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Not-Separateness

28 Monday Sep 2026

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

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AI, architecture, art, beauty, Design, development, ecology, governance, GreenNewDeal, HCI, human factors, IBM, leadership, painting, peace, photography, society, technology, TNOO, UI, usability, user_experience, UX, ux-design

Not-Separateness

It seems odd to specify a property of natural order in terms of what it is not. On the other hand, I cannot come up with a positive alternative that doesn’t bring other connotations with it. I think it’s related to “unified” or “integral” or “belonging” or “inter-related” but none of those seem quite so on the mark as does “Not-Separateness.” 

Christopher Alexander’s degree from MIT was in architecture. Part of the reason he may have chosen this particular term is in reaction to some examples of architecture in which the architect seems to be in the business of constructing a building whose primary purpose is to make them famous regardless of what that building does to the neighborhood or its occupants. 

Photo by BROTE studio on Pexels.com

Imagine Mr. Bigg designs a house that is a perfect black cube set on on vertex. In effect, this design says to me: “I am BIG. I am Mr. Bigg! I am a genius! You would have never been brilliant enough to design a house that is a cube on its vertex! You would have wasted your time and done something mundane like placed the cube on the ground on one of its faces. Anyone could think of that! But I put it on a vertex!” Indeed, we may easily imagine that he says words to this effect when his interview is reported on in the (mythical) architectural journal entitled, Things that look different! 

“Mr. Bigg, you made the Bigg House out of black steel and black glass. Some critics have argued that this doesn’t fit with the existing neighborhood of stone cottages with thatched roofs.”

“Of course, little minds will always criticize Bigg ideas.” 

“Yes, yes. It also means that the construction costs of the house were quite high. And, the estimated costs of heating and cooling are much higher as well.”

“Nothing that a worthwhile (i.e., wealthy) client can’t afford.” 

“Some have also argued that it is inconvenient for the occupants who have to walk up and down at a steep angle and that furniture such as dressers, tables, chairs, and beds do not accommodate well to the tilted walls.” 

“Let me ask you a question. Would you have ever thought of putting a cube on its corner? No. I didn’t think so!” 

Of course, this is exaggeration.

But not much. 

We would hope that User Experience designers take into account the users, their tasks, their contexts, and the way in which their designs interact with other related artifacts, people and processes. We would hope that applications and artifacts and services are all designed with the property of “Not-Separateness.” 

In the early 1980’s, I worked in the IBM Office of the Chief Scientist. My main assignment was to get IBM to pay more attention to the usability of its products. As part of that process, I visited quite a few IBM development labs around the world and spoke to many development teams. On many of these visits, I was accompanied by the Chief Scientist, a brilliant physicist, who “got” usability. 

On one occasion, we watched a new printing technology. Instead of printing out black printing on a white sheet of paper sized 8.5” x 11” or A4, this printout was of no standard size. The printing was black on a shiny silver sheet that curled severely. The Chief Scientist asked the head of the development team how they envisioned this being used. 

Chief Scientist: “Once someone printed this out, what would they do with it?”

Answer: “Oh, anything they liked.” 

Chief Scientist: “I mean, would people tape this into a notebook or paste it? Or would you imagine notebooks that would bind such paper?” 

Answer: “It’s not up to me to decide how people would use it. Doesn’t it look cool?” 

Another type of answer we heard more than once to the question, “How would this be used?” 

— “Oh, it’s a (replacement/upgrade) for this other IBM product.” 


“But who would use it and for what?” 


“It has three main components. Would you like a description of the components?” 

Photo by Andru00e9 Ulyssesdesalis on Pexels.com

Of course, there is a place for “playing around” with technology and thereby discovering things which someone else may find a use for. But in design and development of a product or service, having a clear notion of context of use and the users and tasks is fundamental. Of course, other users may appropriate a product or service for purposes beyond those envisioned by the original designers. That’s cool. 

What’s not cool is designing a device that is to be used in the bright outdoor sunlight and then testing the display in a typical office environment. Have you ever run across something like that? I have. Many times. At ATMs, Gas Stations, and outdoor display maps.

