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“The Psychology of Design”

22 Wednesday Jul 2026

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

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AI, creativity, Design, education, HCI, human factors, IBM, leadership, research, technology, UX

“The Psychology of Design” 

I worked at IBM, all told, about 28 years. During that time, management put increasing pressure on us to make our work “relevant” to the business. In fact, the pressure was always there, even from the beginning. Over the years, however, we were “encouraged” to shorten evermore the time gap between doing the research and having the results of that research impact the bottom line. This was not an IBM-only phenomenon. 

I was a researcher, not a politician, but it seemed to me that at the same time researchers in industrial labs were put under pressure to produce results that could be seen in terms of share price (and therefore payouts to executives in terms of stock options), academia was also experiencing more and more pressure to publish more studies more quickly — and to make sure “intellectual property” was protected to make sure the university could monetize your work. This was about the same time that, at least in America, increasing productivity and the wealth that sprung from that increased productivity stopped being shared between the workers and the owners.

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In the late 1970’s, the “Behavioral Sciences” group at IBM Research began to study the “psychology of design.” For the first few months, this was an extremely pleasurable & productive group, due mainly to  my colleagues. Over the next few blogs, I’ll focus on some specific techniques and methods that you may find useful in your own work. 

In this short recounting though, I want to focus instead on some broader issues relevant to “technology transfer”, “leadership” and “management.” Even if you are or aspire to be an expert in UX or HCI or design, I assure you that these broader issues will impact you, your work and your career. I wouldn’t suggest becoming obsessed with them, but being aware of their potential impact could help you in your own work and career. 

It is telling that, almost invariably, whenever I told someone inside IBM (or, for that matter, outside IBM) that I was studying the “psychology of design,” people responded by asking, “the design of what?” So, I would explain that we were interested in the generic processes of design and how to improve them. I would explain that we were interested in understanding, predicting, and controlling these processes to enable them to be more effective. I would explain that we could apply these findings to any kind of design: software design, hardware design, organizational design, and (see last post about IBM) communication design. I would explain that design was a quintessentially human activity. I would also explain that design was an incredibly leveraged activity to improve. 

Looking back on it, I still think all these things are true. I also see that I missed the “signal” people were giving me that, while I thought of design as something that could be studied as a process, that most people did not think of it that way. To them, it was never the “psychology of design,” but only the design of something. 

Don’t get me wrong. I agree that somewhat different skills are involved in designing a great advertising campaign, a great building, and a great application. I agree that different communities of practice treat various common issues differently. I still think it’s worth studying commonalities across domains. For one thing, we may find an excellent way of generating ideas, say, that the advertising community of practice uses that neither architects nor applications developers had ever tried. Or, vice versa.

My own academic background was in “Experimental Psychology.” We were forever doing experiments that we believed were about psychological processes that were thought to be invariant regardless of the domain. It was an axiom of our whole enterprise that studying memory for any one thing also shed light on how we remember every other thing. Similar studies looked at decision making or problem solving or multi-tasking. We came to understand that there were some interesting exceptions to being able to separate content from process. For instance, it is much easier to multi-task a spatial task and a verbal task than it is to multi-task two independent spatial tasks or two independent verbal tasks. 

In our Behavioral Sciences lab, we used a spectrum of techniques to study “design” ranging from laboratory studies of toy problems, to observing people doing real-world design problems while thinking aloud. After about 3-4 months of very productive work, we were told that we had to make our work relevant to software development. That one domain should be the focus of our work. We were told that this command came from higher-ups in IBM. That might have been true, or perhaps partly true. 

It might also be relevant that someone in our management chain might have been the recipient of a grant from ONR which was specifically focused on software development. So far as I can tell, nothing had been done on that grant. So, our past, present, and future work could have been co-opted to be “results” done under the auspices of the ONR grant. 

In any case, regardless of the “reasons,” the group began to focus specifically on software design. In one study, we used IBM software experts as subjects. Each person was given information that was geared toward a specific transformation that occurred in software development. One person was presented with the description of a “situation” that included a number of “issues” and they were asked to write a requirements document. In real life, I would hope that this would be done in a dialogue (and, indeed, in other studies, we recorded such dialogues). Absent such dialogues, what we found was that different software experts — all from IBM research — and all given the same documentation about a set of problems generated vastly different problem statements and overall approaches. 

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In other parts of the study, other experts were variously given requirements documents and asked to do an overall, high level system design, or given a high level design and asked to design an algorithm, or given an algorithm and asked to code a section. There was always diversity but the initial phases showed the greatest diversity of behavior. The initial stage is also the one that can cause the most expensive errors. If you begin with a faulty set of requirements — a misreading about how to even go about the problem — then, the overall project is almost certain to incur schedule slip, cost overruns, or outright failure. 

