Line between science and fiction

Narrated by AI

In recent posts, I contrasted two different kinds of work environments.

One is a team of peers where there is appreciation for different strengths within the team but everyone is expected to help each other to keep the project going. In a team, if someone is struggling on a task someone else can easily and quickly do himself, the preferred option is for the latter to take the time to help the former complete that task. The team attitude is that no one is left behind.

I contrasted the team approach with the lead and helper model. In this model, there is one person who is responsible for the entire project and all of the most skillful portions of the project. He has helpers who he can direct to perform some tasks that would save him time even though he has to spend some time with supervision. In such a scenario, the lead will have helpers with different levels of experience. The lead will assign tasks appropriately. The key distinction of this model is that there will be times when the lead is the only one actually working. There are tasks that do not need help, and there is no shame in not sharing the work. The helpers are still useful in being around in case something comes up that needs extra help, but they are generally idle.

In the team environment, it is imperative that no one is left out. If any team member has something to do, then everyone on the team has something to do. If they have no assigned tasks, then they will interject themselves into the work of the one who is busy.

I recall an example where I had to create a complete process that massaged data to fit in a database so that it can be used in a report. I was doing the entire process myself and this allowed me to make changes, such as to changing the data ingest algorithm to put the data into a better table structure so that the report will be more useful. In the team environment, I was obliged the divide the tasks to different people where each can focus on a part. Making this work, I needed to spend more time defining what was needed so they have something to work on. Even with that, I had to accept the fact that I had to accept their work even after it became apparent that a different approach would work better. If I were working on my own, I would not have hesitated to make that kind of change. In a team environment, that change would be assigned to a backlog list to tackle in a later iteration because the focus is on each team member to complete their tasks as originally assigned.

Most of the projects I have worked on were projects I felt confident I could do alone. I often got to do it myself because I volunteered and no one else did. This confidence was always a bit deceitful. I was confident I could satisfy the customer with my own approach. This is not the same thing as delivering exactly what the customer requested. I did things my own way with confidence that the customer would appreciate the result. Often the customer may have been disappointed in the appearance or the speed, but he would still appreciate having something that would allow him to advance his objectives.

Very early in my career I embraced advice given to me by men whose careers started before I was born. One advice was to give the customer what he wants instead of what he asks for. Another advice is to do a job multiple times. The first time is to do what the customer requested. The second time is to do what the customer wants. There may be a third time to do what I think is really needed. I learned that people tend to describe their requirements in terms of an implementation. If I gave them their implementation without regard to their actual requirement, they would be disappointed. More significantly, when I gave them a different implementation that addressed their requirement, they would refuse to admit that they originally asked for something very different. This was what they wanted.

I do not think this kind of intuition is possible in a team environment. The entire team would hear the customer’s request. If anyone would propose an alternative implementation, there would be multiple competing ideas to argue over. The path of least resistance is to give the customer exactly what he asked for.

In my last post, I described my recent purchase of a new bathroom scale that asserts that it can measure percentage body fat or fat-free body weight. When I tried it out, it did exactly what I wanted. I step on the scale and then the smart phone would show an extensive breakdown of the different compositions of my body. It shows each composite with a numeric value that can be plotted over time. I want this information.

The problem is that this is obviously incorrect information. To the extent it may be accurate, it is only measuring the composition of my body below my beltline. Most of my fat is above my belt line. As a result, it is showing the contradiction that I have a borderline overweight BMI yet a percentage of body fat that is bordering on athletic levels. I am no athlete.

I notice this is a growing trend even in my work but also in the world at large. Products are giving people the implementations they ask for. People want something that is grounded on science. The products often do trace back to something scientific. In my weight-scale example, there are scientific studies about bioelectric impedance for body composition and the product gives the desired number values for the different compositions of the body. The problem is that these are probably worse than inaccurate. It is like a person drinking sea water to quench his thirst.

Throughout my career, I noticed the disappearance of confidence intervals or error bars. This is partly because my career diverted from the more rigorous research areas. But I do recall even consumer reports having some indication of uncertainty. That uncertainty is all but disappeared in modern products. I think this is also a user request. People are requesting products that have confidence in their readings. The solution is to remove any indications of uncertainty from the displays.

People also request that the devices give them reasonable results. I recall many examples where the user interfaces replaced the actual measurements with something more reasonable. In one case, the tool would not show results more than 100% of a capacity. In another case, the tool would not show a negative percentage of benefit. The tools would constrain the displayed value to fit between 0 and 100. I can imagine scenarios where that could happen in the weight scale: there are possibly some bodies with negative body fat, or having more than 100% of their weight in fat. If those calculations would occur, I doubt those results would ever show up to the user.