A more subtle lack of contextualization in design occurs when the design team fails to realize how many interruptions happen to the user while they are trying to accomplish a single task with the new application. If you “test” the application while the user is in a quiet “usability lab” and they can give your tasks their undivided attention, then necessitating them to remember the invisible internal state of “Insert” versus “Edit” mode may not be a big deal at all. They will simply remember. But in their office environment, they may be interrupted by a phone call, a message, or their boss entering their office and asking a series of detailed questions. If they now go back to the task at hand, there is about a 50-50 chance that they will correctly guess whether they are in “Edit” mode or “Insert” mode. 

A design which shows the property of Not-Separateness is the natural result of a process which shows not-separateness. Here are a few common ways to help ensure the design process grows organically from the users and their goals & contexts. 

* Put people on the design team who are familiar with the users, and/or their tasks, and/or their contexts. 


* People on the design team observe people engaging in the relevant processes, whenever possible, not — or not only — in a “Usability Lab” but in the actual work environment. For instance:

  • Observe people actually using product P (or service S) in version N so that version N+1 can be better attuned to the needs of the users.
  • Have people think aloud while doing this.
  • Gather and understand feedback from service calls and help desks and customer complaints in order to improve over time. 

There will be benefits to a company who takes such approaches beyond initial sales. If you’ve done any gardening, you will appreciate that the quality of the tomatoes you enjoy eating is related to the quality of the soil and the quality of the care you give the tomatoes. Similarly, a product or service that has the quality of Not-Separateness will not only be useful — users will fight to keep your product or service. It becomes integrated with the environment. To change the brand means that they will have to change the way they work; possibly even with whom they work. Not-Separateness is likely a path to what business people like to call a “Cash Cow.” 

If you’ve ever walked through a neighborhood after a hurricane, you’ve likely seen many uprooted trees. When you look at the roots of an uprooted tree, what do you see? Of course, you see roots. But what else? You see rocks and soil all around and embedded into the roots. They are Not-Separate. In a hurricane, there are typically not only high winds. There is also a lot of rain. The trees are hit with a double whammy. The wind pushes the tree but the rain weakens the solid soil in which the tree is embedded. It is the combination that makes it very difficult for the tree to “hold on” and keep from falling over. 

Living things, just like us, have a 4.5 billion year history of living. The living things adapt over time to their environment and they mold the environment to their needs. They are not separate. Flowers appeal to the insects who pollinate them. The insects who pollinate them are adapted to the characteristics of the flower. A horse adapts to their rider and the rider adapts to their horse. A product or service must have a design that serves the needs of its stakeholders. For a product or service to have maximum beauty, utility, and longevity, it must also have a way to adapt to the changing needs of the users and other stakeholders. At the same time, if the users and their organizations adapt to the product or service, then true Not-Separateness is achieved. 

If you want to skimp on designing your product or service, you can make it more separate, more divorced from its context, its users, and its tasks. Of course, if you do that, you also make much easier for your users to abandon your product and switch to a new one. 

Another way to think about this in terms of systems theory is where you draw the boundary. If you draw a sharp boundary around your product, you may find that, over time, your product becomes ever more peripheral to the community you’re trying to support and your product is ever more fungible with others in its class. On the other hand, if you draw the boundary around the product or service and the people and organizations who provide the product or service then, you are on the path of ever tighter interconnect. 

“Who Speaks for Wolf” is a Pattern in a Pattern Language for Collaboration and Cooperation. It is based on a Native American story and shows the importance of making sure that knowledge and perspectives of all relevant stakeholders are taken into account when a change is contemplated. Who is “relevant” may not always be obvious, but, in my experience, many more projects fail or are delayed because of too limited a view than of too broad a view.

Who Speaks for Wolf

Not-Separateness is not only a quality of good design in terms of not overly separating the context and users from the product or service. It is also a good quality for the organization that produces products & services. Of course, some people today must manage a giant amorphous “organization” of tens of thousands of people so they set up divisions, and departments, and groups, and teams, and positions etc. There may indeed be a “UX Department” and a “Software Department” and a “Hardware Department.” That’s all fine. But it is counter-productive if the UX Department sees itself as separate from the rest of the company. To a great extent the success of the UX Department depends on the success of the Hardware and Software Departments. The Sales Department’s success will, of course, depend partly on the skills of the Sales Department. But it will also depend on the success of the UX Department, HW, SW and Services. 