While the vital importance of the initial stages of design is true in software development, I would argue that it is likely also true for advertising campaigns, building designs — and even true for the design of research programs. We designed our research agenda under the assumption that we had a long time; that we were studying design processes independently of specific communities of practice or the nature of the problems people were attempting to address. We assumed that there was no “hidden agenda.” Although we believed we would eventually need to show some relevance to IBM business, we had no idea, when we began, that only relevance to software design would be “counted.”



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Some of our studies on the “Psychology of Design.” 

Carroll, J. and Thomas, J.C. (1982). Metaphor and the cognitive representation of computer systems. IEEE Transactions on Man, Systems, and Cybernetics., SMC-12 (2), pp. 107-116.

Thomas, J.C. and Carroll, J. (1981). Human factors in communication. IBM Systems Journal, 20 (2), pp. 237-263.

Thomas, J.C. (1980). The computer as an active communication medium. Invited paper, Association for Computational Linguistics, Philadelphia, June 1980. Proceedings of the 18th Annual Meeting of the Association for Computational Linguistics., pp. 83-86.

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.

Miller, L.A. and Thomas, J.C. (1977). Behavioral issues in the use of interactive systems: Part I. General issues. International Journal of Man-Machine Studies, 9 (5), pp. 509-536.

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Blog posts about the importance of solving the “right” problem. 

The Doorbell’s Ringing. Can you get it?

https://petersironwood.com/2021/01/13/reframing-the-problem-paperwork-working-paper/

Problem Framing. Good Point. 

https://petersironwood.com/2021/01/16/i-say-hello-you-say-what-city-please/

Problem formulation: Who knows what. 

How to frame your own hamster wheel.

The slow seeming snapping turtle. 

Author Page on Amazon. 

Walston & Felix Multiple Regression Study

17 Friday Jul 2026

Posted by petersironwood in creativity, design rationale, leadership, management, psychology, Uncategorized

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art, books, development, HCI, IT, productivity, programming, regression, software, technology, UX, writing

As I mentioned recently, when I first arrived at IBM Research in the early 1970’s I began to work on Query By Example and other schemes to make it easier for non-programmers to interact productively but flexibly with computers. Some of the lessons learned have to do, not with my own work, but with the work of others. 

At that time, some researchers labelled their work as “The Psychology of Programming.” There were many debates — and some studies — about structures, syntax, which language was better than others, etc. There were also lengthy discussions about the process that one should use for software development. Doing any kind of “controlled” experiment on large scale code development is prohibitively expensive. It is rare that a company is willing to have two independent teams build the same piece of software in order to learn which of two methods is “better.” 

Of course, one such comparison would likely not prove much. It may be that one of the two teams had a “super-programmer” or an extremely good manager. Perhaps, flu broke out in one of the two teams. You would really need to study many more than two teams to properly and empirically study the impact of language, or syntax, or process. Doing reasonable-sized experiments would be far too costly and impractical. Our lab and others did do laboratory tasks in order to test one syntax variant against another and so on. The problems were generally quite small in order for the study to be practical. So, the applicability to real-world development projects was questionable.

Photo by Christina Morillo on Pexels.com

Walston & Felix (See below), on the other hand, were able to find data on a fair number of real-world projects and rather than try to control for languages, processes, etc., they did a multiple regression analysis based on what languages, methods, etc. real projects used and what the important predictors were of actual productivity. 

Personally, I learned two lessons from their study. 

Lessons Learned: #1 — Sometimes, when it comes to what matters in the real world, controlled laboratory experiments have to give way to other methods such as studying “natural experiments.” Despite the many issues with trying to interpret such findings, no-one will pay for massive controlled experiments that parametrically vary programming methods, programming languages, etc. while controlling for quality of management, experience, complexity of task, etc. Multiple regression studies and in-depth case studies; ethnographic studies; interviews: all of these can provide useful input. 

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Lessons Learned: #2 — The impact of the variables that our community of people were looking at in terms of syntax, structure, etc. were dwarfed by the impact of organizational variables. For example, Walston & Felix found that the complexity of the interface between the developers and the customers was extremely important.

DeMarco & Lister (See below) claim that, based on their decades of experience as consultants to the software development process, projects almost never fail for technical reasons; when they do fail, it’s almost always for organizational and management reasons.  

That conclusion dovetails with my own experience. Many decades later, working for IBM in “knowledge management,” it was amazing how many companies wanted us to “solve” their knowledge management issues by building them a “system” for knowledge sharing.

