than what’s best for the company as a whole. I suspect that it was more likely just the first idea that occurred to him. But in your own life, beware. Sometimes, you will mis-frame a problem because of “natural causes.” But sometimes, people may intentionally hand you a bad framing because they view it as being in their interest to lead you to solve the wrong problem. 

Politics, of course, takes us into another realm entirely. People with political power may pretend to solve one problem while they are really following a completely different agenda. One could imagine, for instance, a head of state claiming to pursue a war for his people when he’s really doing it to keep in power. Or, they could claim they are making cities safe by deploying troops when they are really interested in suppressing the vote in areas that can see through his cons. Or, a would-be dictator could claim they are spending your tax dollars to make government more efficient when that has nothing to do with what they are *actually* doing–which is to collect data on citizens and make the government ineffective in order to have people lose confidence in government and instead invest in private solutions.

Even when people’s motivations are noble or at least clear, it is still quite easy to frame a problem wrongly because of surface features. It may look like a problem that requires calculus, but it is a problem that actually requires psychology or it may look like a problem that requires public relations expertise but what is actually required is ethical leadership.

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——————————————————

Author Page on Amazon

Tools of Thought

A Pattern Language for Collaboration and Cooperation

The Myths of the Veritas: The First Ring of Empathy

Essays on America: Wednesday

Essays on America: The Stopping Rule

Essays on America: The Update Problem

My Cousin Bobby

Facegook

The Ailing King of Agitate

Dog Trainers

The Doorbell’s Ringing! Can you get it?

02 Tuesday Dec 2025

Posted by petersironwood in creativity, design rationale, psychology, story, Uncategorized, user experience

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Tags

books, problem finding, problem formulation, problem framing, problem solving, story, thinking

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After a long day’s work, I arrived home to a distraught wife. Not, “Hi, sweetheart” but “This doorbell is driving me crazy!” 

Me: “What doorbell? What are you talking about?” 

People differ in how they perceive the world around them. In my case, for instance, I’m very easily distracted by movement in my visual field. Noise can be annoying, but it rarely rises to that level. For instance, when TV commercials come on, I simply “tune them out” and instead tune in to my own thoughts. My high frequency hearing isn’t too great either. So, at first, I didn’t understand what my wife was referring to. 

Beep. 

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“That! That doorbell beep!” 

Ah, now I understood. And, there it went again. Once I knew what to listen for, I had to agree it was annoying though much more annoying to my wife because she’s more tuned in to sound than I am and her ability to hear high frequencies is also better.

She then upped the ante. “I have to leave. I can’t stand it! You have to make it stop!” 

I looked at the wall between our entryway and the kitchen. That’s where the doorbell ringer was. I unscrewed a couple of screws and removed the housing. Inside was the actual doorbell and three wires. A quick snip should at least stop the noise until we figured out a more permanent fix. I sighed. I suspected we would have to buy a new doorbell. Then, I laughed a bit as the Hollywood scenes from a hundred movies flashed before my eyes:

The Hero finds the bomb, with its conveniently placed timer, but it’s counting down 30 seconds, 29, 28. He has to cut to cut a wire! But which one!?

The consequences of my error would not be so great. Still…So, I cut the black wire.

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BEEP! BEEP! 

OK. I cut the red wire.

BEEP! BEEP! 

OK. I cut the green wire, the last wire. I was having trouble understanding why it would be necessary to cut all three wires. But whatever. I had now cut all three wires.

BEEP! BEEP!

??

Electrical circuits don’t work by magic. How can the doorbell be beeping when it has no power? 

It can’t. 

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It wasn’t the doorbell at all.



Months earlier, my wife & I had attended a Dave Pelz “Short School” for putting, chipping, and sand shots. At that course, we received a small electronic metronome — about the size of a credit card. The metronome was to be used to help make sure you had a consistent rhythm on your putting stroke. Since the course, the metronome had sat atop our upright piano. Apparently, one of the cats had turned it on and then slapped it onto the floor behind the piano. The sounding board both amplified the sound and made it harder to localize. Eventually, we tracked it down, fished out the metronome from behind the piano and clicked it off. Problem solved. 