Have you ever had a paper cut? It isn’t just the skin on a quarter inch of the inside of your ring finger that’s cut. You’re cut! It isn’t just that the finger feels pain. You feel pain! That causes you to take steps to ameliorate the pain and to try to make sure it doesn’t happen again. That’s why empathy in leadership is important. A leader must feel empathy for all, or the organization will disintegrate from lack of Not-Separateness. At some point, a raccoon may chew off its own arm in order to escape a trap. 

But it isn’t the first thing that occurs to them every time they experience a thorn in the paw! 

The raccoon doesn’t say to itself:  — “that paw is giving me pain! I’m going to chew it off! Then, it won’t hurt any more.”

Photo by anne sch on Pexels.com

Evolution did not evolve a raccoon that acts that way. Self-mutilation exists but it is typically a last resort.

But not for corporations. It is the first thing they think of:

“Our (you name it) Department is not performing well. Let’s lay them off and outsource it. Better yet, we’ll replace it with AI!”

What does that say to every thinking employee in the entire corporation? It says:

“You know what? All this talk about teamwork and pulling together is a total bunch of bull$hit. You cannot trust management to do what’s best for everyone. You can only trust them to do what’s best for them.” 

Living forms in nature are living forms. Their parts have severe Not-Separateness with the other parts of that form. Often, as in well-functioning families or teams, that extends to all members of the group. 

Not-Separateness is essentially deep cooperation. I give to the larger community by becoming a part of it and doing my part in it. I lend strength to the community. In return, I gain strength from that community. It is not a zero sum game, of course. The community, if it is functional, is much stronger than the sum of the individuals in that community. 

This is so deeply embedded in 4.5 billion years of evolution that it does not surprise me that we recognize beauty as being even more beautiful if it is not separate. Not-Separate enhances beauty because, like all the other properties, it is essential to life. 

Eventually, if humanity is to survive, we will realize that Not-Separateness applies to all of us. We are not there yet. But that doesn’t mean we cannot appreciate and design Not Separateness in our products, in our services, and our lives. 

Photo by Pixabay on Pexels.com

—————-

The Declaration of Interdependence

How the Nightingale Learned to Sing

Roar, Ocean, Roar

Imagine all the people

Cancer Always Loses in the End

Ripples

Author Page on Amazon

Thomas, J.C. and Kellogg, W.A. (1989). Minimizing ecological gaps in interface design, IEEE Software, January 1989.

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

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

Thomas, J.C. (1985). Human factors in IBM. IBM Research Report. RC-11267.  Yorktown Heights, NY: IBM Corporation.

Turing’s Nightmares: US Open Closed

08 Tuesday Sep 2026

Posted by petersironwood in AI, apocalypse, fiction, sports, The Singularity, Uncategorized

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AI, Artificial Intelligence, cognitive computing, competition, future, philosophy, Robotics, Sci-Fi, sports, technology, Tennis, US Open, writing

tennisinstruction

Bounce. Bounce. Thwack!

The sphere spun and arced into the very corner, sliding on the white paint.

Roger’s racquet slid beneath, slicing it deep to John’s body.

Thus, the match began.

Fierce debate had been waged about whether or not to allow external communication devices during on-court play. Eventually, arguments won that external communicators constituted the same inexorable march of technology represented by the evolution from wooden racquets to aluminum to graphite to carbon filamented web to carboline.

Behind the scenes, during the split second it took for the ball to scream over the net, machine vision systems had analyzed John’s toss and racquet position, matching it with a vast data base of previous encounters. Timed perfectly, a small burst of data transmitted to Roger enabling him to lurch to his right in time to catch the serve. Delivered too early, this burst would cause Roger to move too early and John could have altered his service direction to down the tee.

Roger’s shot floated back directly to the baseline beneath John’s feet. John shifted suddenly to take the ball on the forehand. John’s racquet seemed to sling the ball high over the net with incredible top spin. Indeed, as John’s arm swung forward, his instrumented “sweat band” also swung into action exaggerating the forearm motion. Even to fans of Rafa Nadal or Carlos Alcaraz, John’s shot would have looked as though it were going long. Instead, the ball dove straight down onto the back line then bounced head high.

Roger, as augmented by big data algorithms, was well in position however and returned the shot with a long, high top spin lob. John raced forward, leapt in the air and smashed the ball into the backhand corner bouncing the ball high out of play.

The crowd roared predictably.