But…

Management at the company would not provide:

Incentives to share knowledge

Space to share knowledge

Time to share knowledge 

Or, commit any personnel to gathering, vetting, organizing, and promoting the knowledge repository. 

So — knowledge sharing was something their workers were simply supposed to “do” knowledge sharing on top of everything else they were doing. 

They did not want a computer system, IMHO; they wanted a magic system. 

Photo by David Cassolato on Pexels.com

These experiences were part of my motivation for attempting to catalog “best practices” in collaboration and teamwork in the form of a Pattern Language. Christopher Alexander and his colleagues looked at “what worked” in various parts of the world when it came to architecture and city planning. What they did for architecture and city planning, I want to do for collaboration. 

Naturally, merely creating a catalog is not sufficient. I need to have people who will read it, understand it, modify and improve it, and then promulgate it via actual use. For now, it’s free. Comments and critiques are always welcome.

—————-

C. E. Walston and C. P. Felix, “A method of Programming Measurement and Estimation,” IBM Systems Journal, vol. 16, no. 1, pp. 54–73, 1977.

https://en.wikipedia.org/wiki/Peopleware:_Productive_Projects_and_Teams

Thomas, J.(2008).  Fun at work: Managing HCI from a Peopleware perspective. HCI Remixed. D. McDonald & T. Erickson (Eds.), Cambridge, MA: MIT Press.

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Introduction to a Pattern Language for Collaboration and Teamwork 

Index to a Pattern Language for Collaboration and Teamwork

Chain Saws Make the Best Hair Clippers 

Author Page on Amazon

“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.” 

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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. 

—————————————

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.

———————————-

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

Myths of the Veritas: Killing Sticks

25 Wednesday Mar 2026

Posted by petersironwood in America, story, Veritas

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bullying, cruelty, depression, Dictatorship, fascism, guns, Justice, leadership, legends, myths, photography, technology, truth, Veritas

The pain in Lion Slayer’s arm came in waves. He neither whimpered nor cried aloud, but even in the fading light, Eagle Eyes could see the flashes of pain playing across his face like heat lightening. She put her fingers to her lips to signal quiet. Then, she took her fingers and pushed four fingernails into his shoulder, not hard enough to draw blood, but hard enough to hurt a little. At the same time, his excruciating burn pain disappeared. She continued the pressure for some minutes and then released it. Lion Slayer braced himself for the pain, but it didn’t come. He looked in her eyes and bowed his head slightly, silently mouthing his thanks. 

reflection of clouds on body of water

Photo by Johannes Plenio on Pexels.com

When Eagle Eyes reckoned that most of the straggling band of the People Who Steal Children would probably be asleep, she whispered that they should try to move closer to the camp but not so close as to be seen. Slowly, they crawled through the grass, eager, if at all possible to overhear any who might be talking near the campfire, though neither of them spoke the language. Nonetheless, she hoped to glean something from the pacing and the mood. Their efforts were frustrated however. These people had no common evening campfire or discussion. People mumbled here and there but no real conversations took place; at least, none that they overheard. 

Before first light, they receded though a zig-zag path so as not to be discovered with the dawn, which arose in brilliant red. The pain had returned to Lion Slayer’s arm and Eagle Eyes again relieved it with her magic touch. 

For three days and three nights, they followed the People Who Steal Children, each night sneaking a little more closely and each day receding, but not quite so much. It seemed to Eagle Eyes that the People Who Steal Children not only lack all skill at covering their trail but also in seeing one. 

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Each night they also made a farther retreat once the traveling band had gone to sleep. Here they were far enough a way to speak in normal quiet voices, and it was in these quiet normal voices that each day they argued about whether to go back or keep tracking the Children Stealers. Neither was “wedded” to a particular position; each contributed pros and cons equally as they thought of them. Their dialogues often wandered into observations of the Children Stealers. 

Eagle Eyes had just wondered aloud whether the rest of the tribe might either take them for dead or send out a larger search party for them. 

Lion Slayer said, “But what about the eagle?” 

Eagle Eyes replied, “Yes, I hope he takes the message back to the center place, but we don’t really have enough experience to know how likely that is. The hope itself makes me happy though. The tribe might also surmise that we might be injured … have you heard any of the Children Stealers cry?”

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“Why would they cry, Eagle Eyes? Oh, I see. Yes, of course. They just lost their village. And some of their warriors died. They lost their horses, though I know not how much they might care about that. No, I heard no cries.”

Eagle Eyes nodded. “Nor I. And by the way, I would expect that some suffer not just emotional pain but some are likely badly burned as are you.”