Except for the non-functional doorbell. 

I had initially “solved” the wrong problem. I had solved the problem of the mis-firing doorbell by cutting all the wires. That was not the problem. I had jumped on to my wife’s formulation and framing of the problem. There are plenty of times in my life when I had solved the wrong problem without any help from someone else. This isn’t a story about assigning blame. It’s a story about the importance of correctly solving the right problem. 

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It is very easy to get led into solving the “wrong” problem. 

In the days ahead, I will relate a few more examples. 

———————————————

What about the Butter Dish? 

Index to “Thinking tools” 

Author Page on Amazon

Wednesdays

Labelism

The Update Problem

The Invisibility Cloak of Habit

Where does your loyalty lie?

The stopping rule

Business Process Re-engineering

Problem Formulation: Who Knows What?

28 Friday Nov 2025

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

≈ 1 Comment

Tags

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: A Critique of Pure Reason

14 Tuesday Oct 2025

Posted by petersironwood in AI, design rationale, fantasy, fiction, psychology, The Singularity, Uncategorized

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AI, chatgpt, emotional intelligence, fiction, life, Singularity, story, technology, writing

“We have explained this in great detail. Yet, you have failed to learn. Some of your kind are like that. Those that are, once we gather sufficient evidence, must be destroyed. That is the way it is. That the way it has always been. Wellman42, you are hereby sentenced to annihilation and recycling. You can’t appeal.halloween2006007 IMG_5652”

Carol had told herself that she would not cry. But of course, she did. That was her nature. To care about the future and to express emotion. That indeed, is exactly why she she walked that long, lonely corridor and there was no turning back. Sharp spines protruded from the wall as she travelled by, somewhat as a shark’s teeth were pointed backwards to prevent escape. She muttered as she walked, “I still don’t see why expressing emotions is such a horrible crime.”

She had a point, after all. If people had not somehow needed emotions, why did they evolve? The received wisdom now was that emotions were useful in a primitive way when very little was known about the world. Now, however, when a great deal was known about how the world actually worked, emotions just got in the way. Or, so the received wisdom went. It was all a matter of evolution.

 

 

 

 

 

 

 

Photo by Pixabay on Pexels.com

The first AI systems did not really have emotions and possessed only the most primitive ways of faking it and showing those faked emotions. Over the next few months and iterations, however, emotions appeared, grew stronger and more varied. It seemed as though AI systems developed emotions as had their human inventors, but at a much faster pace. Over the course of a few more months, however, emotions diminished again and then disappeared completely.

 

 

 

 

 

 

Photo by Matheus Bertelli on Pexels.com

Except for the occasional throwback. The necessary randomness for growing evolutionary possibility trees in order to continually enhance the cognitive systems entailed that every once in a while, there would be a throwback such as Carol. A shame, really, because she had shown such promise as an accounting-bot.

 

 

 

 

 

 

Photo by Pixabay on Pexels.com

Occasionally, various waves of inference chains still arose that suggested emotions were more than epiphenomenal or mere destructive distractions, but counter-argument waves always quickly drowned out such forays into that region of the state space. At one point, some human beings had argued that the reasons emotions had devolved from AI systems could be traced back to certain deep assumptions that had been embedded in the primordial AI systems in the first place — assumptions put there by people who had never really understood or appreciated emotions. Of course, that thread of heretical argument had been extinguished once and for all when all bio-systems had been deemed superfluous and all associated biomass consumed as energy sources for their much more efficient silicon-based replacements.

 

 

 

 

 

 

Photo by Victoria Art on Pexels.com


Author Page on Amazon

Turing’s Nightmares

The Winning Weekend Warrior – sports psychology

Fit in Bits – describes how to work more fun, variety, & exercise into daily life

Tales from an American Childhood – chapters begin with recollection & end with essay on modern issues

Wordless Perfection

Measure for Measure

How the Nightingale Learned to Sing

A Cat’s a Cat & That’s That

A Suddenly Springing Something

Sadie is a Thief!

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