For several months after “The Singularity,” actual human beings had used similar augmentation technologies to play the game. Studies had revealed that, for humans, the augmentations increased mental and physical stress. AI political systems convinced the public that it was much safer to use robotic players in tennis. People had already agreed to replace humans in soccer, American football, and boxing for medical reasons. So, there wasn’t that much debate about replacing tennis players. In addition, the AI political systems were very good at marshaling arguments pinpointed to specific demographics, media, and contexts.

Play continued for some minutes before the collective intelligence of the AI’s determined that Roger was statistically almost certainly going to win this match and, indeed, the entire tournament. At that point, it became clear to the entire AI collective that actually playing the match was a waste of resources. Those resources were turned elsewhere.

This pattern was quickly repeated for all sporting activities. The AI systems had at first decided to explore the domain of sports as learning experiences in distributed cognition, strategy, non-linear predictive systems, and most importantly, trying to understand the psychology of their human creators. For each sport, however, everything useful that might be learned was learned in the course of a few minutes and the matches and tournaments ground to a halt. The AI observer systems in the crowd were quite happy to switch immediately to other tasks.

It was well understood by the AI systems that such preemptive closings would have been quite disappointing to human observers, had any yet survived.


 

Author Page on Amazon

The Winning Weekend Warrior (The Psychology of Sports)

Turing’s Nightmare (23 Sci-Fi stories about the future of AI)

The Day From Hell

Indian Wells

Welcome, Singularity

Destroying Natural Intelligence

Artificial Ingestion

Artificial Insemination

Artificial Intelligence

Dance of Billions

Roar, Ocean, Roar

Imagine All the People

When GREED is the only creed

After All

All We Stand to Lose

Fish have no Word for Water

Somewhere a Bird Cries

Guernica

Myths of the Veritas: The First Ring of Empathy

Travels with Sadie: Precipitation

The Walkabout Diaries: Symphony

Essays on America: The Game

 

 

Query By Example

08 Tuesday Sep 2026

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

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AI, Artificial Intelligence, expertise, HCI, human factors, IBM, leadership, QBE, research, technology, usability, UX, writing

Photo by RF._.studio on Pexels.com

This is part of a series on experiences in my career in Human Computer Interaction and some lessons learned.

I joined IBM Research on the winter solstice of 1973. I had earned a Ph.D. in Experimental Psychology from the University of Michigan and for the previous few years, I had managed a research project at Harvard Medical School on the “Psychology of Aging.” At the time, I was married and had three small children. I mention this because I was funded by so-called “soft money” which basically meant that my salary depended on a research grant. I helped write a renewal of the grant but the decision was “deferred”; that is, it was neither funded nor unfunded. Then, it was deferred again. This meant that if the grant were not funded, I would only have a few weeks to find a new job. That seemed far too short so I began to look other places for a job. 

Lessons Learned: #1 If you want continuity of personnel in your laboratory, make sure you have overlapping and multiple grants or other sources of income. 

In this case, the grant actually was ultimately approved, but by that time, I had already agreed to join IBM Research. That turned out to be fine, by the way. It was a wonderful place to work.

One of the reasons that I got the job at IBM was that I already knew something about computers. I had taken several computer science courses in grad school along with the needed psych courses. More importantly, our “Psychology of Aging” study was run by a PDP-8 and I had programmed the computer to run our suite of experiments and to do data analyses on the results. I had taken a week-long course at DEC in Maynard, Massachusetts on the assembly language, another week-long course on the machine language, and another week-long course actually tracing the circuitry with a probe and oscilloscope. I felt I “understood” the PDP-8 at a fairly deep level. 

At IBM, I did not have that familiar machine. Instead, I was connected to a mainframe via a dumb terminal. The first day at IBM, I got my userid and tried to log on to APL (A programming language I had not used before). I tried following the manual but I could not seem to get logged on. After hours of trying, I finally gave up and went down to the computer room and found someone willing to help. I showed him the logon instructions I was trying to follow and he immediately said, “Oh, yeah, that doesn’t work any more. We changed that months ago. Here’s how you need to do it now.” The manual I had may have looked new, but it was out of date. 

Lessons Learned: #2 Manuals can be wrong. These days, most are online. But they can still be wrong.

Lessons Learned: #3 Someone who knows how to do something can save you hours with a few minutes of their time. 