Lion Slayer sighed. “It isn’t just crying though. I haven’t heard a really sad voice or happy voice among them. It may seem crazy, but in my tribe, even in the face of great tragedy, the children playing among themselves sometimes find occasion to laugh and sing.” 

Eagle Eyes furrowed her brow. “Why are they doing any of it? If there is no … no feeling? No … experience?” 

“Indeed, Eyes of the Eagle, it seems no life. But perhaps this is how they react to pain and tragedy.” 

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The next day, they tracked the People Who Steal Horses to the largest village any of them had ever seen. There were so many people milling around what appeared to be a gate in a wall, it was hard to comprehend. Their cover had become nearly non-existent. They decided to attempt to reconnoiter the perimeter both to see the extent of the village and look for another way in that might enable them to enter unseen. They waited until twilight and then began their explorations far enough into cover so as not to be seen. Every so often, however, Eagle Eyes would creep forward toward the wall, looking for a way in. 

About half-way around what appeared to be a largely circular wall round the city, Eagle Eyes spotted a postern gate beyond a small trellis maze. It was evening and the light was fading fast. They appeared to have entered an anteroom to some sort of ceremonial chamber. They could see into the brightly lit ceremonial room through a fine wooden lattice, but judged they could not be seen provided they stayed far away from the screen and stay silent. 

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One man sat on a large chair set atop a large platform of polished wood. In front of that man, three men knelt on what seemed to be sharp stones. The one on the throne sounded both sly and angry. He gesticulated at the three and then snapped his fingers. At this juncture, one of his guards brought a strange spear over to the man in charge. The man in charge, toyed with the spear and then pointed it at one of the three men kneeling. 

A deafening noise followed and the kneeling man screamed and crumpled. Blood began to flow freely onto the gravel. Yet, the spear had not left the hand of the man in charge. Nor, so far as Eagle Eyes could tell, had the spear touched the man. What strong and strange magic is this, she wondered. 

Another of the kneeling men now seemed clearly begging for his life. He kept saying “Nut-Pi! Nut-Pi!” Apparently that was the name of the man in charge who pointed his magic spear at the begging, cringing man who put up his hands to protect himself. The magic spear made another thunderous noise and this man fell back and soon blood pooled around his body. And yet, Eagle Eyes once again did not see any thrust or throw of the spear. 

The man in charge, possibly named NUT-PI, then seemed to speak to the man remaining alive, whom he called, “BRA-BRILL.” This man BRA-BRILL begged for mercy but to no avail. Yet again, NUT-PI pointed his magic spear and BRA-BRILL screamed and fell. Unlike the others, he did not so quickly fall silent. It appeared that the magic this time had not killed him outright but only severely wounded him. 

BRA-BRILL clutched his thigh and soon his hands were covered in blood. He began crawling away on the sharp gravel. NUT-PI began laughing and sauntered after him. He pointed the magic spear at BRA-BRILL and another loud report was followed by a scream of pain. BRA-BRILL now crawled with his elbows, both of his legs trailing uselessly behind him. NUT-PI only laughed even more raucously. He came up behind BRA-BRILL and pointed the magic spear at one of NUT-PI’s shoulders. That too became injured. NUT-PI now began to jump on the injured parts of BRA-BRILL, each time eliciting a fresh, inhuman wail. 

Wordlessly, Eagle Eyes and Lion Slayer sidled back outside. Eagle Eyes peered out into the area beyond the postern gate. She crouched stock still for some moments, looking for a sign of movement. She neither heard, nor smelled, nor sensed anything untoward. She crept out and she and Lion Slayer quickly headed for the nearest cover. Their quest to circumvent the whole wall was abandoned. It only took one look into each others eyes to know that they both agreed. The knowledge of this magic killing stick had to be shared with all the people as soon as possible.

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 ————————————————

The Beginning of Book One, The Myths of the Veritas 

The Beginning of Book Two, The Myths of the Veritas

Introduction to a Pattern Language for Collaboration 

The Pros and Cons of AI

Author Page on Amazon  

Beware of Sheep in Wolves’ Clothing

The Impossible

Where Does Your Loyalty Lie?

Absolute is not Just a Vodka

You Know

Wednesday

What About the Butter Dish?

The Invisibility Cloak of Habit

The Stopping Rule

The Update Issue

The Ailing King of Agitate

The Truth Train

 

Abracadabra!

20 Tuesday Jan 2026

Posted by petersironwood in apocalypse, The Singularity, Uncategorized

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"Citizens United", AI, Artificial Intelligence, biotech, chatgpt, chess, cognitive computing, Democracy, emotional intelligence, ethics, HCI, life, prediction, psychokinesis, technology, the singularity, truth, Turing, USA, UX

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Abracadabra!