If these products were produced by individual product leaders with helpers, I expect that the leader would insist on showing the data as it actually is. He would alert his users to the uncertainty especially when that uncertainty renders the value useless. This is clearly not what the customer is asking for. A reputable leader would not mislead his customers in order to avoid disappointing them. Teams tend to be behave oppositely. The objective is to satisfy the customers.

This is worrisome when it comes to consumer health devices. People are eager to measure their own health metrics. This includes even advanced techniques such as electrocardiograms or brain waves. Devices exist to fill these needs, and their advertisements include testimonials claiming that their measurements are clinically useful while also including the disclaimer that they are not a substitute for professional healthcare measurements.

When I was younger, I vaguely recall there being arguments against consumer healthcare measurement devices. The concern would be that the consumer will use this to avoid seeing a doctor, or they will see the doctors more frequently when they see something change. The devices may be accurate in what they are showing, but they may give a false sense of security about good health, or a false sense of pending health problems. These concerns have largely vanished.

The technologies have improved. In addition the devices are connected to the Internet and the measurements are stored in the cloud. The opportunity exists for the doctor to view the patients measurement history for himself. I think there also is an emergence for a new type of healthcare where the doctors primarily do telemedicine and rely heavily on the consumer’s equipment with the expectation that these devices are accurate enough. Other doctors may disagree, but the consumers may choose to avoid those doctors.

I focus on the health care topic but the problems are much broader. The market is delivering what the people are demanding. People want to be informed about science, but they want that science to be confident, with no uncertainties. They also want science to be consistent with what they are told. If there is a new virus, then there must be a new vaccine, and that vaccine must solve the virus problem. That is what they got.

I watched a couple videos on YouTube that illustrated the broader problem.

One video criticizes the accuracies of certain claims of a popular channel for science content where the original video discussed Damascus Steel for making swords. The criticism points out that the medieval blacksmiths had the ability to craft high quality steel, and that there were low quality swords made from Damascus Steel. The criticism also concerned the details of how the steel forms its structure. All of the criticisms cited appropriate sources. The most powerful criticism, in my mind, is that the more popular channel with a specific reputation of science uncritically reported information that is arguably mythological or exaggerations for marketing at the time. My take away is that the channel devoted to science topics has learned to cater to its customer’s demands to present entertainment in a scientific presentation, but also a science with confidence enough to say that ancient art of steel making included the mastery of carbon nanotubes to strengthen steel. They failed to at least question the certainty of that claim. They satisfied their customers desire for certainty.

The other video broadly criticizes the History Channel, a cable television channel that started with the intent of being devoted to serious historical inquiry. This makes the point more explicitly. The channel may have started off with good historical material, but it was criticized for relying too heavily on stock documentaries instead of more recent studies. At the same time, the channel suffered from a low market because there are not that many people who want this kind of historical analysis to be part of their regular entertainment schedule. The content is useful for archival purposes to be viewed when a particular topic become interesting, but this is not a profitable model for a cable channel. The mentioned video emphasizes a particularly clumsy episode for the channel as something that never should have aired at all. Although that particular show was removed, the channel itself changed its focus to reality television and historical dramatizations that had an increasing level of fictionalization due to the need to produce a regular series. During this transformation, they maintained the illusion that this is a historical discipline rather than fictional story telling. They were rewarded with a large audience that wanted their entertainment to come from an academic discipline but also wanted the information to be presented with certainty. Even if the show is clearly a dramatization, the audience wants to believe this is what actually happened. Most of the audience is never aware of the serious historian’s objections about the accuracy of the depictions.

In these two examples, the channels deliver products that answers population’s request for science or academic presentation of material that is entertaining and yet certain. The channels evolved to show that entertaining a population requires removing the uncertainty. Even purely fictional mysteries have a certainty in their final resolution. Real science and academic disciplines have uncertainties across a multiple dimensions and often with large error bars. The attraction of science is to study in an attempt to narrow those uncertainties. This is rarely entertaining to watch in real time.

I like my useless body-composition weight scale. It gives me numbers and plots with labels describing things I want to know. The numbers come from computations based on scientific studies. The numbers have a certainty to them even though I have no idea where this health story will end up. I am entertained watching the show of my own making.

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