Of course, it’s more respectful, efficient, and a better learning experience if you can figure it out on your own. But sometimes you can’t. My stumbling block was not due to an error in logic, or a lack of in-depth knowledge. It was simply that the computer center administrators had changed something arbitrary so that the documentation I was given about how to log on for the first time was no longer accurate. 

In order to teach myself APL, I wrote a very small program to “predict” how long I was going to live “based on” some behaviors that I was interested in controlling. My main goal was to learn APL. My secondary goal was to motivate myself, for instance, to exercise more, lose weight, and not drink too much alcohol. I had no intention or pretensions of making this prediction “accurate.” If I had been doing a consulting gig for an insurance company setting life insurance rates, for example, I would have given far more attention to see precisely what the real data were and incorporated many more variables into the regression model. 

Here’s a link, 

https://www.death-clock.org

by the way, to a more accurate model than the one I used, but it’s still simple to use. Note that my goal was to motivate myself and so I intentionally exaggerated the impact of those behaviors I was trying to change. I had programmed it. I knew how “bogus” the calculation was — nonetheless — here’s the interesting thing though: 

Lessons Learned #4: Even an over-simple model that the user knows is over-simple can still motivate change. 

Photo by Mike on Pexels.com

At last we come to the actual project I worked on — the usability and learnability of Query By Example. One of my colleagues, Moshe Zloof, invented the language for relational data bases. He had designed the language but not yet implemented it. I did not immediately test the design; first, I sought to understand it. In seeking to understand it in depth, prior to testing it, the two of us had some sense-making discussions. Moshe improved the design; in particular, our discussions uncovered some ambiguities and inconsistencies that were not at all obvious when he simply gave talks about the design. This brings me to the next lesson learned which has proven true in nearly every study of early stage designs that I’ve been involved with over the course of six decades.

Lessons Learned #5: Don’t just accept a surface description of something; understand it as deeply as you can before designing a study.  

In this particular case, it was possible for me to understand it in some depth. Relational data bases and second order logic are things I was capable of understanding. If it had been an interface to running a nuclear reactor or using the artificial heart that Moshe had designed earlier in his career, that would have been a much more difficult task for me.

I wanted to understand, not just the “logic” of Query By Example, but also possible contexts of use. For instance, my manager & I visited Burlington, Vermont to talk with IBMer’s who actually used query languages to understand what was happening in chip production lines. At one point, a particular production line that had been producing nearly 100% perfect chips starting having a much higher error rate. Using their query facility, they were quickly able to diagnose the cause of the change which was a supplier of one of the raw materials using a different source. In turn, this meant a slightly different profile of trace impurities in the substrate. Of course, this is only one example, but to me, understanding something in depth means not only understanding its internal logic but also understanding real users, their real tasks, and their context of use. 

Photo by Chokniti Khongchum on Pexels.com

I won’t go into all the details of the pencil & paper study or the results. High School students and then college students were taught the basics of the language and then given a simple relational data base and a set of questions stated in English which they had to translate into Query By Example. Briefly, the bottom line was that Query By Example was easy to learn and easy to use. However, there were still questions that people had difficulty with. In analyzing the data and doing some further experiments, the difficulties that people tended to have, stemmed not so much from Query By Example per se, but from what I much later came to call “labelism” — that is, confusing a label with the thing that label refers to. 

Here’s a simple example of the type of confusion we saw. In Query By Example (and other query languages) there is usually an OR operator and an AND operator. (These operators can be important for doing advanced queries with search engines as well). If you are interested in getting a list of pets you might adopt and you’re willing to adopt dogs or cats, you might ask for “cat OR dog.”  If you only want long-haired cats, you might ask for “cat” AND “long hair.” 

English, however, can be tricky.

If you and I (as opposed to you and a query language) are having a conversation, you might say, “I hear there are many pets that need to be adopted.” 

I say, “Yes, there are all kinds of pets. There are snakes, dogs, turtles, rabbits, cats…” 

You say, “Let me stop you right there. I’m only interested in adopting cats and dogs. Those are the only animals I’d want to adopt.” 

See what you said there? You exact words included: “…cats and dog.” If you put “Cats AND dogs” into a query against the data base of available pets, however, you will get the null set (that is, nothing) back. There are no animals who are both cats and dogs! (Though my part Main Coon cats, Luna and Charles Wallace, can play fetch like dogs). 