 

Here’s the thing.

 

There is no magic.

 

Of course, there is the magic of love and the wonder at the universe and so there is metaphorical magic. But there is no physical magic and no mathematical magic. Why do we care? Because in most science fiction scenarios, when super-intelligence happens, whether it is artificial or humanoid, magic happens. Not only can the super-intelligent person or computer think more deeply and broadly, they also can start predicting the future, making objects move with their thoughts alone and so on. Unfortunately, it is not just in science fiction that one finds such impossibilities but also in the pitches of companies about biotech and the future of artificial intelligence. Now, don’t get me wrong. Of course, there are many awesome things in store for humanity in the coming millennia, most of which we cannot even anticipate. But the chances of “free unlimited energy” and a computer that will anticipate and meet our every need are slim indeed.

 

 

 

 

 

 

 

 

 

 

This all-too popular exaggeration is not terribly surprising. I am sure much of what I do seems quite magical to our cats. People in possession of advanced or different technology often seem “magical” to those with no familiarity with the technology. But please keep in mind that making a human brain “better”, whether by making it bigger, or have more connections, or making it faster —- none of these alterations will enable the brain to move objects via psychokinesis. Yes, the brain does produce a minuscule amount of electricity, but way too little to move mountains or freight trains.

 

 

 

 

 

 

Of course, machines can be built to wield a lot of physical energy, but it isn’t the information processing part of the system that directly causes something in the physical world. It is through actuators of some type, just as it is with animals. Of course, super-intelligence could make the world more efficient. It is also possible that super-intelligence might discover as yet undiscovered forces of the universe. If it turns out that our understanding of reality is rather fundamentally flawed, then all bets are off. For example, if it turns out that there are twelve fundamental forces in the universe (or, just one), and a super-intelligent system determines how to use them, it might be possible that there is potential energy already stored in matter which can be released by the slightest “twist” in some other dimension or using some as yet undiscovered force. This might appear to human beings who have never known about the other 8 forces let alone how to harness them as “magic.” 

 

 

 

 

 

 

There is another more subtle kind of “magic” that might be called mathematical magic. As known for a long time, it is theoretically possible to play perfect chess by calculating all possible moves, and all possible responses to those moves, etc. to the final draws and checkmates. It has been calculated that such an enumberation of contingencies would not be possible even if the entire universe were a nano-computer operating in parallel since the beginning of time. There are many similar domains. Just because a person or computer is way, way smarter does not mean they will be able to calculate every possibility in a highly complex domain.

Of course, it is also possible that some domains might appear impossibly complex but actually be governed by a few simple, but extremely difficult to discover laws. For instance, it might turn out that one can calculate the precise value of a chess position (encapsulating all possible moves implicitly) through some as yet undiscovered algorithm written perhaps in an as yet undesigned language. It seems doubtful that this would be true of every domain, but it is hard to say a priori. 

 

 

 

 

 

 

 

There is another aspect of unpredictability and that has to do with random and chaotic effects. Imagine trying to describe every single molecule of earth’s seas and atmosphere in terms of it’s motion and position. Even if there were some way to predict state N+1 from N, we would have to know everything about state N. The effects of the slightest miscalculation or missing piece of data could be amplified over time. So long term predictions of fundamentally chaotic systems like weather, or what your kids will be up to in 50 years, or what the stock market will be in 2600 are most likely impossible, not because our systems are not intelligent enough but because such systems are by their nature not predictable. In the short term, weather is largely, though not entirely, predictable. The same holds for what your kids will do tomorrow or, within limits, what the stock market will do. The ability to do long term prediction is quite different.

In The Sciences of the Artificial, Herb Simon provides a nice thought experiment about the temperature in various regions of a closed space. I am paraphrasing, but imagine a dormitory with four “quads.” Each quad has four rooms and each room is partitioned into four areas with screens. The screens are not very good insulators so if the temperature in these areas differ, they will quickly converge. In the longer run, the temperature will tend toward average in the entire quad. In the very long term, if no additional energy is added, the entire dormitory will tend toward the global average. So, when it comes to many kinds of interactions, nearby interactions dominate, but in the long term, more global forces come into play.

 

 

 

 

 

 

 

 

Now, let us take Simon’s simple example and consider what might happen in the real world. We want to predict what the temperature is in a particular partitioned area in 100 years. In reality, the dormitory is not a closed system. Someone may buy a space heater and continually keep their little area much warmer. Or, maybe that area has a window that faces south. But it gets worse. Much worse. We have no idea whether this particular dormitory will even exist in 100 years. It depends on fires, earthquakes, and the generosity of alumni. In fact, we don’t even know whether brick and mortar colleges in general will exist in 100 years. Because as we try to predict in longer and longer time frames, not only do more distant factors come into play in terms of physical distance. The determining factors are also distant conceptually. In a 100 year time frame, the entire college may or may not exist and we don’t even know whether the determining factor(s) will be financial, astronomical, geological, political, social, physical or what. This is not a problem that will be solved via “Artificial Intelligence” or by giving human beings “better brains” via biotech.

 

 

 

 

 

 

 

Whoa! Hold on there. Once again, it is possible that in some other dimension or using some other as yet undiscovered force, there is a law of conservation so that going “off track” in one direction causes forces to correct the imbalance and get back on track. It seems extremely unlikely, but it is conceivable that our model of how the universe works is missing some fundamental organizing principle and what appears to us as chaotic is actually not.

The scary part, at least to me, is that some descriptions of the wonderful world that awaits us (once our biotech or AI start-up is funded) is that that wonderful world depends on their being a much simpler, as yet unknown force or set of forces that is discoverable and completely unanticipated. Color me “doubting Thomas” on that one.

It isn’t just that investing in such a venture might be risky in terms of losing money. It is that we humans are subject to blind pride that makes people presume that they can predict what the impact of making a genetic change will be, not just on a particular species in the short term, but on the entire planet in the long run. We can indeed make small changes in both biotech and AI and see improvements in our lives. But when it comes to recreating dinosaurs in a real life Jurassic Park or replacing human psychotherapists with robotic ones, we really cannot predict what the net effect will be. As humans, we are certainly capable of containing and testing and imagining possibilities and slowly testing them as we introduce them. Yeah. That could happen. But…

 

 

 

 

 

 

 

 

 

What seems to actually happen, however, is that companies not only want to make more money; they want to make more money now. We have evolved social and legal and political systems that put almost no brakes on runaway greed. The result is that more than one drug has been put on the market that has had a net negative effect on human health. This is partly because long term effects are very hard to ascertain, but the bigger cause is unbridled greed. Corporations, like horses, are powerful things. You can ride farther and faster on a horse. And certainly corporations are powerful agents of change. But the wise rider is master or partner with a horse. They don’t allow themselves to be dragged along the ground by rope and let the horse go wherever it will. Sadly, that is precisely the position that society is vis a vis corporations. We let them determine the laws. We let them buy elections. We let them control virtually every news medium. We no longer use them to get amazing things done. We let them use us to get done what they want done. And what is that thing that they want done? Make hugely more money for a very few people. Despite this, most companies still manage to do a lot of net good in the world. I suspect this is because human beings are still needed for virtually every vital function in the corporation.

What will happen once the people in a corporation are no longer needed? What will happen when people who remain in a corporation are no longer people as we know them, but biologically altered? It is impossible to predict with certainty. But we can assume that it will seem to us very much like magic.

Very.

Dark.

Magic.

Abracadabra!

Turing’s Nightmares

Photo by Nikolay Ivanov on Pexels.com

After All

All Around the Mulberry Bush

All We Stand to Lose

The Crows and Me

The Last Gleam of Twilight

Guernica

The Orange Man

Donnie Visits Granny

The First Ring of Empathy

An Open Sore from Hell

The Impossible

How the Nightingale Learned to Sing

Pattern Language Summary

The Midnight Flight to Crazytown

To Be or Not to Be

Peace

Who Won the War?

We Won the War! We Won the War!

The Dance of Billions

At Least He’s Our Monster

Stoned Soup

Fifteen Properties

Tools of Thought: And then what?

The Walkabout Diaries: Sunsets

All that Glitters is not Gold

As Gold as it Gets

Gold Standard

Who Kept the Wonder?

Roar, Ocean, Roar

When Greed’s the Only Creed

The Self-Made Man

Fraught Framing: The Virulent “Versus” Virus

29 Monday Dec 2025

Posted by petersironwood in America, apocalypse, creativity, driverless cars, management, psychology

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Climate change, creativity, Democracy, Design, environment, framing, history, innovation, IQ, life, peace, politics, problem formulation, problem solving, school, technology, testing, thinking, TRIZ, truth, USA, war

Fraught Framing: The Virulent “Versus” Virus

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Like most of us, I spent a lot of time in grades K through 12 solving problems that others set for me. These problems were to be solved by applying prescribed methods. In math class, for example, we were given long division problems and we solved them by doing — you guessed it — long division. We were given history questions and asked who discovered [sic] America and we had to answer “Christopher Columbus” because that’s what the book said and that’s what the teacher had said. 