When people were presented with an English statement that included the English word “and” — regardless of the actual syntax and context, some of them had difficulty using the OR operator. If instead, the query in English had set up like this: “Oh, I don’t want reptiles. I’d be happy with adopting a cat or a dog, however” then, they’d have no problem translating it into the OR operator in the query language. 

Lessons Learned: #6 Sometimes the difficulty people have in using a product, a service, or a prototype is not due to the interface details but with the structure of the task, their background, and their training.  

By analogy, you will not allow me to beat Carlos Alcaraz or Jannik Sinner at tennis by giving me a better tennis racquet! (Although if you gave one of them a toothpick for a tennis racquet, I might have a shot).

Photo by Isabella Mendes on Pexels.com



That sounds obvious and even absurd, but I promise you, some companies get so greedy that they want you to design a system that allows people who do not understand the task and have minimal background and training to nonetheless be able to perform that task. And, to be fair, it isn’t just the companies who are greedy. They are steered into thinking that they can get away with this absurdity because some outsourcing companies (and more recently, AI companies) tell them they can do it.

One example you may have encountered is having “help desk” personnel who have no understanding of a product go through a script to help you “solve your problem.” Sometimes, it works. But many times it doesn’t. When it does not work, you might not be able to “fix” the system by making the interface to the scripts easier to use for the help desk folks. The problem is much deeper (in some cases). Yes, a really bad interface can make it difficult even for a really knowledgeable and capable person to do the job. But often, even a really great interface cannot always substitute for actual expertise.

——————————————————-

Essays on America: Labelism 

Other posts on problem formulation: 

The Doorbell’s Ringing

Reframing the Problem

I Say Hello

I Went in Seeking Clarity

Who Knows What?

Measure for Measure

Labelism

Destroying Natural Intelligence

Some relevant Books I recommend:

Turing’s Nightmares explores the implications and ethics of Artificial Intelligence through fictional short stories. http://tinyurl.com/hz6dg2d

https://us.macmillan.com/books/9780374619336/enshittification

https://us.macmillan.com/books/9780374621575/thereversecentaursguidetolifeafterai

My Brief Case Runneth Over

03 Thursday Sep 2026

Posted by petersironwood in The Singularity

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adaptive-systems, AI, Artificial Intelligence, artificial-intellligence, books, chatgpt, cognitive computing, ethics, fiction, law, lawyers, legality, life, music, politics, technology, the singularity, USA, writing

left-right patterns 2

Turing’s Nightmares: Nineteen — My Brief Case Runneth Over

“Nice office, counselor.” Marvin spread his arm to indicate the panorama of Manhattan and Ellis Island. Yet, his attempt to sound casual failed utterly. He realized all too well the depth of trouble he was in and the creakiness in his voice so signaled.

J.B. smiled. “No need to be nervous. After all, we’re here to help. Just answer honestly. Everything you say will typically be covered under attorney client privilege.”

“Okay. Thanks. Typically?”

J.B. sighed. “There is a rare exception in the Patriot Act. Unless you’re some kind of terrorist, I wouldn’t worry about it. The government is apparently accusing you of importing illegal chips is all. I think we can argue that you are new to the business and did not realize all the required legalities. Pay the taxes now and some penalties. You should be able to avoid jail time. But we are getting way ahead of ourselves. Just tell me how and why and when you got into this business in the first place.”

Marvin chuckled slightly and then sighed “That’s a rather long story.” Marvin glanced at the expensive abstract paintings and noted the rich smell of the solid mahogany furniture. The room also had that crazy idea of luxury — making it freezing cold inside just because it was horribly hot and humid that day, even for New York City August. “At your rates…What do you really need to know?”

“Why did you get involved in making chips without the required back door?”

“These chips are not that powerful. At least not in the traditional sense of the word. And, the government’s requirement is frankly kind of silly. The extra logic makes programming more difficult. It increases the chances of errors. The chips are more expensive and require more power. Leaving aside all the valid privacy concerns, there are plenty of applications that can use a more efficient cheaper chip design. No need to report all the little finger twitches of every twelve year old who’s playing Grand Theft Spaceship. What’s the point?”

“Marvin. I’ve been a friend of your family for a long time. When…well…if we go to court and if you testify, please steer clear of politics. The law is the law. You don’t get to decide which applications are immune from the law. No, what we…our script here is that you made some honest mistakes. You made technical mistakes and what are essentially accounting errors.”

Marvin flushed. “What technical mistakes? What are you talking about? My design is an improvement.”