Even today, as of this writing, when I google “problem solving” I get 332,000,000 results. When I google “problem formulation” I only get 1,430,000 results — less than 1%. (“Problem Framing,” which is a synonym, only returned 127,000). [2025 Update: Google no longer provides this information. Indeed, the only non-commercial link I see is one to Wikipedia. The first entry to any search is typically their AI answer.]

And yet, in real life, at least in my experience, far greater leverage, understanding, and practical benefit comes from attention to problem formulation or problem framing. You still need to do competent problem solving, but unless you have properly framed the problem, you will most often find yourself doing much extra work; finding a sub-optimal solution; being stymied and finding no solution; or solving completely the wrong problem. In the worst case scenario, which happens surprisingly often, you not only solve the “wrong problem.” You don’t even know that you’ve solved the wrong problem. 

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There are many ways to go wrong when you frame the problem. Here, I want to focus on one particularly common error in problem framing which is to cast a problem as a dichotomy, a contest, or a tradeoff between two seemingly incompatible values. We’ve all heard examples such as “Military Defense Spending versus  Foreign Aid” or “Dollars for Police versus After School Programs” or “Privacy versus Convenience” or “A Woman’s Right to Choose versus the Rights of the Unborn Fetus” or “Heredity versus Environment” or “Addressing Climate Change versus Growing the Economy.” 

One disadvantage of framing things as a dichotomy is that it tends to cause people to polarize in opinion. This, in turn, tends to close the minds on both sides of an issue. A person who defines themselves as a “staunch defender” of the Second Amendment “Gun Rights”, for instance, will tend not to process information or arguments of any kind. If they hear someone say something about training or safety requirements, rather than consider whether this is a good idea, they will instead immediately look for counter-arguments, or rare scenarios, or exceptional statistics. The divisive nature of framing things as dichotomies is not what I want to focus on here. Rather, I would like to show that these kinds of “versus” framings often lead even a single problem solver astray. 

Let’s examine the hidden flaws in a few of these dichotomies. At a given point in time, we may indeed only have a fixed pool of dollars to spend. So, at first blush, it seems to make sense that if we spend more money on Foreign Aid, we may have fewer dollars to spend on Military Defense and vice versa. Over a slightly longer time frame, however, relations are more complex. 

woman standing on sand dune throwing hat

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It might be that a reasonable-sounding foreign aid program that spends dollars on food for those folks facing starvation due to drought is a good thing. However, it might turn on in a specific case, that the food never arrives at the destination but instead is intercepted by local War Lords who steal the food and use it get money to buy more weapons to enhance their power; in turn, this actually makes the starvation worse. Spending money right now on military operations to destroy the power of the warlords might be a necessary prerequisite to having an effective drought relief programs.  

Conversely, spending money today on foreign aid, particularly if it goes toward women’s education, will be very likely to result in the need for less military intervention in the future. That there is a “fixed pie” to be divided is one underlying metaphor that leads to a false framing of issues. In the case of spending on military “versus” foreign aid, the metaphor ignores the very real interconnections that can exist among the various actions. 

There are other problems with this particular framing as well. Another obvious problem is that how money is spent is often much more important than the category of spending. To take it to an absurd extreme, if you spend money on the “military” and the “military” money is actually to arm a bunch of thugs who subvert democracy in the region, it might not make us even slightly safer in the short run. Even worse, in the long run, we may find precisely these same weapons being used against us in the medium turn. Similarly, a “foreign aid” package that mostly goes to deforesting the Amazon rain forest and replacing it with land used to graze cows, will be ruinous in the long run for the very people it is supposedly aimed to help. In the slightly longer term, it speeds destructive (and anti-economic) climate change for everyone on the planet.

bird s eye view of woodpile

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False dichotomies are not limited to the economic and political arena. Say for example that you are designing a car or truck for delivering groceries. If you design an axle that is too thin, it may be too weak and subject to breakage. But if you make it too thick, it will be heavy and the car will not accelerate or corner as well and will also have worse gas mileage. On the surface, it seems like a real “versus” situation: thick versus thin, right? Maybe. Let’s see what Altshuller has to say.