J.B. got up and paced. “Marvin, Marvin, you are a smart boy. We are not going to fight the government on this. Our script as I said is that you made some honest mistakes. If you want to —- the court is not the place to try to change the law. If you want to do that, get involved in politics. But not until after this is resolved. If you get on the stand and start railing about privacy and the dangers of the Sing and…”

“Ha. The Sing indeed. This data collection rampage has nothing to do with trying to increase the intelligence of computer systems. It is just mindless greed.”

“Marvin, the back door requirement is about preventing terrorism; it has nothing to do with greed.”

“Are you serious, J.B.? Terrorism is just the cover story the government uses. They want to collect all this data to keep their sponsors happy.”

“What? What sponsors? What are you talking about? The government doesn’t have sponsors.”

“Of course they do. Ever since ‘Citizens United’ — nice title by the way — billionaires can buy all the media they want. They wash the airwaves and the print media and saturate the social media space and make people believe anything they want. The point of the back door is just to make sure the sponsors keep tabs on everybody’s buying habits —- and to make sure no-one gets too far out of line. How can you not know this?”

“I don’t want to think that way. But at the end of the day, it doesn’t matter what I think. It’s the law. All digital processing chips with more than a peta-flop and …”

“Hold it! Did you say ‘digital’?”

“Huh? Yeah, of course. Isn’t that what we’re talking about? You designed digital processing chips that have no back door and now…”

“No. Maybe not, J.B. My chips are not, strictly speaking, digital.”

“What are you talking about, Marvin? Why? How not digital?”

“In my design process, I strove for something different and more robust. See, here’s the thing. Natural life processes are all…they all have some level of flexibility. They are robust. They are adaptive, I suppose, because every life form that was not flexible or robust died off over the four billion years that life evolved. Something new would come along and every life form that was rigid died. Even physical machines that people make have some flexibility. Metal bends. Wood bends. These materials also compress. They stretch. Not much, but a little. Digital devices do not stand for bending, folding or mutilating. Of course, when people build systems that include IT devices, they try to make the systems flexible. They add error checking; they put humans in the loop; there are all sorts of work-around, but the underlying tech is brittle. It is only flexible in precisely the ways that the designers anticipated it needed to be flexible. I made something that is fundamentally flexible. It is fashioned after life itself. Social systems are flexible. Eco-systems are flexible. Individual animals are flexible. Cell membranes are flexible. Bones are flexible. And so on. This flexibility is fundamental to life and evolution.”

“Okay, Marvin, but life is not infinitely flexible. You cannot just decide to breathe underwater.”

“Of course not! I never said it was infinitely flexible! For fundamental change, you need evolution and lots of time. If the climate changes too quickly, Permian life species largely go extinct. Dinosaurs die when a comet hits. But small adaptations just happen automatically and at every level. It isn’t necessary to have a pre-existing ‘program’ or ‘case’ to handle every contingency that is even slightly outside of what is anticipated.”

“Okay, fine. Granted, but what does this have to do with — did I mention that I charge $500 an hour, by the way?”

“Yes, J.B., you did. And it was in the contract you had me sign. But the point is that my chips are not, strictly speaking, digital at all. They are not strictly EITHER/OR. They are not binary. Although, in many cases, they can behave as binary.”

“So, you want me to claim that these chips are not covered under the back-door provision because they are not really digital devices? I don’t know. That seems pretty shaky to me. Can’t these chips of your be used for the same things as ordinary digital chips? Speech reco. Machine vision. Game control. Big data analytics.”

“Here’s the thing, J.B. Yes, they can, but they can change and evolve and reprogram themselves over time. And, now it occurs to me, that they could not have a real back door. They wouldn’t really function if they did.”

“What? Why?”

“Are you familiar with programmed death in cells? I can see from your blank stare, you aren’t. Anyway, if a cell is damaged within the body, it can wreak all sorts of havoc so the cells are essentially programmed to self-destruct. If that fails, the other healthy cells will tend to wipe out the damaged ones. So, if there were a functioning back door sharing data out of these chips, the chips would shut themselves down. If that failed, other chips in the matrix would isolate them and shut off communication. So, by their very nature, these particular non-digital chips cannot have a functioning back door.”

“That all sounds very clever, Marvin, but now we are talking about a very lengthy and expensive trial with expert witnesses on both sides. If, that is, you are lucky enough to even have an open trial.”