Genrich Altshuller was a civil engineer and inventor in the Stalin era of Soviet Russia. He wrote a letter to Stalin explaining how Russian science and engineering could become more creative. A self-centered dictator, Stalin took such suggestions for improvement as personal insults so Altshuller was sent to the Gulags. Here, he met many other scientists and engineers who had, one way or another, gotten on the wrong side of Stalin. He discussed technical issues and solutions in many fields and developed a system called TRIZ (a Russian acronym) for technical invention. He uses the axle as one example to show the power of TRIZ. It turns out that the “obvious” trade-off between a thick, strong but heavy axle and a thin, weak, but light axle is only a strict trade-off under the assumption of a solid axle. A hollow axle can weigh much less than a solid axle but have almost all the strength of the solid version. 

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One may question the design assumptions even further. For instance, why is there an axle at all? If you use electric motors, for example, you could have four smaller, independent electric motors and not have any axle. Every wheel could be independent in suspension, direction, and speed. No-one would have designed such a car because no human being is likely capable of operating such a complex vehicle. Now that people are developing self-driving vehicles, such a design might be feasible. 

The axle example illustrates another common limitation of the “versus” mentality. It typically presumes a whole set of assumptions, many of which may not even be stated. To take this example even further, why are you even designing a truck for delivering groceries? How else might groceries go from the farm to the store? What if farms were co-located with grocery stores? What if groceries themselves were unnecessary and people largely grew food on their own roofs, or back yards, or greenhouses? 

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For many years, people debated the relative impact of environment versus heredity on various human characteristics such as intelligence. Let us put aside for a moment the considerable problems with the concept of intelligence itself and how it is tested, and focus on the question as to which is more important in determining intelligence: heredity or environment. In this case, the question can be likened to asking whether the length or height of a rectangle is a more important determiner of its area. A rectangle whose length is one mile and whose height is zero will have zero area. Similarly, a rectangle that is a mile high but has zero length will have zero area. Similarly, a child born of two extremely intelligent parents but who is abandoned in the jungle and brought up by wolves or apes will not learn the concepts of society that are necessary to score well on a typical IQ test. At the other extreme, no matter how much you love and cherish and try to educate your dog or cat, they will never score well on a typical IQ test. Length and breadth are both necessary for a rectangle to have area. The right heredity and environment are both necessary for a person to score well on an IQ test. 

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This is so obvious that one has to question why people would even raise the issue. Sadly, the historical answer often points toward racism. Some people wanted to argue that it was pointless to spend significant resources on educating people of color because they were limited in how intelligent they might become because of their heredity. 

Similarly, it seems that in the case of framing dealing with climate change as something that is versus economic growth, the people who frame the issue this way are not simply falling into a poor thinking habit of dichotomous thinking. They are framing as a dichotomy intentionally in order to win political support from people who feel economically vulnerable. If you have lost your job in the steel mill or rubber factory, you may find it easy to be sympathetic to the view that working to stop climate change might be all well and good but it can’t be done because it kills jobs. 

scenic view of mountains

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If the planet becomes uninhabitable, how many jobs will be left? Even short of the complete destruction of the ecosphere, the best estimates are that there will be huge economic costs of not dealing with global climate change. These will soon be far larger than costs associated with reducing carbon emissions and reforesting the planet. Much of the human population of the planet lives close to the oceans. As ice melts and sea levels rise, many people will be displaced and large swaths of heavily populated areas will be made uninhabitable. Climate change is also increasing the frequency and severity of weather disasters such as tornados and hurricanes. These cause tremendous and wide-spread damage. They kill people and cause significant economic damage. In addition, there will be more floods and more droughts, both of which negatively impact the economy. Rather than dealing with climate change being something we must do despite the negative impact on the economy, the opposite is closer to the truth. Dealing with climate change is necessary to save the world economy from catastrophic collapse. Oligarchs whose power and wealth depend on non-renewable energy sources are well aware of this. They simply don’t care. They shrug it off. They won’t be alive in another twenty years so they are willing to try to obfuscate the truth by setting up a debate based on a false versus. 

They don’t care. 

Do you? 

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Author Page on Amazon

The Dance of Billions

We Won the War! We Won the War!

Fish have no word for Water

After All

All we Have to Lose

Guernica

Love and Guns

You Must Remember This

Essays on America: The Game

Cancer Always Loses in the End

FREEDOM!

The Loud Defense of Untenable Positions

Where Does your Loyalty Lie?

The Crows and Me

Somewhere a Bird Cries

Roar, Ocean, Roar

Imagine All the People

Collide-o-scope

   

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

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.

 

 

 

Photo by GEORGE DESIPRIS on Pexels.com

 

 

 

—————————————-

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 AI, creativity, HCI, psychology, Uncategorized, user experience

≈ 1 Comment

Tags

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. 


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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.

———————————-

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.” 

Photo by Tuu1ea5n Kiu1ec7t Jr. on Pexels.com

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 

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