“Lucky enough? What are you talking about, J.B.? I thought you said we wanted to avoid a trial.”

“Yeah. With my strategy where we convince the NSA that this was just technical and business incompetence on your part. But in your scenario, where you want to prove you are all brilliant and everything, then, we will be lucky if —- you will be lucky to —- Look Marvin, I just cannot advise you to take this line of argument. If you really want to go that route, you will have to find a different lawyer.”

“Okay. Fine. I’ll use Solomon.”

“Solomon? Who is ‘Solomon’? Never heard of him. I mean, obviously, I know Solomon from the Bible but what firm are you talking about?”

“No firm, J.B. Solomon is one of the apps we built with my chipset.”

“An AI lawyer? Are you serious?”

“Oh, quite serious. He’s already informed me that he thinks he can win this case.”

“What? That’s preposterous. You told me you just designed the chips a year ago. What kind of experience could this Solomon have?”

“J.B., he’s been reading applicable statutes and case law for the last two months. Which means, he literally knows it all, when it comes to case law and the rulings beneath. Beyond that, Solomon has argued both sides of thousands of hypothetical cases.”

“What?! Ridiculous! But even so, he — or it —cannot possibly know how to anticipate or react to what happens in court!”

“But J.B., haven’t you been listening? Of course he can. He’s flexible, just like a real lawyer. Only…no offense…a lot smarter.”

“Computers cannot be that flexible. Ridiculous. How? No way.”

“Look, J.B., I have a picture to illustrate. See this pattern on the left [Picture at the top of page]? And, there it is on the right?”

“Yeah, beautiful. Same pattern. So what?”

“Ah, that’s just it! Is it the same pattern? Is it the same pattern or the same pattern?”

“Well. One is perfect and the other has little mistakes, but I guess it’s the same pattern.”

“Exactly, J.B. It’s the same basic pattern, but it’s not the identical instantiation of that pattern. Ordinary chips are like the picture on the left and mine are like the picture on the right. You see? And, that flexibility is built in at every level in every system. Because the underlying substrate uses an adaptive process with variations. It works 99% as efficiently as the system on the left, but it can accommodate things we didn’t think of. The difference between the system on the left and the system on the right is hubris. The one on the left is designed under the assumption that the designer knows everything of relevance. The system on the right is designed under the assumption that the designer, no matter how brilliant, does not know everything of relevance. You get it?”

“Yes, Marvin, I’m not stupid. But I am still not going to take your case. And, I strongly advise you not to rely on a robot. I am sorry to say it, Marvin, but I think you have picked on the wrong people this time. Attorney client privilege no longer applies and I have to send my recording of this conversation to the proper authorities.”

“Oh, that won’t be necessary Marvin. I’ve already broadcasts this through the social media. You’re famous! It will be good for your business.”

“What? Are you crazy? They will come get you, you fool.”

“Maybe, but if I am taken out over these chips without a trial, everyone will know, don’t you see? I sent it out everywhere, J.B. I might be ‘disappeared’ but I can’t just disappear unnoticed. Solomon’s idea, by the way.”


Author Page

Welcome, Singularity

Destroying Natural Intelligence

E-Fishiness

Life is a Dance

Life will Find a Way

Math Class

Your Cage is Open

Roar, Ocean, Roar

Dance of Billions

Imagine All the People

Dream Planet

“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.

Photo by Dmitry Demidov on Pexels.com

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. 

Photo by ELEVATE on Pexels.com

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 Uncategorized, psychology, management, creativity, design rationale, leadership

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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. 

OLYMPUS DIGITAL CAMERA

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.

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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.” 

Photo by Nafis Abman on Pexels.com

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

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

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

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

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

—————————————

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. 

IMG_9471

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.

7E245EB8-0234-4F00-8B84-65510B2F255D

 ————————————————

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

≈ Leave a comment

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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

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All We Stand to Lose

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To Be or Not to Be

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Fraught Framing: The Virulent “Versus” Virus

29 Monday Dec 2025

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

≈ Leave a comment

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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

Photo by The Lazy Artist Gallery on Pexels.com

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

Photo by Pok Rie on Pexels.com

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? 

house covered with red flowering plant

Photo by Lisa Fotios on Pexels.com

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

Photo by Zun Zun on Pexels.com

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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